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+12.[[:space:]]Optimizers,[[:space:]]Learning[[:space:]]Rates[[:space:]]&[[:space:]]Callbacks[[:space:]]with[[:space:]]Fruit[[:space:]]Classification/4.[[:space:]]Build[[:space:]]a[[:space:]]Fruit[[:space:]]Classifier.mp4 filter=lfs diff=lfs merge=lfs -text
+13.[[:space:]]Batch[[:space:]]Normalization[[:space:]]&[[:space:]]LeNet,[[:space:]]AlexNet[[:space:]]Clothing[[:space:]]Classifier/3.[[:space:]]Build[[:space:]]AlexNet[[:space:]]and[[:space:]]test[[:space:]]on[[:space:]]CIFAR10.mp4 filter=lfs diff=lfs merge=lfs -text
+14.[[:space:]]Advanced[[:space:]]Image[[:space:]]Classiers[[:space:]]-[[:space:]]ImageNet[[:space:]]in[[:space:]]Keras[[:space:]](VGG1619,[[:space:]]InceptionV3,[[:space:]]ResNet50)/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+14.[[:space:]]Advanced[[:space:]]Image[[:space:]]Classiers[[:space:]]-[[:space:]]ImageNet[[:space:]]in[[:space:]]Keras[[:space:]](VGG1619,[[:space:]]InceptionV3,[[:space:]]ResNet50)/4.[[:space:]]Understanding[[:space:]]ResNet50.mp4 filter=lfs diff=lfs merge=lfs -text
+14.[[:space:]]Advanced[[:space:]]Image[[:space:]]Classiers[[:space:]]-[[:space:]]ImageNet[[:space:]]in[[:space:]]Keras[[:space:]](VGG1619,[[:space:]]InceptionV3,[[:space:]]ResNet50)/3.[[:space:]]Understanding[[:space:]]VGG16[[:space:]]and[[:space:]]VGG19.mp4 filter=lfs diff=lfs merge=lfs -text
+15.[[:space:]]Transfer[[:space:]]Learning[[:space:]]Build[[:space:]]a[[:space:]]Flower[[:space:]]&[[:space:]]Monkey[[:space:]]Breed[[:space:]]Classifier/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+14.[[:space:]]Advanced[[:space:]]Image[[:space:]]Classiers[[:space:]]-[[:space:]]ImageNet[[:space:]]in[[:space:]]Keras[[:space:]](VGG1619,[[:space:]]InceptionV3,[[:space:]]ResNet50)/5.[[:space:]]Understanding[[:space:]]InceptionV3.mp4 filter=lfs diff=lfs merge=lfs -text
+14.[[:space:]]Advanced[[:space:]]Image[[:space:]]Classiers[[:space:]]-[[:space:]]ImageNet[[:space:]]in[[:space:]]Keras[[:space:]](VGG1619,[[:space:]]InceptionV3,[[:space:]]ResNet50)/2.[[:space:]]ImageNet[[:space:]]-[[:space:]]Experimenting[[:space:]]with[[:space:]]pre-trained[[:space:]]Models[[:space:]]in[[:space:]]Keras[[:space:]](VGG16,[[:space:]]ResNet50,[[:space:]]Mobi.mp4 filter=lfs diff=lfs merge=lfs -text
+16.[[:space:]]Design[[:space:]]Your[[:space:]]Own[[:space:]]CNN[[:space:]]-[[:space:]]LittleVGG/LittleVGG.png filter=lfs diff=lfs merge=lfs -text
+15.[[:space:]]Transfer[[:space:]]Learning[[:space:]]Build[[:space:]]a[[:space:]]Flower[[:space:]]&[[:space:]]Monkey[[:space:]]Breed[[:space:]]Classifier/2.[[:space:]]What[[:space:]]is[[:space:]]Transfer[[:space:]]Learning[[:space:]]and[[:space:]][[:space:]]Fine[[:space:]]Tuning.mp4 filter=lfs diff=lfs merge=lfs -text
+16.[[:space:]]Design[[:space:]]Your[[:space:]]Own[[:space:]]CNN[[:space:]]-[[:space:]]LittleVGG[[:space:]]A[[:space:]]Simpsons[[:space:]]Classifier/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+16.[[:space:]]Design[[:space:]]Your[[:space:]]Own[[:space:]]CNN[[:space:]]-[[:space:]]LittleVGG[[:space:]]A[[:space:]]Simpsons[[:space:]]Classifier/2.[[:space:]]Introducing[[:space:]]LittleVGG.mp4 filter=lfs diff=lfs merge=lfs -text
+17.[[:space:]]Advanced[[:space:]]Activation[[:space:]]Functions[[:space:]]&[[:space:]]Initializations/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+15.[[:space:]]Transfer[[:space:]]Learning[[:space:]]Build[[:space:]]a[[:space:]]Flower[[:space:]]&[[:space:]]Monkey[[:space:]]Breed[[:space:]]Classifier/4.[[:space:]]Build[[:space:]]a[[:space:]]Flower[[:space:]]Classifier[[:space:]]with[[:space:]]VGG16[[:space:]]using[[:space:]]Transfer[[:space:]]Learning.mp4 filter=lfs diff=lfs merge=lfs -text
+15.[[:space:]]Transfer[[:space:]]Learning[[:space:]]Build[[:space:]]a[[:space:]]Flower[[:space:]]&[[:space:]]Monkey[[:space:]]Breed[[:space:]]Classifier/3.[[:space:]]Build[[:space:]]a[[:space:]]Monkey[[:space:]]Breed[[:space:]]Classifier[[:space:]]with[[:space:]]MobileNet[[:space:]]using[[:space:]]Transfer[[:space:]]Learning.mp4 filter=lfs diff=lfs merge=lfs -text
+17.[[:space:]]Advanced[[:space:]]Activation[[:space:]]Functions[[:space:]]&[[:space:]]Initializations/3.[[:space:]]Advanced[[:space:]]Initializations.mp4 filter=lfs diff=lfs merge=lfs -text
+17.[[:space:]]Advanced[[:space:]]Activation[[:space:]]Functions[[:space:]]&[[:space:]]Initializations/2.[[:space:]]Dying[[:space:]]ReLU[[:space:]]Problem[[:space:]]and[[:space:]]Introduction[[:space:]]to[[:space:]]Leaky[[:space:]]ReLU,[[:space:]]ELU[[:space:]]and[[:space:]]PReLUs.mp4 filter=lfs diff=lfs merge=lfs -text
+18.[[:space:]]Facial[[:space:]]Applications[[:space:]]-[[:space:]]Emotion,[[:space:]]Age[[:space:]]&[[:space:]]Gender[[:space:]]Recognition/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+16.[[:space:]]Design[[:space:]]Your[[:space:]]Own[[:space:]]CNN[[:space:]]-[[:space:]]LittleVGG[[:space:]]A[[:space:]]Simpsons[[:space:]]Classifier/3.[[:space:]]Simpsons[[:space:]]Character[[:space:]]Recognition[[:space:]]using[[:space:]]LittleVGG.mp4 filter=lfs diff=lfs merge=lfs -text
+19.[[:space:]]Medical[[:space:]]Imaging[[:space:]]-[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]U-Net/1.[[:space:]]Chapter[[:space:]]Overview[[:space:]]on[[:space:]]Image[[:space:]]Segmentation[[:space:]]&[[:space:]]Medical[[:space:]]Imaging[[:space:]]in[[:space:]]U-Net.mp4 filter=lfs diff=lfs merge=lfs -text
+12.[[:space:]]Optimizers,[[:space:]]Learning[[:space:]]Rates[[:space:]]&[[:space:]]Callbacks[[:space:]]with[[:space:]]Fruit[[:space:]]Classification/4.2[[:space:]]fruits-360.tar.gz filter=lfs diff=lfs merge=lfs -text
+19.[[:space:]]Medical[[:space:]]Imaging[[:space:]]-[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]U-Net/2.[[:space:]]What[[:space:]]is[[:space:]]Segmentation[[:space:]]And[[:space:]]Applications[[:space:]]in[[:space:]]Medical[[:space:]]Imaging.mp4 filter=lfs diff=lfs merge=lfs -text
+19.[[:space:]]Medical[[:space:]]Imaging[[:space:]]-[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]U-Net/3.[[:space:]]U-Net[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]CNNs.mp4 filter=lfs diff=lfs merge=lfs -text
+19.[[:space:]]Medical[[:space:]]Imaging[[:space:]]-[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]U-Net/4.[[:space:]]The[[:space:]]Intersection[[:space:]]over[[:space:]]Union[[:space:]](IoU)[[:space:]]Metric.mp4 filter=lfs diff=lfs merge=lfs -text
+20.[[:space:]]Principles[[:space:]]of[[:space:]]Object[[:space:]]Detection/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+20.[[:space:]]Principles[[:space:]]of[[:space:]]Object[[:space:]]Detection/4.[[:space:]]Single[[:space:]]Shot[[:space:]]Detectors[[:space:]](SSDs).mp4 filter=lfs diff=lfs merge=lfs -text
+20.[[:space:]]Principles[[:space:]]of[[:space:]]Object[[:space:]]Detection/2.[[:space:]]Object[[:space:]]Detection[[:space:]]Introduction[[:space:]]-[[:space:]]Sliding[[:space:]]Windows[[:space:]]with[[:space:]]HOGs.mp4 filter=lfs diff=lfs merge=lfs -text
+18.[[:space:]]Facial[[:space:]]Applications[[:space:]]-[[:space:]]Emotion,[[:space:]]Age[[:space:]]&[[:space:]]Gender[[:space:]]Recognition/2.[[:space:]]Build[[:space:]]an[[:space:]]Emotion,[[:space:]]Facial[[:space:]]Expression[[:space:]]Detector.mp4 filter=lfs diff=lfs merge=lfs -text
+21.[[:space:]]TensorFlow[[:space:]]Object[[:space:]]Detection[[:space:]]API/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+20.[[:space:]]Principles[[:space:]]of[[:space:]]Object[[:space:]]Detection/5.[[:space:]]YOLO[[:space:]]to[[:space:]]YOLOv3.mp4 filter=lfs diff=lfs merge=lfs -text
+18.[[:space:]]Facial[[:space:]]Applications[[:space:]]-[[:space:]]Emotion,[[:space:]]Age[[:space:]]&[[:space:]]Gender[[:space:]]Recognition/3.[[:space:]]Build[[:space:]]EmotionAgeGender[[:space:]]Recognition[[:space:]]in[[:space:]]our[[:space:]]Deep[[:space:]]Surveillance[[:space:]]Monitor.mp4 filter=lfs diff=lfs merge=lfs -text
+19.[[:space:]]Medical[[:space:]]Imaging[[:space:]]-[[:space:]]Image[[:space:]]Segmentation[[:space:]]with[[:space:]]U-Net/5.[[:space:]]Finding[[:space:]]the[[:space:]]Nuclei[[:space:]]in[[:space:]]Divergent[[:space:]]Images.mp4 filter=lfs diff=lfs merge=lfs -text
+21.[[:space:]]TensorFlow[[:space:]]Object[[:space:]]Detection[[:space:]]API/2.[[:space:]]TFOD[[:space:]]API[[:space:]]Install[[:space:]]and[[:space:]]Setup.mp4 filter=lfs diff=lfs merge=lfs -text
+22.[[:space:]]Object[[:space:]]Detection[[:space:]]with[[:space:]]YOLO[[:space:]]&[[:space:]]Darkflow[[:space:]]Build[[:space:]]a[[:space:]]London[[:space:]]Underground[[:space:]]Sign[[:space:]]Detector/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+20.[[:space:]]Principles[[:space:]]of[[:space:]]Object[[:space:]]Detection/3.[[:space:]]R-CNN,[[:space:]]Fast[[:space:]]R-CNN,[[:space:]]Faster[[:space:]]R-CNN[[:space:]]and[[:space:]]Mask[[:space:]]R-CNN.mp4 filter=lfs diff=lfs merge=lfs -text
+21.[[:space:]]TensorFlow[[:space:]]Object[[:space:]]Detection[[:space:]]API/3.[[:space:]]Experiment[[:space:]]with[[:space:]]a[[:space:]]ResNet[[:space:]]SSD[[:space:]]on[[:space:]]images,[[:space:]]webcam[[:space:]]and[[:space:]]videos.mp4 filter=lfs diff=lfs merge=lfs -text
+21.[[:space:]]TensorFlow[[:space:]]Object[[:space:]]Detection[[:space:]]API/4.[[:space:]]How[[:space:]]to[[:space:]]Train[[:space:]]a[[:space:]]TFOD[[:space:]]Model.mp4 filter=lfs diff=lfs merge=lfs -text
+22.[[:space:]]Object[[:space:]]Detection[[:space:]]with[[:space:]]YOLO[[:space:]]&[[:space:]]Darkflow[[:space:]]Build[[:space:]]a[[:space:]]London[[:space:]]Underground[[:space:]]Sign[[:space:]]Detector/2.[[:space:]]Setting[[:space:]]up[[:space:]]and[[:space:]]install[[:space:]]Yolo[[:space:]]DarkNet[[:space:]]and[[:space:]]DarkFlow.mp4 filter=lfs diff=lfs merge=lfs -text
+23.[[:space:]]DeepDream[[:space:]]&[[:space:]]Neural[[:space:]]Style[[:space:]]Transfers[[:space:]]Make[[:space:]]AI[[:space:]]Generated[[:space:]]Art/1.[[:space:]]Chapter[[:space:]]Introduction.mp4 filter=lfs diff=lfs merge=lfs -text
+24.[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Networks[[:space:]](GANs)[[:space:]]Simulate[[:space:]]Aging[[:space:]]Faces/1.[[:space:]]Generative[[:space:]]Adverserial[[:space:]]Neural[[:space:]]Networks[[:space:]]Chapter[[:space:]]Overview.mp4 filter=lfs diff=lfs merge=lfs -text
+22.[[:space:]]Object[[:space:]]Detection[[:space:]]with[[:space:]]YOLO[[:space:]]&[[:space:]]Darkflow[[:space:]]Build[[:space:]]a[[:space:]]London[[:space:]]Underground[[:space:]]Sign[[:space:]]Detector/3.[[:space:]]Experiment[[:space:]]with[[:space:]]YOLO[[:space:]]on[[:space:]]still[[:space:]]images,[[:space:]]webcam[[:space:]]and[[:space:]]videos.mp4 filter=lfs diff=lfs merge=lfs -text
+23.[[:space:]]DeepDream[[:space:]]&[[:space:]]Neural[[:space:]]Style[[:space:]]Transfers[[:space:]]Make[[:space:]]AI[[:space:]]Generated[[:space:]]Art/2.[[:space:]]DeepDream[[:space:]]–[[:space:]]How[[:space:]]AI[[:space:]]Generated[[:space:]]Art[[:space:]]All[[:space:]]Started.mp4 filter=lfs diff=lfs merge=lfs -text
+24.[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Networks[[:space:]](GANs)[[:space:]]Simulate[[:space:]]Aging[[:space:]]Faces/3.[[:space:]]Mathematics[[:space:]]of[[:space:]]GANs.mp4 filter=lfs diff=lfs merge=lfs -text
+22.[[:space:]]Object[[:space:]]Detection[[:space:]]with[[:space:]]YOLO[[:space:]]&[[:space:]]Darkflow[[:space:]]Build[[:space:]]a[[:space:]]London[[:space:]]Underground[[:space:]]Sign[[:space:]]Detector/4.[[:space:]]Build[[:space:]]your[[:space:]]own[[:space:]]YOLO[[:space:]]Object[[:space:]]Detector[[:space:]]-[[:space:]]Detecting[[:space:]]London[[:space:]]Underground[[:space:]]Signs.mp4 filter=lfs diff=lfs merge=lfs -text
+24.[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Networks[[:space:]](GANs)[[:space:]]Simulate[[:space:]]Aging[[:space:]]Faces/2.[[:space:]]Introduction[[:space:]]To[[:space:]]GANs.mp4 filter=lfs diff=lfs merge=lfs -text
+23.[[:space:]]DeepDream[[:space:]]&[[:space:]]Neural[[:space:]]Style[[:space:]]Transfers[[:space:]]Make[[:space:]]AI[[:space:]]Generated[[:space:]]Art/3.[[:space:]]Neural[[:space:]]Style[[:space:]]Transfer.mp4 filter=lfs diff=lfs merge=lfs -text
+24.[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Networks[[:space:]](GANs)[[:space:]]Simulate[[:space:]]Aging[[:space:]]Faces/5.[[:space:]]Face[[:space:]]Aging[[:space:]]GAN.mp4 filter=lfs diff=lfs merge=lfs -text
+25.[[:space:]]Face[[:space:]]Recognition/testfriends.mp4 filter=lfs diff=lfs merge=lfs -text
+24.[[:space:]]Generative[[:space:]]Adversarial[[:space:]]Networks[[:space:]](GANs)[[:space:]]Simulate[[:space:]]Aging[[:space:]]Faces/4.[[:space:]]Implementing[[:space:]]GANs[[:space:]]in[[:space:]]Keras.mp4 filter=lfs diff=lfs merge=lfs -text
+26.[[:space:]]The[[:space:]]Computer[[:space:]]Vision[[:space:]]World/4.[[:space:]]Popular[[:space:]]Computer[[:space:]]Vision[[:space:]]Conferences[[:space:]]&[[:space:]]Finding[[:space:]]Datasets.mp4 filter=lfs diff=lfs merge=lfs -text
+26.[[:space:]]The[[:space:]]Computer[[:space:]]Vision[[:space:]]World/Screenshot[[:space:]]from[[:space:]]2024-08-10[[:space:]]20-58-43.png filter=lfs diff=lfs merge=lfs -text
+26.[[:space:]]The[[:space:]]Computer[[:space:]]Vision[[:space:]]World/Screenshot[[:space:]]from[[:space:]]2024-08-10[[:space:]]20-59-06.png filter=lfs diff=lfs merge=lfs -text
+26.[[:space:]]The[[:space:]]Computer[[:space:]]Vision[[:space:]]World/5.[[:space:]]Building[[:space:]]a[[:space:]]Deep[[:space:]]Learning[[:space:]]Machine[[:space:]]vs.[[:space:]]Cloud[[:space:]]GPUs.mp4 filter=lfs diff=lfs merge=lfs -text
+28.[[:space:]]BONUS[[:space:]]-[[:space:]]Use[[:space:]]Cloud[[:space:]]GPUs[[:space:]]on[[:space:]]PaperSpace/2.1[[:space:]]AlexNet[[:space:]]CIFAR10.zip filter=lfs diff=lfs merge=lfs -text
+4.[[:space:]]Handwriting[[:space:]]Recognition/2.[[:space:]]Experiment[[:space:]]with[[:space:]]a[[:space:]]Handwriting[[:space:]]Classifier.mp4 filter=lfs diff=lfs merge=lfs -text
+4.[[:space:]]Handwriting[[:space:]]Recognition/3.[[:space:]]Experiment[[:space:]]with[[:space:]]a[[:space:]]Image[[:space:]]Classifier.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/10.[[:space:]]Transformations,[[:space:]]Affine[[:space:]]And[[:space:]]Non-Affine[[:space:]]-[[:space:]]The[[:space:]]Many[[:space:]]Ways[[:space:]]We[[:space:]]Can[[:space:]]Change[[:space:]]Images.mp4 filter=lfs diff=lfs merge=lfs -text
+4.[[:space:]]Handwriting[[:space:]]Recognition/4.[[:space:]]OpenCV[[:space:]]Demo[[:space:]]–[[:space:]]Live[[:space:]]Sketch[[:space:]]with[[:space:]]Webcam.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/11.[[:space:]]Image[[:space:]]Translations[[:space:]]-[[:space:]]Moving[[:space:]]Images[[:space:]]Up,[[:space:]]Down.[[:space:]]Left[[:space:]]And[[:space:]]Right.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/12.[[:space:]]Rotations[[:space:]]-[[:space:]]How[[:space:]]To[[:space:]]Spin[[:space:]]Your[[:space:]]Image[[:space:]]Around[[:space:]]And[[:space:]]Do[[:space:]]Horizontal[[:space:]]Flipping.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/14.[[:space:]]Image[[:space:]]Pyramids[[:space:]]-[[:space:]]Another[[:space:]]Way[[:space:]]of[[:space:]]Re-Sizing.mp4 filter=lfs diff=lfs merge=lfs -text
+25.[[:space:]]Face[[:space:]]Recognition[[:space:]]with[[:space:]]VGGFace/.goutputstream-XYXP52 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/13.[[:space:]]Scaling,[[:space:]]Re-sizing[[:space:]]and[[:space:]]Interpolations[[:space:]]-[[:space:]]Understand[[:space:]]How[[:space:]]Re-Sizing[[:space:]]Affects[[:space:]]Quality.mp4 filter=lfs diff=lfs merge=lfs -text
+25.[[:space:]]Face[[:space:]]Recognition[[:space:]]with[[:space:]]VGGFace/2.1[[:space:]]vgg_face_weights.h5.tar.gz filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/15.[[:space:]]Cropping[[:space:]]-[[:space:]]Cut[[:space:]]Out[[:space:]]The[[:space:]]Image[[:space:]]The[[:space:]]Regions[[:space:]]You[[:space:]]Want[[:space:]]or[[:space:]]Don't[[:space:]]Want.mp4 filter=lfs diff=lfs merge=lfs -text
+25.[[:space:]]Face[[:space:]]Recognition[[:space:]]with[[:space:]]VGGFace/2.2[[:space:]]vgg_face_weights.h5.tar.gz filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/16.[[:space:]]Arithmetic[[:space:]]Operations[[:space:]]-[[:space:]]Brightening[[:space:]]and[[:space:]]Darkening[[:space:]]Images.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/17.[[:space:]]Bitwise[[:space:]]Operations[[:space:]]-[[:space:]]How[[:space:]]Image[[:space:]]Masking[[:space:]]Works.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/19.[[:space:]]Sharpening[[:space:]]-[[:space:]]Reverse[[:space:]]Your[[:space:]]Images[[:space:]]Blurs.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/2.[[:space:]]What[[:space:]]are[[:space:]]Images.mp4 filter=lfs diff=lfs merge=lfs -text
+5.[[:space:]]OpenCV[[:space:]]Tutorial[[:space:]]-[[:space:]]Learn[[:space:]]Classic[[:space:]]Computer[[:space:]]Vision[[:space:]]&[[:space:]]Face[[:space:]]Detection[[:space:]](OPTIONAL)/18.[[:space:]]Blurring[[:space:]]-[[:space:]]The[[:space:]]Many[[:space:]]Ways[[:space:]]We[[:space:]]Can[[:space:]]Blur[[:space:]]Images[[:space:]]&[[:space:]]Why[[:space:]]It's[[:space:]]Important.mp4 filter=lfs diff=lfs merge=lfs -text
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10. Data Augmentation/10.1 - 10.3 - Data Augmentation - Cats vs Dogs.ipynb

@@ -1,802 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cats vs Dogs\n",
-    "\n",
-    "### Loading our images \n",
-    "- Images are labeled catxxx.jpg and dogxxx.jpg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3002 images loaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get filenames in list\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "\n",
-    "mypath = \"./datasets/catsvsdogs/images/\"\n",
-    "\n",
-    "file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "\n",
-    "print(str(len(file_names)) + ' images loaded')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Splitting our loaded images into a training and test/validation dataset\n",
-    "- We also need to store their labels (i.e. y_train and y_test)\n",
-    "- We re-size our images here to maintain a constant dimension of 150 x 150\n",
-    "- We're going to use 1000 images of dogs and 1000 images of cats as our training data\n",
-    "- For our test/validation dataset we're going to use 500 of each class\n",
-    "- Dogs will be labels 1 and cats 0\n",
-    "- We store our new images in the following directories\n",
-    " - /datasets/catsvsdogs/train/dogs\n",
-    " - /datasets/catsvsdogs/train/cats\n",
-    " - /datasets/catsvsdogs/validation/dogs\n",
-    " - /datasets/catsvsdogs/validation/cats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training and Test Data Extraction Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "import sys\n",
-    "import os\n",
-    "import shutil\n",
-    "\n",
-    "# Extract 1000 for our training data and 500 for our validation set\n",
-    "# Takes about ~20 seconds to run\n",
-    "dog_count = 0\n",
-    "cat_count = 0\n",
-    "training_size = 1000\n",
-    "test_size = 500\n",
-    "training_images = []\n",
-    "training_labels = []\n",
-    "test_images = []\n",
-    "test_labels = []\n",
-    "size = 150\n",
-    "dog_dir_train = \"./datasets/catsvsdogs/train/dogs/\"\n",
-    "cat_dir_train = \"./datasets/catsvsdogs/train/cats/\"\n",
-    "dog_dir_val = \"./datasets/catsvsdogs/validation/dogs/\"\n",
-    "cat_dir_val = \"./datasets/catsvsdogs/validation/cats/\"\n",
-    "\n",
-    "def make_dir(directory):\n",
-    "        if os.path.exists(directory):\n",
-    "            shutil.rmtree(directory)\n",
-    "        os.makedirs(directory)\n",
-    "\n",
-    "make_dir(dog_dir_train)\n",
-    "make_dir(cat_dir_train)\n",
-    "make_dir(dog_dir_val)\n",
-    "make_dir(cat_dir_val)\n",
-    "\n",
-    "def getZeros(number):\n",
-    "    if(number > 10 and number < 100):\n",
-    "        return \"0\"\n",
-    "    if(number < 10):\n",
-    "        return \"00\"\n",
-    "    else:\n",
-    "        return \"\"\n",
-    "\n",
-    "for i, file in enumerate(file_names):\n",
-    "    \n",
-    "    if file_names[i][0] == \"d\":\n",
-    "        dog_count += 1\n",
-    "        image = cv2.imread(mypath+file)\n",
-    "        image = cv2.resize(image, (size, size), interpolation = cv2.INTER_AREA)\n",
-    "        if dog_count <= training_size:\n",
-    "            training_images.append(image)\n",
-    "            training_labels.append(1)\n",
-    "            zeros = getZeros(dog_count)\n",
-    "            cv2.imwrite(dog_dir_train + \"dog\" + str(zeros) + str(dog_count) + \".jpg\", image)\n",
-    "        if dog_count > training_size and dog_count <= training_size+test_size:\n",
-    "            test_images.append(image)\n",
-    "            test_labels.append(1)\n",
-    "            zeros = getZeros(dog_count-1000)\n",
-    "            cv2.imwrite(dog_dir_val + \"dog\" + str(zeros) + str(dog_count-1000) + \".jpg\", image)\n",
-    "            \n",
-    "    if file_names[i][0] == \"c\":\n",
-    "        cat_count += 1\n",
-    "        image = cv2.imread(mypath+file)\n",
-    "        image = cv2.resize(image, (size, size), interpolation = cv2.INTER_AREA)\n",
-    "        if cat_count <= training_size:\n",
-    "            training_images.append(image)\n",
-    "            training_labels.append(0)\n",
-    "            zeros = getZeros(cat_count)\n",
-    "            cv2.imwrite(cat_dir_train + \"cat\" + str(zeros) + str(cat_count) + \".jpg\", image)\n",
-    "        if cat_count > training_size and cat_count <= training_size+test_size:\n",
-    "            test_images.append(image)\n",
-    "            test_labels.append(0)\n",
-    "            zeros = getZeros(cat_count-1000)\n",
-    "            cv2.imwrite(cat_dir_val + \"cat\" + str(zeros) + str(cat_count-1000) + \".jpg\", image)\n",
-    "\n",
-    "    if dog_count == training_size+test_size and cat_count == training_size+test_size:\n",
-    "        break\n",
-    "\n",
-    "print(\"Training and Test Data Extraction Complete\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's save our dataset's to NPZ files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Using numpy's savez function to store our loaded data as NPZ files\n",
-    "np.savez('cats_vs_dogs_training_data.npz', np.array(training_images))\n",
-    "np.savez('cats_vs_dogs_training_labels.npz', np.array(training_labels))\n",
-    "np.savez('cats_vs_dogs_test_data.npz', np.array(test_images))\n",
-    "np.savez('cats_vs_dogs_test_labels.npz', np.array(test_labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loader Function\n",
-    "import numpy as np\n",
-    "\n",
-    "def load_data_training_and_test(datasetname):\n",
-    "    \n",
-    "    npzfile = np.load(datasetname + \"_training_data.npz\")\n",
-    "    train = npzfile['arr_0']\n",
-    "    \n",
-    "    npzfile = np.load(datasetname + \"_training_labels.npz\")\n",
-    "    train_labels = npzfile['arr_0']\n",
-    "    \n",
-    "    npzfile = np.load(datasetname + \"_test_data.npz\")\n",
-    "    test = npzfile['arr_0']\n",
-    "    \n",
-    "    npzfile = np.load(datasetname + \"_test_labels.npz\")\n",
-    "    test_labels = npzfile['arr_0']\n",
-    "\n",
-    "    return (train, train_labels), (test, test_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's view some of our loaded images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 - Cat\n",
-      "2 - Cat\n",
-      "3 - Dog\n",
-      "4 - Cat\n",
-      "5 - Cat\n",
-      "6 - Dog\n",
-      "7 - Cat\n",
-      "8 - Cat\n",
-      "9 - Dog\n",
-      "10 - Dog\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(1,11):\n",
-    "    random = np.random.randint(0, len(training_images))\n",
-    "    cv2.imshow(\"image_\"+str(i), training_images[random])\n",
-    "    if training_labels[random] == 0:\n",
-    "        print(str(i) + \" - Cat\")\n",
-    "    else:\n",
-    "        print(str(i)+ \" - Dog\")\n",
-    "    cv2.waitKey(0)\n",
-    "    \n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's get our data ready in the format expected by Keras\n",
-    "- We also stick the previous naming convention "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2000, 150, 150, 3)\n",
-      "(2000, 1)\n",
-      "(1000, 150, 150, 3)\n",
-      "(1000, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "(x_train, y_train), (x_test, y_test) = load_data_training_and_test(\"cats_vs_dogs\")\n",
-    "\n",
-    "# Reshaping our label data from (2000,) to (2000,1) and test data from (1000,) to (1000,1)\n",
-    "y_train = y_train.reshape(y_train.shape[0], 1)\n",
-    "y_test = y_test.reshape(y_test.shape[0], 1)\n",
-    "\n",
-    "# Change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "print(x_train.shape)\n",
-    "print(y_train.shape)\n",
-    "print(x_test.shape)\n",
-    "print(y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's create our model using a simple CNN that similar to what we used for CIFAR10\n",
-    "- Except now we use a Sigmoid instead of Softmax\n",
-    "- **Sigmoids are used when we're doing binary (i.e. two class) classification\n",
-    "- Note the binary_crossentropy loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_3 (Conv2D)            (None, 148, 148, 32)      896       \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 148, 148, 32)      0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 74, 74, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 72, 72, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 72, 72, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 36, 36, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 34, 34, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 34, 34, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 17, 17, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 18496)             0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 64)                1183808   \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 1)                 65        \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 1)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,212,513\n",
-      "Trainable params: 1,212,513\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "import os\n",
-    "\n",
-    "batch_size = 16\n",
-    "epochs = 10\n",
-    "\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "input_shape = (img_rows, img_cols, 3)\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(64, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(1))\n",
-    "model.add(Activation('sigmoid'))\n",
-    "\n",
-    "model.compile(loss='binary_crossentropy',\n",
-    "              optimizer='rmsprop',\n",
-    "              metrics=['accuracy'])\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training our model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 2000 samples, validate on 1000 samples\n",
-      "2000/2000 [==============================] - 42s 21ms/sample - loss: 0.7029 - accuracy: 0.5335 - val_loss: 0.6820 - val_accuracy: 0.5440\n",
-      "1000/1000 [==============================] - 5s 5ms/sample - loss: 0.6820 - accuracy: 0.5440\n",
-      "Test loss: 0.6819891605377197\n",
-      "Test accuracy: 0.544\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          validation_data=(x_test, y_test),\n",
-    "          shuffle=True)\n",
-    "\n",
-    "model.save(\"cats_vs_dogs_V1.h5\")\n",
-    "\n",
-    "# Evaluate the performance of our trained model\n",
-    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
-    "print('Test loss:', scores[0])\n",
-    "print('Test accuracy:', scores[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('cats_vs_dogs_V1.h5')\n",
-    "\n",
-    "def draw_test(name, pred, input_im):\n",
-    "    BLACK = [0,0,0]\n",
-    "    if pred == \"[0]\":\n",
-    "        pred = \"cat\"\n",
-    "    if pred == \"[1]\":\n",
-    "        pred = \"dog\"\n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    #expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (252, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "\n",
-    "    imageL = cv2.resize(input_im, None, fx=2, fy=2, interpolation = cv2.INTER_CUBIC)\n",
-    "    cv2.imshow(\"Test Image\", imageL)\n",
-    "\n",
-    "    input_im = input_im.reshape(1,150,150,3) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(classifier.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "\n",
-    "    draw_test(\"Prediction\", res, imageL) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis\n",
-    "- Our results aren't bad, but they could be better"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Now let's train our Cats vs Dogs Classifier using Data Augmentation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2000 images belonging to 2 classes.\n",
-      "Found 1000 images belonging to 2 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras import optimizers\n",
-    "import scipy\n",
-    "import pylab as pl\n",
-    "import matplotlib.cm as cm\n",
-    "%matplotlib inline\n",
-    "\n",
-    "input_shape = (150, 150, 3)\n",
-    "img_width = 150\n",
-    "img_height = 150\n",
-    "\n",
-    "nb_train_samples = 2000\n",
-    "nb_validation_samples = 1000\n",
-    "batch_size = 16\n",
-    "epochs = 5\n",
-    "\n",
-    "train_data_dir = './datasets/catsvsdogs/train'\n",
-    "validation_data_dir = './datasets/catsvsdogs/validation'\n",
-    "\n",
-    "# Creating our data generator for our test data\n",
-    "validation_datagen = ImageDataGenerator(\n",
-    "    # used to rescale the pixel values from [0, 255] to [0, 1] interval\n",
-    "    rescale = 1./255)\n",
-    "\n",
-    "# Creating our data generator for our training data\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale = 1./255,              # normalize pixel values to [0,1]\n",
-    "      rotation_range = 30,           # randomly applies rotations\n",
-    "      width_shift_range = 0.3,       # randomly applies width shifting\n",
-    "      height_shift_range = 0.3,      # randomly applies height shifting\n",
-    "      horizontal_flip = True,        # randonly flips the image\n",
-    "      fill_mode = 'nearest')         # uses the fill mode nearest to fill gaps created by the above\n",
-    "\n",
-    "# Specify criteria about our training data, such as the directory, image size, batch size and type \n",
-    "# automagically retrieve images and their classes for train and validation sets\n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size = (img_width, img_height),\n",
-    "        batch_size = batch_size,\n",
-    "        class_mode = 'binary',\n",
-    "        shuffle = True)\n",
-    "\n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size = (img_width, img_height),\n",
-    "        batch_size = batch_size,\n",
-    "        class_mode = 'binary',\n",
-    "        shuffle = False)    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create our model, just like we did previously"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_2\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_6 (Conv2D)            (None, 148, 148, 32)      896       \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 148, 148, 32)      0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 74, 74, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_7 (Conv2D)            (None, 72, 72, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 72, 72, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_7 (MaxPooling2 (None, 36, 36, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 34, 34, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 34, 34, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_8 (MaxPooling2 (None, 17, 17, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 18496)             0         \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 64)                1183808   \n",
-      "_________________________________________________________________\n",
-      "activation_13 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 1)                 65        \n",
-      "_________________________________________________________________\n",
-      "activation_14 (Activation)   (None, 1)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,212,513\n",
-      "Trainable params: 1,212,513\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating out model\n",
-    "model = Sequential()\n",
-    "model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(64, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(1))\n",
-    "model.add(Activation('sigmoid'))\n",
-    "\n",
-    "print(model.summary())\n",
-    "\n",
-    "model.compile(loss='binary_crossentropy',\n",
-    "              optimizer='rmsprop',\n",
-    "              metrics=['accuracy'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From <ipython-input-13-03f251166bbb>:6: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use Model.fit, which supports generators.\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 125 steps, validate for 62 steps\n",
-      "Epoch 1/5\n",
-      "125/125 [==============================] - 64s 511ms/step - loss: 0.7178 - accuracy: 0.5190 - val_loss: 0.6823 - val_accuracy: 0.5887\n",
-      "Epoch 2/5\n",
-      "125/125 [==============================] - 45s 363ms/step - loss: 0.6883 - accuracy: 0.5555 - val_loss: 0.6647 - val_accuracy: 0.5746\n",
-      "Epoch 3/5\n",
-      "125/125 [==============================] - 46s 370ms/step - loss: 0.6841 - accuracy: 0.5795 - val_loss: 0.6531 - val_accuracy: 0.6411\n",
-      "Epoch 4/5\n",
-      "125/125 [==============================] - 47s 373ms/step - loss: 0.6642 - accuracy: 0.6160 - val_loss: 0.7110 - val_accuracy: 0.5151\n",
-      "Epoch 5/5\n",
-      "125/125 [==============================] - 50s 399ms/step - loss: 0.6643 - accuracy: 0.5815 - val_loss: 0.6231 - val_accuracy: 0.6905\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting our Loss and Accuracy Graphs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our loss charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "loss_values = history_dict['loss']\n",
-    "val_loss_values = history_dict['val_loss']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_loss_values, label='Validation/Test Loss')\n",
-    "line2 = plt.plot(epochs, loss_values, label='Training Loss')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Loss')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our accuracy charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "acc_values = history_dict['accuracy']\n",
-    "val_acc_values = history_dict['val_accuracy']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_acc_values, label='Validation/Test Accuracy')\n",
-    "line2 = plt.plot(epochs, acc_values, label='Training Accuracy')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:750dcb623ec0693ac4fc91ea386e409df22841d5b51ef3df09053d361d112d72
+size 84376

10. Data Augmentation/10.4 - Data Augmentation Demos.ipynb

@@ -1,420 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Augmentation\n",
-    "\n",
-    "### Let's look at our untouched dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot images\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from matplotlib import pyplot\n",
-    "\n",
-    "# load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# create a grid of 3x3 images\n",
-    "for i in range(0, 9):\n",
-    "    pyplot.subplot(330 + 1 + i)\n",
-    "    pyplot.imshow(x_train[i], cmap=pyplot.get_cmap('gray'))\n",
-    "    \n",
-    "# show the plot\n",
-    "pyplot.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Random Rotations\n",
-    "- As per Keras documentation random is 50%"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from matplotlib import pyplot\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape our data to be in the forma [samples, width, height, color_depth]\n",
-    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
-    "\n",
-    "# Change datatype to float32\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Create our image generator\n",
-    "# Define random rotation parameter to be 60 degrees\n",
-    "train_datagen = ImageDataGenerator(rotation_range=60)\n",
-    "\n",
-    "# fit parameters from data\n",
-    "train_datagen.fit(x_train)\n",
-    "\n",
-    "# configure batch size and retrieve one batch of images\n",
-    "for x_batch, y_batch in train_datagen.flow(x_train, y_train, batch_size=9):\n",
-    "    # create a grid of 3x3 images\n",
-    "    for i in range(0, 9):\n",
-    "        pyplot.subplot(330 + 1 + i)\n",
-    "        pyplot.imshow(x_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))# show the plot\n",
-    "    pyplot.show()\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Randon Shearing and zooming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from matplotlib import pyplot\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape our data to be in the forma [samples, width, height, color_depth]\n",
-    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
-    "\n",
-    "# Change datatype to float32\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Create our image generator\n",
-    "# Define shearing and zooming parameters to be 0.5 each\n",
-    "train_datagen = ImageDataGenerator(shear_range=0.5,\n",
-    "                                   zoom_range=0.5)\n",
-    "\n",
-    "# fit parameters from data\n",
-    "train_datagen.fit(x_train)\n",
-    "\n",
-    "# configure batch size and retrieve one batch of images\n",
-    "for x_batch, y_batch in train_datagen.flow(x_train, y_train, batch_size=9):\n",
-    "    # create a grid of 3x3 images\n",
-    "    for i in range(0, 9):\n",
-    "        pyplot.subplot(330 + 1 + i)\n",
-    "        pyplot.imshow(x_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))# show the plot\n",
-    "    pyplot.show()\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Horizontal and Vertical Flips"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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sVN45N8rlZik5usj50nhixdVlcOacdqzN2Cqu5anmxrkE6Bb5fjtgaamTnXONt8anFBMrrpIpim1CqrlxTgd6mVkPM+sIDAEmJVMsqSPFtXkptgmpuI7TObfWzM4i1+jTAbjVOTc3sZJJXSiuzUuxTU6qsyOZWXoXa0wzm7HuSHFVXJtUybhqkg8RkZh04xQRiSntVS5rZpNNNgFgxIjcKLJjjjkmOLbzzjsDsGrVKgAmTpwYHLvxxhsB+M9//pNKOUUk+5RxiojE1DSNQxMmTABg4MCB/lrBMf9v9Pui/+YPP/wQgJNPPhmAe++9t1ZFBDUitKpLly7B9siRIwEYMmRIwTlLl4bdDu+77z4ALrvsMgBWr16dRDEqobg2JzUOiYgkRTdOEZGYMv+ofsABBwBw++23A/DnP/8ZgN/85jfrnLvlllsCcPHFFwf7zjvvPADmzZsHwK677pp0EaP0SFfE17/+dQD+9re/Bft69uxZznUBWLBgAVD4WD979uxqihSX4tqc9KguIpKUzGecjzzyCBA26owaFW8d+s8++wwIG4zWX7+mPbSUmUSst17u7/add94JwHHHHRcca/n/8sknnwTC7BJg6NChQNgV7e677w6OnXDCCQB8/vnnlRQtLsW1iL32yn0k06dPD/a99NJLAEydOhWAl1/OLWH08MPhHECvv144m9vGG28cbPfo0QOAV199FYCPPvqomiK2RRmniEhSMt8BfosttgDgqaeeKvtnfF0nFHZbknR96UtfAmDw4MFtnuvje/nllwf7rrnmGgDmz58PwLHHHhsc++1vfwukXtcpEWvXrgXg008/Dfb5NoSWbQkff/xxsP3WW28VHOvYsWOwvdVWuaWDhg8fDsAf//jHBEtcPmWcIiIx6cYpIhJTm4/qZnYrcCSwwjnXJ7+vC/A3oDvwGjDYOfd27YpZ2kknnQSUN9b8Zz/7GQA//OEPg32+ESI6fr09aIS4+lFbvqJ/xx13DI4ddVRuGWw/OqhPnz7r/Lz/uWL8CLL2+KjeCLGF8LN//PHHg337778/EFar+O6E/fr1C87ZZpttAOjWLTdZ/YoVK4Jjzz33HAALFy6sUanLU07GORoY0GLfCOBx51wv4PH895Ito1Fcm9VoFNuaajPjdM49lV/oPWog0C+/PQaYClyUYLnKVirTPPHEE4Pt6667DggbhaJdXXzj0Pe+9z0g/AsYp7Epixohru+99x4ATzzxBAC9evUKjq1Zk1sae9CgQQBccsklAPz0pz8Nzrn22muB4g187bnRrxFiG3XrrbcG2wceeCAQxsfHNco3Bn3hC18AChuXatz9qGyVtqpv7ZxbBuCcW2ZmW5U6UcuNZori2rzKiq3iWp6ad0dyzo0CRkE6Q7i++tWvAmGWCWG3F59pRjNOn7H6OTt93VqzZ5zVSjKud911FwCnnx7+vvo6ygsuuAAI6zpLlKWsfdK2Wvy+Rgcm+OHOf/3rXwHo2ze3rPuSJUuCcz755JOC10ZUaav6cjPbFiD/uqKN8yUbFNfmpdgmqNKMcxIwDLg6/3p/YiWqkB925+fljHZy99nHo48+CoQt8RDWtbzwwgtAOOmHHwYIMGvWrFoVu9HUJa6+DssPfwU466yzgHBI7WOPPZZGUZpZQ/zO+p4tfojlRRflqlnPPvvsehSnYm1mnGY2DngO2NnMlpjZD8h9+P3NbAHQP/+9ZIji2rwU29orp1V9aIlDBydcFkmR4tq8FNvay/xYdc+PWfcNQdHxrr/+9a8BuOmmm0r+/MqVKwHYfvvtAdhll12CY+3oUb0unn32WaBwnlQ/1vyWW24B4LDDDgO0qF7W+c7wPo4//vGPgcIGpCw0zGrIpYhITE2Tcfo5/PxwrUr5xiI/NAxg7NixVb2nlCfahcw34Pmhlr6R6Pzzzw/Oaa2Lkp/3URqL72J02mmnATBlyhSgcGYrZZwiIk2oaTLOavk1h/bYY486l0QgXIfonnvuAeDoo48Gwu5mEM4g72d598M0IcxkpDE988wzAIwfPx4I6zohfMJ7/vnn0y9YmZRxiojEpBuniEhMmXxU96OEIHyEO/fccwHYe++9K3pP3x2pPc+q04jOOOMMAGbOnAnAlVdeGRzzj+h+ZJhf+Avggw8+SKuIUoWbb74ZgOOPPz7Yd/XVub75AwbkZsZrlBmRopRxiojElMmM08+ABDB69GigsDtDJXyHd82q01j8QAbfIX7u3LnBMb8ktOdn2gF45ZVXgDBDve2224BwATFpDP5JItoFyc/Z2b17d6AxBz0o4xQRiSmTGecdd9wRbK9atQpYN/uIyw/Z9HWcvruENAZfn/nOO++UPGfOnDnBdu/evYFw+Vg/w390vak333wz8XJKPL7+8sILLwz2Pf300wD84he/AArrPxuFMk4RkZgymXH62doBRo0aVdV7+fpS/+rr1PxfPWl8ixYtAsL1ogCGDBkCwJ///GcADj30UKCwU/UxxxwDhHOxSv1MmzYt2PYTgfh2C9/yDo3zJFjOfJzdzGyKmc0zs7lmdm5+fxcz+4eZLci/dq59cSUpimtzUlzTUc6j+lrgAufcLsA3gOFm1hstN5p1imtzUlxTUM5ExssAvzreu2Y2D+hKHZcb9WOUoXCJjEpcddVVAGy88cYA/OUvfwFg8eLFVb1vo2vEuMbl/x/07NkTKBz84Ofx/Pvf/17w6huNIFwepX///kA4w1aWNUNcL730UiCcocwvAw3wzW9+EwgbC+slVh1nfq3m3YFpaLnRpqG4NifFtXbKvnGaWSdgAnCec25NuUMTa7HcaHRonV/O1y8tO3HiRCAcQhnls9Pbb7892OcbDfx7+tni24tGimtcLYdcDh8+PDjmG4F8lyM/fO+JJ54IzvGZ6imnnALA5ZdfXtsCpyjLcZ0xYwYADzzwABA29EG4EoCfn7VeyuqOZGYbkAvCWOfcxPxuLTeacYprc1Jca8/aGmJouT9VY4DVzrnzIvuvAVY55642sxFAF+fchaXeJ/8zifwFiw659H95dthhByDMPqJ/YVvui/6bWy4ZXCxTTdBM59xetbxAuRoxruXYYIMNgm0/h+MNN9ywznmTJ08GwiGXvsvSpEmTgnP22muvgnOqyDgV1xro1KkTAMuXLw/2+fWpfL10jZWMazmP6vsBJwEvmtns/L5LyC0vOj6/9OhioLrB4pI2xbU5Ka4pKKdV/RmgVAWJlhvNKMW1OSmu6cjkyKHobCm+C4p/3PKzHH3rW98KzvGP5n40UHRce2tLBkvj+fTTT4PtkSNHArDjjjsCcMIJJwTHfCOCfy1WTeNHiTVDN6Rm9N577wGFi/j5xfr8Ejf1WrpbY9VFRGJqs3Eo0YvVubK5ATRMI0KSGiWuBx10ULDtu5X5JxKfcUbnfRwxIjd4JjpOukKKaw1tt912wfb06dOBcBG/s88+u5aXLhlXZZwiIjEp40yXMpPmpLimZMyYMUA4R2ePHj0AWLJkSS0up4xTRCQpunGKSGaMGzeOcePG8dFHH/HRRx9x4IEHBmsUpUk3ThGRmHTjFBGJKZMd4EWkffLzqm666aZ1LYcyThGRmNLOOFcC7+dfs2YLqi/3DkkUpAEprs1JcS0h1X6cAGY2I4t93rJa7rRk9fPJarnTktXPp9bl1qO6iEhMunGKiMRUjxvnqDpcMwlZLXdasvr5ZLXcacnq51PTcqdexykiknV6VBcRiUk3ThGRmFK7cZrZADObb2YL86vsNSQz62ZmU8xsnpnNNbNz8/u7mNk/zGxB/rVzvcvaKLIQW8U1PsW1leumUcdpZh2AV4D+wBJgOjDUOfdyzS8eU37N6W2dc7PMbFNgJnAUcAq5JVf98qqdnXMX1bGoDSErsVVc41FcW5dWxrkPsNA5t8g59wlwFzAwpWvH4pxb5pybld9+F5gHdCVX3jH508aQC45kJLaKa2yKayuqunHGSOW7Am9Evl+S39fQzKw7sDswDdjaObcMcsECtqpfyWor5iNa5mLbXuMKzf07m2ZcK75x5lP5PwCHA72BoWbWu9TpRfY1dD8oM+sETADOc86tqXd50hIzrpCx2LbXuEJz/86mHlfnXEVfwL7A5Mj3FwMXt3YuuQ++PX+9VennndZXnLhGzq/351rvr4aPa4W/s/X+XOv9VTKu1cyOVCyV79vyJDM7HTgd2K2KazWL1+tdgDLEjatkI65QRmwV1wIl41pNHWdZqbxzbpTLzVJydBXXkvTEiqvL4Mw57VibsVVcy1PNjXMJ0C3y/XbA0lInO+ceruJakp5YcZVMUWwTUs2NczrQy8x6mFlHYAgwKZliSR0prs1LsU1IxXWczrm1ZnYWuUafDsCtzrm5iZWsTFttletlsHRp4R/O888/P9geOXJkqmXKskaJqyRPsU1OVUtn5B+/9QjeZBTX5qXYJqNpV7ncay/VbYtIbWh2JBGRmJo245Rs++UvfwnAgQceCEC/fv0AmDp1anDOk08+WfRnRGpNGaeISEypLp1hZolfzGcijz32WMH+AQMGBNstj9XRzGbsWJxUXKdMmRJs+7hW6qCDDgIKM9QaUlybU8m4KuMUEYlJN04RkZgy3zh0+OGHA2BWOAz3rbfeqkdxpAK+Uafax/OoYo1J0ni+/OUvAzB8+PBgX7duuVGhG220EQCHHHJIcMz/nt92220F7/PBBx8E23/5y18AeP/994Ha3AuUcYqIxJT5jHPjjTcG8HMIMmlSbujtnDlz6lYmqT/fjUkaS8eOHQH41a9+BcAPfvADADbffPOyft5nnOecc07Jc0aMyE1s/+abbwLwxz/+MTj2m9/8JmaJi1PGKSISUyYzzi233DLYPuOMMwD48MMPAfjd735XlzJJ+nz9ZbG60STrS6U6O++8c7A9fvx4APr06VPy/GeeeQaAV155BYDZs2cHx1oOejjxxBMB+NKXvhTs87H/yle+AsAVV1wRHFt//dwt78orr4z3j2hBGaeISEy6cYqIxNTmo7qZ3QocCaxwzvXJ7+sC/A3oDrwGDHbOvV27YhYaNmzYOvsWLlwIwPPPP7/Ose7duwPQq1evdY6tWrUKgFmzZiVYwsbXiHFtjX8s9yOCotIc/ZYFjRbbCy+8MNgu9Yi+du3aYNt3T4uOJCvFNwRFbbjhhgAcfXRutZ477rgjOOa7PY0dOxaARYsWtXmNYsrJOEcDA1rsGwE87pzrBTye/16yZTSKa7MajWJbU21mnM65p/ILvUcNBPrlt8cAU4GLEixXq0466aSSx7bddlsAzjzzzGDfKaecUnAs6r333gPCvzy+m4OvoG5WjRhXSUajxNZnlz7zi/Kd071NNtkk2B41ahQA3/72t4GwkahcH330EQDjxo0D4LDDDguO+XvHDjvsAFSecVbaqr61c24ZgHNumZltVepELTeaKYpr8yortopreWreHck5NwoYBcnNtuLXGcq/JxAOuXrxxRcB6Ny5c3DOeuvlaiR8Pdlrr70WHDv55JMB+NrXvgbAI488AhTWpc2YMSOJYjeVWsS1Nb6LSbTeq1h9p1QnybieffbZAGy22WbBvo8//hgI2yn872a0HtJ3I3r22WeBsAsTwE033QTA/Pnz27x+//79Aejbt28bZ8ZXaav6cjPbFiD/uiK5IkkdKa7NS7FNUKUZ5yRgGHB1/vX+xErUij322AOATp06Bft8i2rLvyrROsrLLrsMKD7hw6mnngrADTfcAIR1nE888URwjs9sZs6cWVX5M6AucW052zuU7sAe3a/W9FhSj+0222yzzr4NNtgACFvRH3jgAaBwko7LL78cgD333BOAH/3oR8Ex3+Hd15HeeeedQDgABuC0004DoEuXLkDY6R3C1XAXL15c2T8qr82M08zGAc8BO5vZEjP7AbkPv7+ZLQD657+XDFFcm5diW3vltKoPLXHo4ITLIilSXJuXYlt7mRqr3rt3byCcEQnWfVzz3Yt8ug7ldWfwy2v4bkzRaxxwwAFAu3hUr6toY0+1j+H+cU/qx1d3HXnkkcG+Tz75BIDly5cXnOsbZQGmTZsGwPe+9z2gcMDLfvvtB4Tdl37yk5+UvP4777wDwL333hvs83NZvPrqq3H+KevQkEsRkZgytTMcdrcAAAatSURBVFjbxIkTARg4cGCwz5ffdzE67rjjgMqzw+eeew6AvffeO9jnuzjtvvvuFb1nhBb1isl3P4o725HPOFNaMlhxLcI3zkyfPj3Y54c/33PPPUD4+1qugw8uv7bBZ7UvvfRSrGtEaLE2EZGkZKqO0/+1KuaFF14Aqq+HnDBhAlCYcUr9+HrPuJmn74LmpZR5SsTq1asBuPnmm4N91157LQD77LMPEHZZ+t///lfWez7++ONJFrFiyjhFRGLSjVNEJKZMPar7MePRRpoVK3Ijx/yiT9XyS3FElxtuufSwpM839sRtJNIje/35RdMAPvvsMwC23357AHbbbTeg/Ef1RqGMU0Qkpkx1R/Id4KMVxH7hNj/2dcCA3Pytxcalt+b003MzafmlRP1fRoATTjgBgLvvvruCUhdQt5UKJfX/NNoxPsHsU3Et07x58wDYaaedgDAbPeKII4Jzqug+lDR1RxIRSUqm6jhffvllAA455JBgn88+3347t3xKOd2RNtpoo2D7ootyk2D7tUi86PyACWSaUqHW6jT9U4VfMrZlfWYx0RmYJH1+qV4/q1HXrl2BwuV6i80Y32iUcYqIxNRmHaeZdQNuB7YBPgdGOeduqmTVvFrUmfhM0c8M7Ver9PNrAgwaNAgIJ+6Izue57777tiwjAD179gz2RWeMr1LD1IU1ely9YjO/t+Q7yUfrtcvpMJ/gsEzFtfzyAeHv7XXXXQcU1mH7J8oGWPerqjrOtcAFzrldgG8Aw82sN1o1L+sU1+akuKagzRunc26Zc25WfvtdYB7QldyqeWPyp40BjqpVISV5imtzUlzTEas7Un7J0aeAPsBi59zmkWNvO+c6l/hRf07iqb9fCMp3ju/Ro0ex6wKtd2n573//C4TLhz7//POJljOvYR7pohoxrl45j+pesS5o5XSYT2CAg+JaIT9X5ne/+91gn1+y1y+dsWbNmloXo5SScS27Vd3MOgETgPOcc2vK/c+m5UYbm+LanBTX2ior4zSzDYAHgcnOuevz++YD/fJrNG8LTHXO7dzG+9TsL9iOO+4IhB3Zo/xMR34m94cffjg4NnnyZCDsfuRnja6RhspMshBXzzfglNPlqFw+Q01gmWHFtUJbbLEFULjc7+ab5xLjhx56CIDjjz8+OOZXeEhJ5Y1DlvtTdQswzwchz6+aBymuiCjJUFybk+KajnK6I+0PPA28SK57A8AlwDRgPLA9sBg41jm3uo33au/ruTZMZpLVuEbrOuNO+NFSgpO3KK5VinYJu/TSSwuO1ahrYDkqr+N0zj0DlPofplXzMkpxbU6Kazo0ckhEJKZMzY7UBBrmkS5J9YprnK5KfpRQtMtS3Bm0WqG4Vim6HPcbb7wBhI1El1xySXDst7/9bVpFAs2OJCKSHGWc6VJm0pwU1wQdfvjhADz44IPrHOvQoUOaRVHGKSKSlEzNxykizW/OnDlAODv8ypUr61mcopRxiojEpIxTRBrK0qVLgXAlzEakjFNEJCbdOEVEYtKNU0QkJt04RURiSrtxaCXwfv41a7ag+nLvkERBGpDi2pwU1xJSHTkEYGYzsjjKIqvlTktWP5+sljstWf18al1uPaqLiMSkG6eISEz1uHGOqsM1k5DVcqclq59PVsudlqx+PjUtd+p1nCIiWadHdRGRmFK7cZrZADObb2YLzWxEWteNy8y6mdkUM5tnZnPN7Nz8/i5m9g8zW5B/7VzvsjaKLMRWcY1PcW3lumk8qptZB+AVoD+wBJgODHXOvVzzi8eUX3N6W+fcLDPbFJgJHAWcAqx2zl2d/0/U2Tl3UR2L2hCyElvFNR7FtXVpZZz7AAudc4ucc58AdwEDU7p2LM65Zc65Wfntd4F5QFdy5R2TP20MueBIRmKruMamuLYirRtnV+CNyPdL8vsampl1B3Yntyb11s65ZZALFrBV/UrWUDIXW8W1LIprK9K6cRZb57mhm/PNrBMwATjPObem3uVpYJmKreJaNsW1FWndOJcA3SLfbwcsTenasZnZBuSCMNY5NzG/e3m+PsXXq6yoV/kaTGZiq7jGori2Iq0b53Sgl5n1MLOOwBBgUkrXjsXMDLgFmOecuz5yaBIwLL89DLg/7bI1qEzEVnGNTXFt7bppdYA3syOAG4EOwK3OuatSuXBMZrY/8DTwIvB5fvcl5OpNxgPbA4uBY51zq+tSyAaThdgqrvEprq1cVyOHRETi0cghEZGYdOMUEYlJN04RkZh04xQRiUk3ThGRmHTjFBGJSTdOEZGYdOMUEYnp/wM6qn2w8AQFsAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from matplotlib import pyplot\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape our data to be in the forma [samples, width, height, color_depth]\n",
-    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
-    "\n",
-    "# Change datatype to float32\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# define data preparation\n",
-    "train_datagen = ImageDataGenerator(vertical_flip=True,\n",
-    "                                   horizontal_flip=True)\n",
-    "\n",
-    "# fit parameters from data\n",
-    "train_datagen.fit(x_train)\n",
-    "\n",
-    "# configure batch size and retrieve one batch of images\n",
-    "for x_batch, y_batch in train_datagen.flow(x_train, y_train, batch_size=9):\n",
-    "    # create a grid of 3x3 images\n",
-    "    for i in range(0, 9):\n",
-    "        pyplot.subplot(330 + 1 + i)\n",
-    "        pyplot.imshow(x_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))# show the plot\n",
-    "    pyplot.show()\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Random Shifts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from matplotlib import pyplot\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape our data to be in the forma [samples, width, height, color_depth]\n",
-    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
-    "\n",
-    "# Change datatype to float32\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# define data preparation\n",
-    "train_datagen = ImageDataGenerator(width_shift_range=0.3,\n",
-    "                                   height_shift_range=0.3)\n",
-    "\n",
-    "# fit parameters from data\n",
-    "train_datagen.fit(x_train)\n",
-    "\n",
-    "# configure batch size and retrieve one batch of images\n",
-    "for x_batch, y_batch in train_datagen.flow(x_train, y_train, batch_size=9):\n",
-    "    # create a grid of 3x3 images\n",
-    "    for i in range(0, 9):\n",
-    "        pyplot.subplot(330 + 1 + i)\n",
-    "        pyplot.imshow(x_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))# show the plot\n",
-    "    pyplot.show()\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Applying all at once"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from matplotlib import pyplot\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Load data\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# Reshape our data to be in the forma [samples, width, height, color_depth]\n",
-    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
-    "\n",
-    "# Change datatype to float32\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# define data preparation\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "        rotation_range=45,\n",
-    "        width_shift_range=0.2,\n",
-    "        height_shift_range=0.2,\n",
-    "        shear_range=0.2,\n",
-    "        zoom_range=0.2,\n",
-    "        horizontal_flip=True,\n",
-    "        fill_mode='nearest')\n",
-    "\n",
-    "# fit parameters from data\n",
-    "train_datagen.fit(x_train)\n",
-    "\n",
-    "# configure batch size and retrieve one batch of images\n",
-    "for x_batch, y_batch in train_datagen.flow(x_train, y_train, batch_size=9):\n",
-    "    # create a grid of 3x3 images\n",
-    "    for i in range(0, 9):\n",
-    "        pyplot.subplot(330 + 1 + i)\n",
-    "        pyplot.imshow(x_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))# show the plot\n",
-    "    pyplot.show()\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Read more about it at the official Keras Documentation\n",
-    "https://keras.io/preprocessing/image/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test Augmentation on a single image\n",
-    "- Outputs to ./preview diretory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
-    "\n",
-    "datagen = ImageDataGenerator(\n",
-    "        rotation_range=40,\n",
-    "        width_shift_range=0.2,\n",
-    "        height_shift_range=0.2,\n",
-    "        shear_range=0.2,\n",
-    "        zoom_range=0.2,\n",
-    "        horizontal_flip=True,\n",
-    "        fill_mode='nearest')\n",
-    "\n",
-    "img = load_img('dog.jpeg')  \n",
-    "x = img_to_array(img)  # creating a Numpy array with shape (3, 150, 150)\n",
-    "x = x.reshape((1,) + x.shape)  # converting to a Numpy array with shape (1, 3, 150, 150)\n",
-    "\n",
-    "i = 0\n",
-    "for batch in datagen.flow(x, save_to_dir='output', save_prefix='dog', save_format='jpeg'):\n",
-    "    i += 1\n",
-    "    if i > 35:\n",
-    "        break "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:ddd167c1ab455569d8304594d2622e70dc2ecd8126303dee640445cbe021ac54
+size 88984

11. Confusion Matrix and Viewing Misclassifications/11.1 - 11.2 - MNIST Confusion Matrix Analysis and Viewing Misclassifications.ipynb

@@ -1,484 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Confusion Matrix Analysis and Viewing Misclassifications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (60000, 28, 28, 1)\n",
-      "60000 train samples\n",
-      "10000 test samples\n",
-      "Number of Classes: 10\n",
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 9216)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 128)               1179776   \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                1290      \n",
-      "=================================================================\n",
-      "Total params: 1,199,882\n",
-      "Trainable params: 1,199,882\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "60000/60000 [==============================] - 117s 2ms/sample - loss: 0.7767 - accuracy: 0.7570 - val_loss: 0.2555 - val_accuracy: 0.9265\n",
-      "Test loss: 0.25551080925762654\n",
-      "Test accuracy: 0.9265\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.optimizers import SGD \n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "\n",
-    "# Training Parameters\n",
-    "batch_size = 64\n",
-    "epochs = 1\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "# Let's count the number columns in our hot encoded matrix \n",
-    "print (\"Number of Classes: \" + str(y_test.shape[1]))\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
-    "\n",
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))\n",
-    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(128, activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = SGD(0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "# We can use the summary function to display our model layers and parameters\n",
-    "print(model.summary())\n",
-    "\n",
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          verbose=1,\n",
-    "          validation_data=(x_test, y_test))\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Let's save our history file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle \n",
-    "\n",
-    "pickle_out = open(\"MNIST_history.pickle\",\"wb\")\n",
-    "pickle.dump(history.history, pickle_out)\n",
-    "pickle_out.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Loading out saved history is as simple as these two lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'loss': [0.7766780077775319], 'accuracy': [0.75701666], 'val_loss': [0.255510806620121], 'val_accuracy': [0.9265]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pickle_in = open(\"MNIST_history.pickle\",\"rb\")\n",
-    "saved_history = pickle.load(pickle_in)\n",
-    "print(saved_history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our loss charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "loss_values = history_dict['loss']\n",
-    "val_loss_values = history_dict['val_loss']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_loss_values, label='Validation/Test Loss')\n",
-    "line2 = plt.plot(epochs, loss_values, label='Training Loss')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Loss')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our accuracy charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "acc_values = history_dict['accuracy']\n",
-    "val_acc_values = history_dict['val_accuracy']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_acc_values, label='Validation/Test Accuracy')\n",
-    "line2 = plt.plot(epochs, acc_values, label='Training Accuracy')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Now let's display our Confusion Matrix and Classification Report"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.94      0.98      0.96       980\n",
-      "           1       0.95      0.99      0.97      1135\n",
-      "           2       0.94      0.91      0.92      1032\n",
-      "           3       0.88      0.94      0.91      1010\n",
-      "           4       0.91      0.94      0.93       982\n",
-      "           5       0.96      0.85      0.90       892\n",
-      "           6       0.93      0.96      0.95       958\n",
-      "           7       0.90      0.94      0.92      1028\n",
-      "           8       0.92      0.88      0.90       974\n",
-      "           9       0.95      0.87      0.91      1009\n",
-      "\n",
-      "    accuracy                           0.93     10000\n",
-      "   macro avg       0.93      0.93      0.93     10000\n",
-      "weighted avg       0.93      0.93      0.93     10000\n",
-      "\n",
-      "[[ 964    0    1    3    0    2    7    1    2    0]\n",
-      " [   0 1120    4    2    0    1    3    0    5    0]\n",
-      " [  12    4  940   14   10    0   11   19   21    1]\n",
-      " [   1    2   11  950    1    5    0   18   17    5]\n",
-      " [   1    4    6    0  925    1   18    3    2   22]\n",
-      " [  18    3    4   60   10  754   16    4   19    4]\n",
-      " [  13    3    4    2    3    9  920    1    3    0]\n",
-      " [   2   21   20    2    9    0    0  963    3    8]\n",
-      " [   7   10    8   33   10   11   11   24  856    4]\n",
-      " [  11    9    3   18   49    3    1   39    3  873]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import classification_report,confusion_matrix\n",
-    "import numpy as np\n",
-    "\n",
-    "y_pred = model.predict_classes(x_test)\n",
-    "\n",
-    "print(classification_report(np.argmax(y_test,axis=1), y_pred))\n",
-    "print(confusion_matrix(np.argmax(y_test,axis=1), y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying our misclassified data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Indices of misclassifed data are: \n",
-      "\n",
-      "(array([   8,   33,   62,   66,   73,   77,  121,  124,  151,  193,  195,\n",
-      "        217,  233,  241,  247,  259,  290,  300,  313,  318,  320,  321,\n",
-      "        340,  341,  349,  352,  359,  362,  381,  403,  406,  412,  435,\n",
-      "        444,  445,  448,  464,  478,  479,  483,  495,  502,  507,  511,\n",
-      "        515,  528,  530,  543,  551,  565,  578,  582,  591,  606,  610,\n",
-      "        613,  619,  624,  628,  659,  667,  684,  689,  691,  707,  717,\n",
-      "        720,  728,  740,  791,  839,  844,  924,  939,  944,  947,  950,\n",
-      "        951,  956,  965,  975,  982,  992, 1003, 1014, 1032, 1033, 1039,\n",
-      "       1044, 1062, 1068, 1082, 1089, 1101, 1107, 1112, 1114, 1119, 1128,\n",
-      "       1152, 1181, 1192, 1198, 1200, 1204, 1206, 1224, 1226, 1228, 1232,\n",
-      "       1234, 1242, 1243, 1247, 1253, 1256, 1260, 1270, 1272, 1283, 1289,\n",
-      "       1299, 1319, 1326, 1378, 1393, 1402, 1409, 1423, 1433, 1440, 1453,\n",
-      "       1465, 1466, 1476, 1500, 1514, 1525, 1527, 1530, 1549, 1553, 1554,\n",
-      "       1559, 1581, 1587, 1601, 1609, 1621, 1634, 1640, 1678, 1681, 1709,\n",
-      "       1716, 1717, 1718, 1722, 1732, 1737, 1751, 1754, 1772, 1782, 1790,\n",
-      "       1819, 1850, 1874, 1878, 1899, 1901, 1917, 1930, 1938, 1940, 1948,\n",
-      "       1952, 1955, 1968, 1970, 1973, 1982, 1984, 2016, 2024, 2035, 2037,\n",
-      "       2040, 2043, 2044, 2052, 2053, 2068, 2070, 2093, 2098, 2099, 2109,\n",
-      "       2118, 2125, 2129, 2130, 2135, 2138, 2177, 2182, 2183, 2185, 2186,\n",
-      "       2189, 2192, 2208, 2215, 2224, 2232, 2266, 2272, 2293, 2299, 2325,\n",
-      "       2339, 2362, 2369, 2371, 2378, 2381, 2386, 2387, 2393, 2394, 2395,\n",
-      "       2404, 2406, 2408, 2414, 2422, 2425, 2447, 2460, 2488, 2515, 2526,\n",
-      "       2528, 2542, 2545, 2556, 2559, 2560, 2578, 2582, 2586, 2589, 2598,\n",
-      "       2604, 2607, 2610, 2631, 2635, 2648, 2654, 2670, 2695, 2698, 2730,\n",
-      "       2740, 2751, 2758, 2760, 2770, 2771, 2780, 2810, 2812, 2832, 2850,\n",
-      "       2863, 2896, 2905, 2914, 2925, 2927, 2930, 2945, 2953, 2970, 2986,\n",
-      "       2990, 2995, 3005, 3060, 3073, 3078, 3100, 3102, 3110, 3114, 3117,\n",
-      "       3130, 3132, 3133, 3136, 3139, 3145, 3157, 3167, 3189, 3206, 3225,\n",
-      "       3240, 3269, 3284, 3289, 3316, 3319, 3329, 3330, 3333, 3369, 3405,\n",
-      "       3406, 3414, 3436, 3475, 3503, 3520, 3549, 3552, 3558, 3565, 3567,\n",
-      "       3573, 3578, 3580, 3597, 3598, 3604, 3618, 3629, 3662, 3664, 3681,\n",
-      "       3687, 3702, 3709, 3716, 3718, 3726, 3732, 3751, 3757, 3763, 3767,\n",
-      "       3769, 3776, 3778, 3780, 3796, 3806, 3808, 3811, 3817, 3818, 3821,\n",
-      "       3833, 3836, 3838, 3846, 3848, 3853, 3855, 3862, 3869, 3876, 3893,\n",
-      "       3902, 3906, 3924, 3926, 3929, 3941, 3946, 3954, 3962, 3968, 3976,\n",
-      "       3984, 3985, 3998, 4000, 4031, 4063, 4065, 4068, 4072, 4075, 4076,\n",
-      "       4078, 4093, 4111, 4131, 4140, 4145, 4152, 4154, 4159, 4163, 4176,\n",
-      "       4201, 4205, 4211, 4212, 4224, 4238, 4248, 4255, 4265, 4271, 4272,\n",
-      "       4284, 4289, 4294, 4297, 4300, 4302, 4306, 4313, 4315, 4317, 4341,\n",
-      "       4355, 4360, 4374, 4380, 4405, 4425, 4433, 4435, 4449, 4451, 4454,\n",
-      "       4477, 4497, 4498, 4500, 4505, 4521, 4523, 4540, 4548, 4567, 4571,\n",
-      "       4575, 4601, 4615, 4633, 4639, 4640, 4662, 4671, 4724, 4731, 4735,\n",
-      "       4751, 4785, 4807, 4808, 4814, 4823, 4828, 4829, 4837, 4863, 4874,\n",
-      "       4876, 4879, 4880, 4886, 4890, 4910, 4943, 4950, 4952, 4956, 4966,\n",
-      "       4978, 4990, 5001, 5009, 5015, 5065, 5067, 5068, 5100, 5135, 5140,\n",
-      "       5210, 5217, 5246, 5299, 5311, 5331, 5360, 5457, 5522, 5562, 5600,\n",
-      "       5601, 5611, 5634, 5642, 5677, 5734, 5735, 5749, 5757, 5821, 5842,\n",
-      "       5852, 5862, 5867, 5874, 5887, 5888, 5891, 5913, 5922, 5936, 5937,\n",
-      "       5955, 5957, 5972, 5973, 5981, 5982, 5985, 6035, 6042, 6043, 6059,\n",
-      "       6071, 6081, 6091, 6112, 6124, 6157, 6166, 6168, 6172, 6173, 6304,\n",
-      "       6347, 6385, 6400, 6421, 6425, 6426, 6505, 6517, 6555, 6560, 6568,\n",
-      "       6569, 6571, 6574, 6576, 6577, 6597, 6598, 6603, 6625, 6641, 6642,\n",
-      "       6651, 6662, 6706, 6721, 6725, 6740, 6744, 6746, 6755, 6765, 6775,\n",
-      "       6784, 6785, 6793, 6817, 6870, 6872, 6894, 6895, 6906, 6919, 6926,\n",
-      "       7003, 7035, 7043, 7094, 7121, 7130, 7198, 7212, 7235, 7372, 7432,\n",
-      "       7434, 7451, 7459, 7473, 7492, 7498, 7579, 7580, 7637, 7672, 7673,\n",
-      "       7756, 7777, 7779, 7786, 7797, 7821, 7823, 7849, 7859, 7886, 7888,\n",
-      "       7905, 7918, 7921, 7945, 7991, 8020, 8044, 8062, 8072, 8081, 8091,\n",
-      "       8094, 8095, 8165, 8183, 8196, 8198, 8246, 8279, 8332, 8339, 8408,\n",
-      "       8410, 8426, 8431, 8457, 8477, 8520, 8522, 8530, 8639, 8912, 9007,\n",
-      "       9009, 9010, 9015, 9016, 9019, 9024, 9026, 9036, 9045, 9168, 9211,\n",
-      "       9245, 9280, 9316, 9422, 9427, 9433, 9446, 9456, 9465, 9482, 9530,\n",
-      "       9554, 9560, 9587, 9610, 9624, 9634, 9642, 9664, 9679, 9680, 9692,\n",
-      "       9698, 9700, 9712, 9716, 9719, 9726, 9729, 9740, 9741, 9744, 9745,\n",
-      "       9749, 9751, 9752, 9755, 9768, 9770, 9777, 9779, 9780, 9792, 9808,\n",
-      "       9811, 9832, 9839, 9858, 9867, 9874, 9883, 9888, 9890, 9892, 9893,\n",
-      "       9905, 9925, 9941, 9944, 9959, 9970, 9975, 9980, 9982], dtype=int64),)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Use numpy to create an array that stores a value of 1 when a misclassification occurs\n",
-    "result = np.absolute(y_test - y_pred)\n",
-    "result_indices = np.nonzero(result > 0)\n",
-    "\n",
-    "#  Display the indices of mislassifications\n",
-    "print(\"Indices of misclassifed data are: \\n\\n\" + str(result_indices))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Displaying the misclassifications"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2 \n",
-    "#from keras.models import load_model\n",
-    "\n",
-    "#classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/mnist_simple_cnn.h5')\n",
-    "\n",
-    "def draw_test(name, pred, input_im, true_label):\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0]*2 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
-    "    cv2.putText(expanded_image, str(true_label), (250, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,0,255), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "\n",
-    "    input_im = x_test[result_indices[0][i]]\n",
-    "    #print(y_test[result_indices[0][i]])\n",
-    "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC) \n",
-    "    input_im = input_im.reshape(1,28,28,1) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(model.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "    draw_test(\"Prediction\", res, imageL, y_test[result_indices[0][i]]) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  }
- ],
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-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
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-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
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- "nbformat": 4,
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+size 45598

12. Optimizers, Adaptive Learning Rate & Callbacks/12.2 Checkpointing Models and Early Stopping.ipynb

@@ -1,277 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (60000, 28, 28, 1)\n",
-      "60000 train samples\n",
-      "10000 test samples\n",
-      "Number of Classes: 10\n",
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 9216)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 128)               1179776   \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                1290      \n",
-      "=================================================================\n",
-      "Total params: 1,199,882\n",
-      "Trainable params: 1,199,882\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.optimizers import SGD \n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras import backend as K\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
-    "import os\n",
-    "\n",
-    "# Training Parameters\n",
-    "batch_size = 64\n",
-    "epochs = 15\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "# Let's count the number columns in our hot encoded matrix \n",
-    "print (\"Number of Classes: \" + str(y_test.shape[1]))\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
-    "\n",
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
-    "                 activation='relu',\n",
-    "                 input_shape=input_shape))\n",
-    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(128, activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = SGD(0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "print(model.summary())\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"MNIST_Checkpoint.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "callbacks = [checkpoint]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "\n",
-      "Epoch 00001: val_loss improved from inf to 0.24715, saving model to MNIST_Checkpoint.h5\n",
-      "60000/60000 - 97s - loss: 0.7167 - accuracy: 0.7759 - val_loss: 0.2471 - val_accuracy: 0.9275\n",
-      "Test loss: 0.24714763118624689\n",
-      "Test accuracy: 0.9275\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size = batch_size,\n",
-    "          epochs = epochs,\n",
-    "          verbose = 2,\n",
-    "          callbacks = callbacks,\n",
-    "          validation_data = (x_test, y_test))\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Adding Multiple Call Backs & Early Stopping\n",
-    "\n",
-    "We can use other call back methods to monitor our training process such as **Early Stopping**. Checkout the Keras documentation for more:\n",
-    "- https://keras.io/callbacks/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.callbacks import EarlyStopping\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', # value being monitored for improvement\n",
-    "                          min_delta = 0, #Abs value and is the min change required before we stop\n",
-    "                          patience = 3, #Number of epochs we wait before stopping \n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True) #keeps the best weigths once stopped\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We can attempt to run again to see if it worked!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/3\n",
-      "\n",
-      "Epoch 00001: val_loss improved from 0.24715 to 0.18733, saving model to MNIST_Checkpoint.h5\n",
-      "60000/60000 - 105s - loss: 0.3593 - accuracy: 0.8905 - val_loss: 0.1873 - val_accuracy: 0.9437\n",
-      "Epoch 2/3\n",
-      "\n",
-      "Epoch 00002: val_loss improved from 0.18733 to 0.15683, saving model to MNIST_Checkpoint.h5\n",
-      "60000/60000 - 105s - loss: 0.3018 - accuracy: 0.9084 - val_loss: 0.1568 - val_accuracy: 0.9525\n",
-      "Epoch 3/3\n",
-      "\n",
-      "Epoch 00003: val_loss improved from 0.15683 to 0.13865, saving model to MNIST_Checkpoint.h5\n",
-      "60000/60000 - 108s - loss: 0.2658 - accuracy: 0.9205 - val_loss: 0.1386 - val_accuracy: 0.9578\n",
-      "Test loss: 0.1386499687358737\n",
-      "Test accuracy: 0.9578\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=64,\n",
-    "          epochs=3,\n",
-    "          verbose=2,\n",
-    "          callbacks = callbacks,\n",
-    "          validation_data=(x_test, y_test))\n",
-    "\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Another useful callback is Reducing our learning Rate on Plateau\n",
-    "\n",
-    "We can avoid having our oscillate around the global minimum by attempting to reduce the Learn Rate by a certain fact. If no improvement is seen in our monitored metric (val_loss typically), we wait a certain number of epochs (patience) then this callback reduces the learning rate by a factor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
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13. Building LeNet and AlexNet in Keras/13.1 Built LeNet and test on MNIST.ipynb

@@ -1,209 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's construct LeNet in Keras!\n",
-    "\n",
-    "#### First let's load and prep our MNIST data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.regularizers import l2\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let's create our layers to replicate LeNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 28, 28, 20)        520       \n",
-      "_________________________________________________________________\n",
-      "activation (Activation)      (None, 28, 28, 20)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 14, 14, 20)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 14, 14, 50)        25050     \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 14, 14, 50)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 50)          0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 2450)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 500)               1225500   \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 500)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                5010      \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 1,256,080\n",
-      "Trainable params: 1,256,080\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "# 2 sets of CRP (Convolution, RELU, Pooling)\n",
-    "model.add(Conv2D(20, (5, 5),\n",
-    "                 padding = \"same\", \n",
-    "                 input_shape = input_shape))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(50, (5, 5),\n",
-    "                 padding = \"same\"))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
-    "\n",
-    "# Fully connected layers (w/ RELU)\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(500))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "\n",
-    "# Softmax (for classification)\n",
-    "model.add(Dense(num_classes))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "           \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = tf.keras.optimizers.Adadelta(),\n",
-    "              metrics = ['accuracy'])\n",
-    "    \n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let us train LeNet on our MNIST Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "60000/60000 [==============================] - 82s 1ms/sample - loss: 2.2876 - accuracy: 0.1511 - val_loss: 2.2647 - val_accuracy: 0.2432\n",
-      "10000/10000 [==============================] - 4s 436us/sample - loss: 2.2647 - accuracy: 0.2432\n",
-      "Test loss: 2.264678302001953\n",
-      "Test accuracy: 0.2432\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Training Parameters\n",
-    "batch_size = 128\n",
-    "epochs = 1\n",
-    "\n",
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          validation_data=(x_test, y_test),\n",
-    "          shuffle=True)\n",
-    "\n",
-    "model.save(\"mnist_LeNet.h5\")\n",
-    "\n",
-    "# Evaluate the performance of our trained model\n",
-    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
-    "print('Test loss:', scores[0])\n",
-    "print('Test accuracy:', scores[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
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13. Building LeNet and AlexNet in Keras/13.2 Build AlexNet and test on CIFAR10.ipynb

@@ -1,266 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's construct AlexNet in Keras!\n",
-    "\n",
-    "#### First let's load and prep our CIFAR10 data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (50000, 32, 32, 3)\n",
-      "50000 train samples\n",
-      "10000 test samples\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import cifar10\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.regularizers import l2\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "\n",
-    "# Loads the CIFAR dataset\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "\n",
-    "# Display our data shape/dimensions\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "num_classes = 10\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let's create our layers to replicate AlexNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 32, 32, 96)        34944     \n",
-      "_________________________________________________________________\n",
-      "batch_normalization (BatchNo (None, 32, 32, 96)        384       \n",
-      "_________________________________________________________________\n",
-      "activation (Activation)      (None, 32, 32, 96)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 16, 16, 96)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 16, 16, 256)       614656    \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 16, 16, 256)       1024      \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 16, 16, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "zero_padding2d (ZeroPadding2 (None, 10, 10, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 10, 10, 512)       1180160   \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 10, 10, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 10, 10, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "zero_padding2d_1 (ZeroPaddin (None, 7, 7, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 7, 7, 1024)        4719616   \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 7, 7, 1024)        4096      \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 7, 7, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "zero_padding2d_2 (ZeroPaddin (None, 9, 9, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 9, 9, 1024)        9438208   \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 9, 9, 1024)        4096      \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 9, 9, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 16384)             0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 3072)              50334720  \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 3072)              12288     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 3072)              0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 3072)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 4096)              12587008  \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 4096)              16384     \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 4096)              0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 4096)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                40970     \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 10)                40        \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 78,990,642\n",
-      "Trainable params: 78,970,462\n",
-      "Non-trainable params: 20,180\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "l2_reg = 0\n",
-    "\n",
-    "# Initialize model\n",
-    "model = Sequential()\n",
-    "\n",
-    "# 1st Conv Layer \n",
-    "model.add(Conv2D(96, (11, 11), input_shape=x_train.shape[1:],\n",
-    "    padding='same', kernel_regularizer=l2(l2_reg)))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "# 2nd Conv Layer \n",
-    "model.add(Conv2D(256, (5, 5), padding='same'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "# 3rd Conv Layer \n",
-    "model.add(ZeroPadding2D((1, 1)))\n",
-    "model.add(Conv2D(512, (3, 3), padding='same'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "# 4th Conv Layer \n",
-    "model.add(ZeroPadding2D((1, 1)))\n",
-    "model.add(Conv2D(1024, (3, 3), padding='same'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "\n",
-    "# 5th Conv Layer \n",
-    "model.add(ZeroPadding2D((1, 1)))\n",
-    "model.add(Conv2D(1024, (3, 3), padding='same'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "\n",
-    "# 1st FC Layer\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(3072))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# 2nd FC Layer\n",
-    "model.add(Dense(4096))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# 3rd FC Layer\n",
-    "model.add(Dense(num_classes))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Activation('softmax'))\n",
-    "\n",
-    "print(model.summary())\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = tf.keras.optimizers.Adadelta(),\n",
-    "              metrics = ['accuracy'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let us train AlexNet on our CIFAR10 Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Training Parameters\n",
-    "batch_size = 32\n",
-    "epochs = 1\n",
-    "\n",
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          validation_data=(x_test, y_test),\n",
-    "          shuffle=True)\n",
-    "\n",
-    "model.save(\"CIFAR10_AlexNet_1_Epoch.h5\")\n",
-    "\n",
-    "# Evaluate the performance of our trained model\n",
-    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
-    "print('Test loss:', scores[0])\n",
-    "print('Test accuracy:', scores[1])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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13. Building LeNet and AlexNet in Keras/13.4 Fashion MNIST.ipynb

@@ -1,445 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fashion MNIST"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### After downloading our dataset we see it's coded in the ubyte form\n",
-    "- We then use the following function to read the data and return it as a numpy array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import struct\n",
-    "import numpy as np\n",
-    "\n",
-    "def read_idx(filename):\n",
-    "    \"\"\"Credit: https://gist.github.com/tylerneylon\"\"\"\n",
-    "    with open(filename, 'rb') as f:\n",
-    "        zero, data_type, dims = struct.unpack('>HBB', f.read(4))\n",
-    "        shape = tuple(struct.unpack('>I', f.read(4))[0] for d in range(dims))\n",
-    "        return np.frombuffer(f.read(), dtype=np.uint8).reshape(shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We use the function to extact our training and test datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train = read_idx(\"./fashion_mnist/train-images-idx3-ubyte\")\n",
-    "y_train = read_idx(\"./fashion_mnist/train-labels-idx1-ubyte\")\n",
-    "x_test = read_idx(\"./fashion_mnist/t10k-images-idx3-ubyte\")\n",
-    "y_test = read_idx(\"./fashion_mnist/t10k-labels-idx1-ubyte\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's inspect our dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initial shape or dimensions of x_train (60000, 28, 28)\n",
-      "Number of samples in our training data: 60000\n",
-      "Number of labels in our training data: 60000\n",
-      "Number of samples in our test data: 10000\n",
-      "Number of labels in our test data: 10000\n",
-      "\n",
-      "Dimensions of x_train:(28, 28)\n",
-      "Labels in x_train:(60000,)\n",
-      "\n",
-      "Dimensions of x_test:(28, 28)\n",
-      "Labels in y_test:(10000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# printing the number of samples in x_train, x_test, y_train, y_test\n",
-    "print(\"Initial shape or dimensions of x_train\", str(x_train.shape))\n",
-    "\n",
-    "print (\"Number of samples in our training data: \" + str(len(x_train)))\n",
-    "print (\"Number of labels in our training data: \" + str(len(y_train)))\n",
-    "print (\"Number of samples in our test data: \" + str(len(x_test)))\n",
-    "print (\"Number of labels in our test data: \" + str(len(y_test)))\n",
-    "print()\n",
-    "print (\"Dimensions of x_train:\" + str(x_train[0].shape))\n",
-    "print (\"Labels in x_train:\" + str(y_train.shape))\n",
-    "print()\n",
-    "print (\"Dimensions of x_test:\" + str(x_test[0].shape))\n",
-    "print (\"Labels in y_test:\" + str(y_test.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's view some sample images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 6 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Let's do the same thing but using matplotlib to plot 6 images \n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plots 6 images, note subplot's arugments are nrows,ncols,index\n",
-    "# we set the color map to grey since our image dataset is grayscale\n",
-    "plt.subplot(331)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "plt.subplot(332)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "plt.subplot(333)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "plt.subplot(334)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "plt.subplot(335)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "plt.subplot(336)\n",
-    "random_num = np.random.randint(0,len(x_train))\n",
-    "plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))\n",
-    "\n",
-    "# Display out plots\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's create our model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_classes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (60000, 28, 28, 1)\n",
-      "60000 train samples\n",
-      "10000 test samples\n",
-      "Number of Classes: 10\n",
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "batch_normalization (BatchNo (None, 26, 26, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 9216)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 128)               1179776   \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 128)               512       \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                1290      \n",
-      "_________________________________________________________________\n",
-      "activation (Activation)      (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 1,200,778\n",
-      "Trainable params: 1,200,330\n",
-      "Non-trainable params: 448\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
-    "from tensorflow.keras.optimizers import SGD \n",
-    "\n",
-    "# Training Parameters\n",
-    "batch_size = 128\n",
-    "epochs = 1\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255.0\n",
-    "x_test /= 255.0\n",
-    "\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "# Let's count the number columns in our hot encoded matrix \n",
-    "print (\"Number of Classes: \" + str(num_classes))\n",
-    "\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
-    "\n",
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
-    "                 activation='relu',\n",
-    "                 input_shape=input_shape))\n",
-    "model.add(BatchNormalization())\n",
-    "\n",
-    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(BatchNormalization())\n",
-    "\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(128, activation='relu'))\n",
-    "model.add(BatchNormalization())\n",
-    "\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes))\n",
-    "model.add(Activation('softmax'))\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = SGD(0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's train our model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "60000/60000 [==============================] - 150s 3ms/sample - loss: 0.6229 - accuracy: 0.7877 - val_loss: 0.5973 - val_accuracy: 0.8136\n",
-      "Test loss: 0.5973387075424195\n",
-      "Test accuracy: 0.8136\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          verbose=1,\n",
-    "          validation_data=(x_test, y_test))\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's test out our model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "def getLabel(input_class):\n",
-    "    number = int(input_class)\n",
-    "    if number == 0:\n",
-    "        return \"T-shirt/top \"\n",
-    "    if number == 1:\n",
-    "        return \"Trouser\"\n",
-    "    if number == 2:\n",
-    "        return \"Pullover\"\n",
-    "    if number == 3:\n",
-    "        return \"Dress\"\n",
-    "    if number == 4:\n",
-    "        return \"Coat\"\n",
-    "    if number == 5:\n",
-    "        return \"Sandal\"\n",
-    "    if number == 6:\n",
-    "        return \"Shirt\"\n",
-    "    if number == 7:\n",
-    "        return \"Sneaker\"\n",
-    "    if number == 8:\n",
-    "        return \"Bag\"\n",
-    "    if number == 9:\n",
-    "        return \"Ankle boot\"\n",
-    "\n",
-    "def draw_test(name, pred, actual, input_im):\n",
-    "    BLACK = [0,0,0]\n",
-    "\n",
-    "    res = getLabel(pred)\n",
-    "    actual = getLabel(actual)   \n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, 4*imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, \"Predicted - \" + str(res), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,1, (0,255,0), 1)\n",
-    "    cv2.putText(expanded_image, \"   Actual - \" + str(actual), (152, 90) , cv2.FONT_HERSHEY_COMPLEX_SMALL,1, (0,0,255), 1)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "    actual = y_test[rand].argmax(axis=0)\n",
-    "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC)\n",
-    "    input_im = input_im.reshape(1,28,28,1) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(model.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "\n",
-    "    draw_test(\"Prediction\", res, actual, imageL) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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14. ImageNet and Pretrained Models VGG16_ResNet50_InceptionV3/14.1 Experimenting with pre-trained Models in Keras.ipynb

@@ -1,227 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's start with loading ResNet50 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
-    "import numpy as np\n",
-    "\n",
-    "resnet_model = ResNet50(weights='imagenet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Predicted: [('n02100583', 'vizsla', 0.5282586), ('n02092339', 'Weimaraner', 0.32402116), ('n02099849', 'Chesapeake_Bay_retriever', 0.07540441)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.preprocessing import image\n",
-    "\n",
-    "img_path = './images/dog.jpg' \n",
-    "\n",
-    "img = image.load_img(img_path, target_size=(224, 224))\n",
-    "x = image.img_to_array(img)\n",
-    "x = np.expand_dims(x, axis=0)\n",
-    "x = preprocess_input(x)\n",
-    "\n",
-    "preds = resnet_model.predict(x)\n",
-    "# decode the results into a list of tuples (class, description, probability)\n",
-    "# (one such list for each sample in the batch)\n",
-    "print('Predicted:', decode_predictions(preds, top=3)[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's run through a few test images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "\n",
-    "# Our openCV function that displays the image and it's predicted labels \n",
-    "def draw_test(name, preditions, input_im):\n",
-    "    \"\"\"Function displays the output of the prediction alongside the orignal image\"\"\"\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[1]+300 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    img_width = input_im.shape[1]\n",
-    "    for (i,predition) in enumerate(preditions):\n",
-    "        string = str(predition[1]) + \" \" + str(predition[2])\n",
-    "        cv2.putText(expanded_image,str(name),(img_width + 50,50),cv2.FONT_HERSHEY_COMPLEX_SMALL,2,(0,0,255),1)\n",
-    "        cv2.putText(expanded_image,string,(img_width + 50,50+((i+1)*50)),cv2.FONT_HERSHEY_COMPLEX_SMALL,2,(0,255,0),1)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "# Get images located in ./images folder    \n",
-    "mypath = \"./images/\"\n",
-    "file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "\n",
-    "# Loop through images run them through our classifer\n",
-    "for file in file_names:\n",
-    "\n",
-    "    from tensorflow.keras.preprocessing import image # Need to reload as opencv2 seems to have a conflict\n",
-    "    img = image.load_img(mypath+file, target_size=(224, 224))\n",
-    "    x = image.img_to_array(img)\n",
-    "    x = np.expand_dims(x, axis=0)\n",
-    "    x = preprocess_input(x)\n",
-    "    \n",
-    "    #load image using opencv\n",
-    "    img2 = cv2.imread(mypath+file)\n",
-    "    imageL = cv2.resize(img2, None, fx=.5, fy=.5, interpolation = cv2.INTER_CUBIC) \n",
-    "    \n",
-    "    # Get Predictions\n",
-    "    preds = resnet_model.predict(x)\n",
-    "    preditions = decode_predictions(preds, top=3)[0]\n",
-    "    draw_test(\"Predictions\", preditions, imageL) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's now load VGG16 and InceptionV3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.applications import vgg16, inception_v3, resnet50\n",
-    " \n",
-    "#Loads the VGG16 model\n",
-    "vgg_model = vgg16.VGG16(weights='imagenet')\n",
-    " \n",
-    "# Loads the Inception_V3 model\n",
-    "inception_model = inception_v3.InceptionV3(weights='imagenet')\n",
-    " \n",
-    "# Loads the ResNet50 model \n",
-    "# uncomment the line below if you didn't load resnet50 beforehand\n",
-    "#resnet_model = resnet50.ResNet50(weights='imagenet')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compare all 3 Models with the same test images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getImage(path, dim=224, inception = False):\n",
-    "    img = image.load_img(path, target_size=(dim, dim))\n",
-    "    x = image.img_to_array(img)\n",
-    "    x = np.expand_dims(x, axis=0)\n",
-    "    if inception:\n",
-    "        x /= 255.\n",
-    "        x -= 0.5\n",
-    "        x *= 2.\n",
-    "    else:\n",
-    "        x = preprocess_input(x)\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get images located in ./images folder    \n",
-    "mypath = \"./images/\"\n",
-    "file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "\n",
-    "# Loop through images run them through our classifer\n",
-    "for file in file_names:\n",
-    "\n",
-    "    from tensorflow.keras.preprocessing import image # Need to reload as opencv2 seems to have a conflict\n",
-    "    #img = image.load_img(mypath+file, target_size=(dim, dim))\n",
-    "    x = getImage(mypath+file, 229)\n",
-    "    #load image using opencv\n",
-    "    img2 = cv2.imread(mypath+file)\n",
-    "    imageL = cv2.resize(img2, None, fx=.5, fy=.5, interpolation = cv2.INTER_CUBIC) \n",
-    "    \n",
-    "    # Get VGG16 Predictions\n",
-    "    x = getImage(mypath+file, 224)\n",
-    "    preds_vgg_model = vgg_model.predict(x)\n",
-    "    preditions_vgg = decode_predictions(preds_vgg_model, top=3)[0]\n",
-    "    draw_test(\"VGG16 Predictions\", preditions_vgg, imageL) \n",
-    "    \n",
-    "    # Get Inception_V3 Predictions\n",
-    "    x = getImage(mypath+file, 299, inception = True)\n",
-    "    preds_inception = inception_model.predict(x)\n",
-    "    preditions_inception = decode_predictions(preds_inception, top=3)[0]\n",
-    "    draw_test(\"Inception_V3 Predictions\", preditions_inception, imageL) \n",
-    "\n",
-    "    # Get ResNet50 Predictions\n",
-    "    x = getImage(mypath+file, 224)\n",
-    "    preds_resnet = resnet_model.predict(x)\n",
-    "    preditions_resnet = decode_predictions(preds_resnet, top=3)[0]\n",
-    "    draw_test(\"ResNet50 Predictions\", preditions_resnet, imageL) \n",
-    "    \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad641d6516f4e7974dfe8149b95fabfaf34b8f1cd7f5b4373365177ec7e3c792
+size 7394

15. Transfer Learning & Fine Tuning/15.2 Using MobileNet to make a Monkey Breed Classifier.ipynb

@@ -1,657 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using MobileNet for our Monkey Classifer\n",
-    "\n",
-    "### Loading the MobileNet Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Freeze all layers except the top 4, as we'll only be training the top 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf_no_top.h5\n",
-      "17227776/17225924 [==============================] - 14s 1us/step\n",
-      "0 InputLayer False\n",
-      "1 ZeroPadding2D False\n",
-      "2 Conv2D False\n",
-      "3 BatchNormalization False\n",
-      "4 ReLU False\n",
-      "5 DepthwiseConv2D False\n",
-      "6 BatchNormalization False\n",
-      "7 ReLU False\n",
-      "8 Conv2D False\n",
-      "9 BatchNormalization False\n",
-      "10 ReLU False\n",
-      "11 ZeroPadding2D False\n",
-      "12 DepthwiseConv2D False\n",
-      "13 BatchNormalization False\n",
-      "14 ReLU False\n",
-      "15 Conv2D False\n",
-      "16 BatchNormalization False\n",
-      "17 ReLU False\n",
-      "18 DepthwiseConv2D False\n",
-      "19 BatchNormalization False\n",
-      "20 ReLU False\n",
-      "21 Conv2D False\n",
-      "22 BatchNormalization False\n",
-      "23 ReLU False\n",
-      "24 ZeroPadding2D False\n",
-      "25 DepthwiseConv2D False\n",
-      "26 BatchNormalization False\n",
-      "27 ReLU False\n",
-      "28 Conv2D False\n",
-      "29 BatchNormalization False\n",
-      "30 ReLU False\n",
-      "31 DepthwiseConv2D False\n",
-      "32 BatchNormalization False\n",
-      "33 ReLU False\n",
-      "34 Conv2D False\n",
-      "35 BatchNormalization False\n",
-      "36 ReLU False\n",
-      "37 ZeroPadding2D False\n",
-      "38 DepthwiseConv2D False\n",
-      "39 BatchNormalization False\n",
-      "40 ReLU False\n",
-      "41 Conv2D False\n",
-      "42 BatchNormalization False\n",
-      "43 ReLU False\n",
-      "44 DepthwiseConv2D False\n",
-      "45 BatchNormalization False\n",
-      "46 ReLU False\n",
-      "47 Conv2D False\n",
-      "48 BatchNormalization False\n",
-      "49 ReLU False\n",
-      "50 DepthwiseConv2D False\n",
-      "51 BatchNormalization False\n",
-      "52 ReLU False\n",
-      "53 Conv2D False\n",
-      "54 BatchNormalization False\n",
-      "55 ReLU False\n",
-      "56 DepthwiseConv2D False\n",
-      "57 BatchNormalization False\n",
-      "58 ReLU False\n",
-      "59 Conv2D False\n",
-      "60 BatchNormalization False\n",
-      "61 ReLU False\n",
-      "62 DepthwiseConv2D False\n",
-      "63 BatchNormalization False\n",
-      "64 ReLU False\n",
-      "65 Conv2D False\n",
-      "66 BatchNormalization False\n",
-      "67 ReLU False\n",
-      "68 DepthwiseConv2D False\n",
-      "69 BatchNormalization False\n",
-      "70 ReLU False\n",
-      "71 Conv2D False\n",
-      "72 BatchNormalization False\n",
-      "73 ReLU False\n",
-      "74 ZeroPadding2D False\n",
-      "75 DepthwiseConv2D False\n",
-      "76 BatchNormalization False\n",
-      "77 ReLU False\n",
-      "78 Conv2D False\n",
-      "79 BatchNormalization False\n",
-      "80 ReLU False\n",
-      "81 DepthwiseConv2D False\n",
-      "82 BatchNormalization False\n",
-      "83 ReLU False\n",
-      "84 Conv2D False\n",
-      "85 BatchNormalization False\n",
-      "86 ReLU False\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.applications import MobileNet\n",
-    "\n",
-    "# MobileNet was designed to work on 224 x 224 pixel input images sizes\n",
-    "img_rows, img_cols = 224, 224 \n",
-    "\n",
-    "# Re-loads the MobileNet model without the top or FC layers\n",
-    "MobileNet = MobileNet(weights = 'imagenet', \n",
-    "                 include_top = False, \n",
-    "                 input_shape = (img_rows, img_cols, 3))\n",
-    "\n",
-    "# Here we freeze the last 4 layers \n",
-    "# Layers are set to trainable as True by default\n",
-    "for layer in MobileNet.layers:\n",
-    "    layer.trainable = False\n",
-    "    \n",
-    "# Let's print our layers \n",
-    "for (i,layer) in enumerate(MobileNet.layers):\n",
-    "    print(str(i) + \" \"+ layer.__class__.__name__, layer.trainable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's make a function that returns our FC Head"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def addTopModelMobileNet(bottom_model, num_classes):\n",
-    "    \"\"\"creates the top or head of the model that will be \n",
-    "    placed ontop of the bottom layers\"\"\"\n",
-    "\n",
-    "    top_model = bottom_model.output\n",
-    "    top_model = GlobalAveragePooling2D()(top_model)\n",
-    "    top_model = Dense(1024,activation='relu')(top_model)\n",
-    "    top_model = Dense(1024,activation='relu')(top_model)\n",
-    "    top_model = Dense(512,activation='relu')(top_model)\n",
-    "    top_model = Dense(num_classes,activation='softmax')(top_model)\n",
-    "    return top_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's add our FC Head back onto MobileNet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
-      "_________________________________________________________________\n",
-      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
-      "_________________________________________________________________\n",
-      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
-      "_________________________________________________________________\n",
-      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
-      "_________________________________________________________________\n",
-      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
-      "_________________________________________________________________\n",
-      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
-      "_________________________________________________________________\n",
-      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
-      "_________________________________________________________________\n",
-      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
-      "_________________________________________________________________\n",
-      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
-      "_________________________________________________________________\n",
-      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
-      "_________________________________________________________________\n",
-      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
-      "_________________________________________________________________\n",
-      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
-      "_________________________________________________________________\n",
-      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
-      "_________________________________________________________________\n",
-      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
-      "_________________________________________________________________\n",
-      "global_average_pooling2d_1 ( (None, 1024)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 1024)              1049600   \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 1024)              1049600   \n",
-      "_________________________________________________________________\n",
-      "dense_6 (Dense)              (None, 512)               524800    \n",
-      "_________________________________________________________________\n",
-      "dense_7 (Dense)              (None, 10)                5130      \n",
-      "=================================================================\n",
-      "Total params: 5,857,994\n",
-      "Trainable params: 2,629,130\n",
-      "Non-trainable params: 3,228,864\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, GlobalAveragePooling2D\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.models import Model\n",
-    "\n",
-    "# Set our class number to 3 (Young, Middle, Old)\n",
-    "num_classes = 10\n",
-    "\n",
-    "FC_Head = addTopModelMobileNet(MobileNet, num_classes)\n",
-    "\n",
-    "model = Model(inputs = MobileNet.input, outputs = FC_Head)\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading our Monkey Breed Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1098 images belonging to 10 classes.\n",
-      "Found 272 images belonging to 10 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "\n",
-    "train_data_dir = './monkey_breed/train'\n",
-    "validation_data_dir = './monkey_breed/validation'\n",
-    "\n",
-    "# Let's use some data augmentaiton \n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=45,\n",
-    "      width_shift_range=0.3,\n",
-    "      height_shift_range=0.3,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "# set our batch size (typically on most mid tier systems we'll use 16-32)\n",
-    "batch_size = 32\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical')\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training out Model\n",
-    "- Note we're using checkpointing and early stopping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"monkey_breed_mobileNet.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint]\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = RMSprop(lr = 0.001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "# Enter the number of training and validation samples here\n",
-    "nb_train_samples = 1097\n",
-    "nb_validation_samples = 272\n",
-    "\n",
-    "# We only train 5 EPOCHS \n",
-    "epochs = 1\n",
-    "batch_size = 16\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading our classifer\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "OSError",
-     "evalue": "SavedModel file does not exist at: monkey_breed_mobileNet.h5/{saved_model.pbtxt|saved_model.pb}",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-071d0eb65c49>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclassifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'monkey_breed_mobileNet.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\saving\\save.py\u001b[0m in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 149\u001b[1;33m     \u001b[0mloader_impl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    150\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\saved_model\\loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[1;34m(export_dir)\u001b[0m\n\u001b[0;32m     81\u001b[0m                   (export_dir,\n\u001b[0;32m     82\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mOSError\u001b[0m: SavedModel file does not exist at: monkey_breed_mobileNet.h5/{saved_model.pbtxt|saved_model.pb}"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('monkey_breed_mobileNet.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our classifer on some test images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Class - mantled_howler \n",
-      "Class - patas_monkey\n",
-      "Class - patas_monkey\n",
-      "Class - silvery_marmoset\n",
-      "Class - black_headed_night_monkey\n",
-      "Class - pygmy_marmoset \n",
-      "Class - silvery_marmoset\n",
-      "Class - mantled_howler \n",
-      "Class - common_squirrel_monkey\n",
-      "Class - patas_monkey\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "\n",
-    "monkey_breeds_dict = {\"[0]\": \"mantled_howler \", \n",
-    "                      \"[1]\": \"patas_monkey\",\n",
-    "                      \"[2]\": \"bald_uakari\",\n",
-    "                      \"[3]\": \"japanese_macaque\",\n",
-    "                      \"[4]\": \"pygmy_marmoset \",\n",
-    "                      \"[5]\": \"white_headed_capuchin\",\n",
-    "                      \"[6]\": \"silvery_marmoset\",\n",
-    "                      \"[7]\": \"common_squirrel_monkey\",\n",
-    "                      \"[8]\": \"black_headed_night_monkey\",\n",
-    "                      \"[9]\": \"nilgiri_langur\"}\n",
-    "\n",
-    "monkey_breeds_dict_n = {\"n0\": \"mantled_howler \", \n",
-    "                      \"n1\": \"patas_monkey\",\n",
-    "                      \"n2\": \"bald_uakari\",\n",
-    "                      \"n3\": \"japanese_macaque\",\n",
-    "                      \"n4\": \"pygmy_marmoset \",\n",
-    "                      \"n5\": \"white_headed_capuchin\",\n",
-    "                      \"n6\": \"silvery_marmoset\",\n",
-    "                      \"n7\": \"common_squirrel_monkey\",\n",
-    "                      \"n8\": \"black_headed_night_monkey\",\n",
-    "                      \"n9\": \"nilgiri_langur\"}\n",
-    "\n",
-    "def draw_test(name, pred, im):\n",
-    "    monkey = monkey_breeds_dict[str(pred)]\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(im, 80, 0, 0, 100 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    cv2.putText(expanded_image, monkey, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "def getRandomImage(path):\n",
-    "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
-    "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
-    "    random_directory = np.random.randint(0,len(folders))\n",
-    "    path_class = folders[random_directory]\n",
-    "    print(\"Class - \" + monkey_breeds_dict_n[str(path_class)])\n",
-    "    file_path = path + path_class\n",
-    "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
-    "    random_file_index = np.random.randint(0,len(file_names))\n",
-    "    image_name = file_names[random_file_index]\n",
-    "    return cv2.imread(file_path+\"/\"+image_name)    \n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    input_im = getRandomImage(\"./monkey_breed/validation/\")\n",
-    "    input_original = input_im.copy()\n",
-    "    input_original = cv2.resize(input_original, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
-    "    \n",
-    "    input_im = cv2.resize(input_im, (224, 224), interpolation = cv2.INTER_LINEAR)\n",
-    "    input_im = input_im / 255.\n",
-    "    input_im = input_im.reshape(1,224,224,3) \n",
-    "    \n",
-    "    # Get Prediction\n",
-    "    res = np.argmax(classifier.predict(input_im, 1, verbose = 0), axis=1)\n",
-    "    \n",
-    "    # Show image with predicted class\n",
-    "    draw_test(\"Prediction\", res, input_original) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  }
- ],
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-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
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15. Transfer Learning & Fine Tuning/15.3 Making a Flower Classifier with VGG16.ipynb

@@ -1,693 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Making a Flower Classifier with VGG16\n",
-    "\n",
-    "### Loading the VGG16 Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.applications import VGG16\n",
-    "\n",
-    "# VGG16 was designed to work on 224 x 224 pixel input images sizes\n",
-    "img_rows = 224\n",
-    "img_cols = 224 \n",
-    "\n",
-    "#Loads the VGG16 model \n",
-    "vgg16 = VGG16(weights = 'imagenet', \n",
-    "                 include_top = False, \n",
-    "                 input_shape = (img_rows, img_cols, 3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Inpsecting each layer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 InputLayer True\n",
-      "1 Conv2D True\n",
-      "2 Conv2D True\n",
-      "3 MaxPooling2D True\n",
-      "4 Conv2D True\n",
-      "5 Conv2D True\n",
-      "6 MaxPooling2D True\n",
-      "7 Conv2D True\n",
-      "8 Conv2D True\n",
-      "9 Conv2D True\n",
-      "10 MaxPooling2D True\n",
-      "11 Conv2D True\n",
-      "12 Conv2D True\n",
-      "13 Conv2D True\n",
-      "14 MaxPooling2D True\n",
-      "15 Conv2D True\n",
-      "16 Conv2D True\n",
-      "17 Conv2D True\n",
-      "18 MaxPooling2D True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's print our layers \n",
-    "for (i,layer) in enumerate(vgg16.layers):\n",
-    "    print(str(i) + \" \"+ layer.__class__.__name__, layer.trainable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's freeze all layers except the top 4 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 InputLayer False\n",
-      "1 Conv2D False\n",
-      "2 Conv2D False\n",
-      "3 MaxPooling2D False\n",
-      "4 Conv2D False\n",
-      "5 Conv2D False\n",
-      "6 MaxPooling2D False\n",
-      "7 Conv2D False\n",
-      "8 Conv2D False\n",
-      "9 Conv2D False\n",
-      "10 MaxPooling2D False\n",
-      "11 Conv2D False\n",
-      "12 Conv2D False\n",
-      "13 Conv2D False\n",
-      "14 MaxPooling2D False\n",
-      "15 Conv2D False\n",
-      "16 Conv2D False\n",
-      "17 Conv2D False\n",
-      "18 MaxPooling2D False\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.applications import VGG16\n",
-    "\n",
-    "# VGG16 was designed to work on 224 x 224 pixel input images sizes\n",
-    "img_rows = 224\n",
-    "img_cols = 224 \n",
-    "\n",
-    "# Re-loads the VGG16 model without the top or FC layers\n",
-    "vgg16 = VGG16(weights = 'imagenet', \n",
-    "                 include_top = False, \n",
-    "                 input_shape = (img_rows, img_cols, 3))\n",
-    "\n",
-    "# Here we freeze the last 4 layers \n",
-    "# Layers are set to trainable as True by default\n",
-    "for layer in vgg16.layers:\n",
-    "    layer.trainable = False\n",
-    "    \n",
-    "# Let's print our layers \n",
-    "for (i,layer) in enumerate(vgg16.layers):\n",
-    "    print(str(i) + \" \"+ layer.__class__.__name__, layer.trainable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's make a function that returns our FC Head"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def addTopModel(bottom_model, num_classes, D=256):\n",
-    "    \"\"\"creates the top or head of the model that will be \n",
-    "    placed ontop of the bottom layers\"\"\"\n",
-    "    top_model = bottom_model.output\n",
-    "    top_model = Flatten(name = \"flatten\")(top_model)\n",
-    "    top_model = Dense(D, activation = \"relu\")(top_model)\n",
-    "    top_model = Dropout(0.3)(top_model)\n",
-    "    top_model = Dense(num_classes, activation = \"softmax\")(top_model)\n",
-    "    return top_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's add our FC Head back onto VGG"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"model\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "input_3 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
-      "_________________________________________________________________\n",
-      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
-      "_________________________________________________________________\n",
-      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
-      "_________________________________________________________________\n",
-      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
-      "_________________________________________________________________\n",
-      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
-      "_________________________________________________________________\n",
-      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
-      "_________________________________________________________________\n",
-      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
-      "_________________________________________________________________\n",
-      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
-      "_________________________________________________________________\n",
-      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
-      "_________________________________________________________________\n",
-      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
-      "_________________________________________________________________\n",
-      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
-      "_________________________________________________________________\n",
-      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
-      "_________________________________________________________________\n",
-      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
-      "_________________________________________________________________\n",
-      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
-      "_________________________________________________________________\n",
-      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 25088)             0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 256)               6422784   \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 256)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 17)                4369      \n",
-      "=================================================================\n",
-      "Total params: 21,141,841\n",
-      "Trainable params: 6,427,153\n",
-      "Non-trainable params: 14,714,688\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.models import Model\n",
-    "\n",
-    "num_classes = 17\n",
-    "\n",
-    "FC_Head = addTopModel(vgg16, num_classes)\n",
-    "\n",
-    "model = Model(inputs=vgg16.input, outputs=FC_Head)\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading our Flowers Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1190 images belonging to 17 classes.\n",
-      "Found 170 images belonging to 17 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "\n",
-    "train_data_dir = './17_flowers/train'\n",
-    "validation_data_dir = './17_flowers/validation'\n",
-    "\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=20,\n",
-    "      width_shift_range=0.2,\n",
-    "      height_shift_range=0.2,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "# Change the batchsize according to your system RAM\n",
-    "train_batchsize = 16\n",
-    "val_batchsize = 10\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=train_batchsize,\n",
-    "        class_mode='categorical')\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=val_batchsize,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training our top layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 74 steps, validate for 10 steps\n",
-      "73/74 [============================>.] - ETA: 4s - loss: 3.7274 - accuracy: 0.2366\n",
-      "Epoch 00001: val_loss improved from inf to 1.32238, saving model to flowers_vgg.h5\n",
-      "74/74 [==============================] - 340s 5s/step - loss: 3.7036 - accuracy: 0.2394 - val_loss: 1.3224 - val_accuracy: 0.5900\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
-    "                   \n",
-    "checkpoint = ModelCheckpoint(\"flowers_vgg.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint]\n",
-    "\n",
-    "# Note we use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = RMSprop(lr = 0.001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 1190\n",
-    "nb_validation_samples = 170\n",
-    "epochs = 5\n",
-    "batch_size = 16\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)\n",
-    "\n",
-    "model.save(\"flowers_vgg.h5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Can we speed this up?\n",
-    "#### Let's try re-sizing the image to 64 x 64"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 InputLayer False\n",
-      "1 Conv2D False\n",
-      "2 Conv2D False\n",
-      "3 MaxPooling2D False\n",
-      "4 Conv2D False\n",
-      "5 Conv2D False\n",
-      "6 MaxPooling2D False\n",
-      "7 Conv2D False\n",
-      "8 Conv2D False\n",
-      "9 Conv2D False\n",
-      "10 MaxPooling2D False\n",
-      "11 Conv2D False\n",
-      "12 Conv2D False\n",
-      "13 Conv2D False\n",
-      "14 MaxPooling2D False\n",
-      "15 Conv2D False\n",
-      "16 Conv2D False\n",
-      "17 Conv2D False\n",
-      "18 MaxPooling2D False\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.applications import VGG16\n",
-    "\n",
-    "# Setting the input size now to 64 x 64 pixel \n",
-    "img_rows = 64\n",
-    "img_cols = 64 \n",
-    "\n",
-    "# Re-loads the VGG16 model without the top or FC layers\n",
-    "vgg16 = VGG16(weights = 'imagenet', \n",
-    "                 include_top = False, \n",
-    "                 input_shape = (img_rows, img_cols, 3))\n",
-    "\n",
-    "# Here we freeze the last 4 layers \n",
-    "# Layers are set to trainable as True by default\n",
-    "for layer in vgg16.layers:\n",
-    "    layer.trainable = False\n",
-    "    \n",
-    "# Let's print our layers \n",
-    "for (i,layer) in enumerate(vgg16.layers):\n",
-    "    print(str(i) + \" \"+ layer.__class__.__name__, layer.trainable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's create our new model using an image size of 64 x 64"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 1190 images belonging to 17 classes.\n",
-      "Found 170 images belonging to 17 classes.\n",
-      "Model: \"model_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "input_5 (InputLayer)         [(None, 64, 64, 3)]       0         \n",
-      "_________________________________________________________________\n",
-      "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
-      "_________________________________________________________________\n",
-      "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
-      "_________________________________________________________________\n",
-      "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
-      "_________________________________________________________________\n",
-      "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
-      "_________________________________________________________________\n",
-      "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
-      "_________________________________________________________________\n",
-      "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
-      "_________________________________________________________________\n",
-      "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
-      "_________________________________________________________________\n",
-      "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
-      "_________________________________________________________________\n",
-      "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
-      "_________________________________________________________________\n",
-      "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
-      "_________________________________________________________________\n",
-      "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 256)               524544    \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 256)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 17)                4369      \n",
-      "=================================================================\n",
-      "Total params: 15,243,601\n",
-      "Trainable params: 528,913\n",
-      "Non-trainable params: 14,714,688\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.applications import VGG16\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "\n",
-    "train_data_dir = './17_flowers/train'\n",
-    "validation_data_dir = './17_flowers/validation'\n",
-    "\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=20,\n",
-    "      width_shift_range=0.2,\n",
-    "      height_shift_range=0.2,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "# Change the batchsize according to your system RAM\n",
-    "train_batchsize = 16\n",
-    "val_batchsize = 10\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=train_batchsize,\n",
-    "        class_mode='categorical')\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=val_batchsize,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=False)\n",
-    "\n",
-    "# Re-loads the VGG16 model without the top or FC layers\n",
-    "vgg16 = VGG16(weights = 'imagenet', \n",
-    "                 include_top = False, \n",
-    "                 input_shape = (img_rows, img_cols, 3))\n",
-    "\n",
-    "# Freeze layers\n",
-    "for layer in vgg16.layers:\n",
-    "    layer.trainable = False\n",
-    "    \n",
-    "# Number of classes in the Flowers-17 dataset\n",
-    "num_classes = 17\n",
-    "\n",
-    "FC_Head = addTopModel(vgg16, num_classes)\n",
-    "\n",
-    "model = Model(inputs=vgg16.input, outputs=FC_Head)\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training using 64 x 64 image size is MUCH faster!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 37 steps, validate for 5 steps\n",
-      "Epoch 1/5\n",
-      "36/37 [============================>.] - ETA: 0s - loss: 2.7539 - accuracy: 0.1343\n",
-      "Epoch 00001: val_loss improved from inf to 2.57583, saving model to flowers_vgg_64.h5\n",
-      "37/37 [==============================] - 18s 486ms/step - loss: 2.7530 - accuracy: 0.1375 - val_loss: 2.5758 - val_accuracy: 0.0800\n",
-      "Epoch 2/5\n",
-      "36/37 [============================>.] - ETA: 0s - loss: 2.5438 - accuracy: 0.2101\n",
-      "Epoch 00002: val_loss improved from 2.57583 to 2.45450, saving model to flowers_vgg_64.h5\n",
-      "37/37 [==============================] - 20s 537ms/step - loss: 2.5496 - accuracy: 0.2111 - val_loss: 2.4545 - val_accuracy: 0.2600\n",
-      "Epoch 3/5\n",
-      "36/37 [============================>.] - ETA: 0s - loss: 2.3475 - accuracy: 0.2934\n",
-      "Epoch 00003: val_loss improved from 2.45450 to 2.16252, saving model to flowers_vgg_64.h5\n",
-      "37/37 [==============================] - 18s 494ms/step - loss: 2.3422 - accuracy: 0.2939 - val_loss: 2.1625 - val_accuracy: 0.4400\n",
-      "Epoch 4/5\n",
-      "36/37 [============================>.] - ETA: 0s - loss: 2.2175 - accuracy: 0.3316\n",
-      "Epoch 00004: val_loss improved from 2.16252 to 2.07525, saving model to flowers_vgg_64.h5\n",
-      "37/37 [==============================] - 18s 473ms/step - loss: 2.2229 - accuracy: 0.3260 - val_loss: 2.0753 - val_accuracy: 0.4800\n",
-      "Epoch 5/5\n",
-      "36/37 [============================>.] - ETA: 0s - loss: 2.0746 - accuracy: 0.3681\n",
-      "Epoch 00005: val_loss improved from 2.07525 to 2.02786, saving model to flowers_vgg_64.h5\n",
-      "37/37 [==============================] - 16s 440ms/step - loss: 2.0630 - accuracy: 0.3694 - val_loss: 2.0279 - val_accuracy: 0.4000\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
-    "                   \n",
-    "checkpoint = ModelCheckpoint(\"flowers_vgg_64.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 5,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss',\n",
-    "                              factor = 0.2,\n",
-    "                              patience = 3,\n",
-    "                              verbose = 1,\n",
-    "                              min_delta = 0.00001)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
-    "\n",
-    "# Note we use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = RMSprop(lr = 0.0001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 1190\n",
-    "nb_validation_samples = 170\n",
-    "epochs = 5\n",
-    "batch_size = 32\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)\n",
-    "\n",
-    "model.save(\"flowers_vgg_64.h5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.2 Building an Emotion Detector with LittleVGG.ipynb

@@ -1,723 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using LittleVGG for Emotion Detection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training Emotion Detector"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 28709 images belonging to 7 classes.\n",
-      "Found 3589 images belonging to 7 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "import os\n",
-    "\n",
-    "num_classes = 7\n",
-    "img_rows, img_cols = 48, 48\n",
-    "batch_size = 16\n",
-    "\n",
-    "train_data_dir = './fer2013/train'\n",
-    "validation_data_dir = './fer2013/validation'\n",
-    "\n",
-    "# Let's use some data augmentaiton \n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=30,\n",
-    "      shear_range=0.3,\n",
-    "      zoom_range=0.3,\n",
-    "      width_shift_range=0.4,\n",
-    "      height_shift_range=0.4,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        color_mode = 'grayscale',\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        color_mode = 'grayscale',\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Our Keras Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.layers import ELU\n",
-    "from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Keras LittleVGG Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_8 (Conv2D)            (None, 48, 48, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 48, 48, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_13 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 24, 24, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_14 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_13 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_7 (Dropout)          (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 12, 12, 128)       73856     \n",
-      "_________________________________________________________________\n",
-      "activation_15 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_14 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_13 (Conv2D)           (None, 12, 12, 128)       147584    \n",
-      "_________________________________________________________________\n",
-      "activation_16 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_15 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_8 (Dropout)          (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_14 (Conv2D)           (None, 6, 6, 256)         295168    \n",
-      "_________________________________________________________________\n",
-      "activation_17 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_16 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "conv2d_15 (Conv2D)           (None, 6, 6, 256)         590080    \n",
-      "_________________________________________________________________\n",
-      "activation_18 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_17 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_7 (MaxPooling2 (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_9 (Dropout)          (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2304)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 64)                147520    \n",
-      "_________________________________________________________________\n",
-      "activation_19 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_18 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_10 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 64)                4160      \n",
-      "_________________________________________________________________\n",
-      "activation_20 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_19 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_11 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 7)                 455       \n",
-      "_________________________________________________________________\n",
-      "activation_21 (Activation)   (None, 7)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,328,167\n",
-      "Trainable params: 1,325,991\n",
-      "Non-trainable params: 2,176\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
-    "                 input_shape = (img_rows, img_cols, 1)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
-    "                 input_shape = (img_rows, img_cols, 1)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #5: first set of FC => RELU layers\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #6: second set of FC => RELU layers\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #7: softmax classifier\n",
-    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training our model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 1795 steps\n",
-      "1795/1795 [==============================] - 607s 338ms/step - loss: 2.0255 - accuracy: 0.2012\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"emotion_little_vgg.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint] #reduce_lr]\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = Adam(lr=0.001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 28273\n",
-    "nb_validation_samples = 3534\n",
-    "epochs = 5\n",
-    "\n",
-    "history = model.fit(\n",
-    "    train_generator,\n",
-    "    epochs = epochs)\n",
-    "    callbacks = callbacks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 3589 images belonging to 7 classes.\n",
-      "Confusion Matrix\n",
-      "[[  0   0   0 439   0  52   0]\n",
-      " [  0   0   0  52   0   3   0]\n",
-      " [  0   0   0 486   0  42   0]\n",
-      " [  0   0   0 790   0  89   0]\n",
-      " [  0   0   0 565   0  61   0]\n",
-      " [  0   0   0 496   0  98   0]\n",
-      " [  0   0   0 401   0  15   0]]\n",
-      "Classification Report\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "       Angry       0.00      0.00      0.00       491\n",
-      "     Disgust       0.00      0.00      0.00        55\n",
-      "        Fear       0.00      0.00      0.00       528\n",
-      "       Happy       0.24      0.90      0.38       879\n",
-      "     Neutral       0.00      0.00      0.00       626\n",
-      "         Sad       0.27      0.16      0.21       594\n",
-      "    Surprise       0.00      0.00      0.00       416\n",
-      "\n",
-      "    accuracy                           0.25      3589\n",
-      "   macro avg       0.07      0.15      0.08      3589\n",
-      "weighted avg       0.10      0.25      0.13      3589\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
-      "  'precision', 'predicted', average, warn_for)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import sklearn\n",
-    "from sklearn.metrics import classification_report, confusion_matrix\n",
-    "import numpy as np\n",
-    "\n",
-    "nb_train_samples = 28273\n",
-    "nb_validation_samples = 3534\n",
-    "\n",
-    "# We need to recreate our validation generator with shuffle = false\n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        color_mode = 'grayscale',\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=False)\n",
-    "\n",
-    "class_labels = validation_generator.class_indices\n",
-    "class_labels = {v: k for k, v in class_labels.items()}\n",
-    "classes = list(class_labels.values())\n",
-    "\n",
-    "#Confution Matrix and Classification Report\n",
-    "Y_pred = model.predict(validation_generator)\n",
-    "y_pred = np.argmax(Y_pred, axis=1)\n",
-    "\n",
-    "print('Confusion Matrix')\n",
-    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
-    "print('Classification Report')\n",
-    "target_names = list(class_labels.values())\n",
-    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))\n",
-    "\n",
-    "plt.figure(figsize=(8,8))\n",
-    "cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)\n",
-    "\n",
-    "plt.imshow(cnf_matrix, interpolation='nearest')\n",
-    "plt.colorbar()\n",
-    "tick_marks = np.arange(len(classes))\n",
-    "_ = plt.xticks(tick_marks, classes, rotation=90)\n",
-    "_ = plt.yticks(tick_marks, classes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Loading our saved model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('emotion_little_vgg.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get our class labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 3589 images belonging to 7 classes.\n",
-      "{0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Neutral', 5: 'Sad', 6: 'Surprise'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        color_mode = 'grayscale',\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=False)\n",
-    "\n",
-    "class_labels = validation_generator.class_indices\n",
-    "class_labels = {v: k for k, v in class_labels.items()}\n",
-    "classes = list(class_labels.values())\n",
-    "print(class_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's test on some of validation images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import load_model\n",
-    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import re\n",
-    "\n",
-    "def draw_test(name, pred, im, true_label):\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 300 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    cv2.putText(expanded_image, \"predited - \"+ pred, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
-    "    cv2.putText(expanded_image, \"true - \"+ true_label, (20, 120) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "\n",
-    "def getRandomImage(path, img_width, img_height):\n",
-    "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
-    "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
-    "    random_directory = np.random.randint(0,len(folders))\n",
-    "    path_class = folders[random_directory]\n",
-    "    file_path = path + path_class\n",
-    "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
-    "    random_file_index = np.random.randint(0,len(file_names))\n",
-    "    image_name = file_names[random_file_index]\n",
-    "    final_path = file_path + \"/\" + image_name\n",
-    "    return image.load_img(final_path, target_size = (img_width, img_height),grayscale=True), final_path, path_class\n",
-    "\n",
-    "# dimensions of our images\n",
-    "img_width, img_height = 48, 48\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = RMSprop(lr = 0.001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "files = []\n",
-    "predictions = []\n",
-    "true_labels = []\n",
-    "\n",
-    "# predicting images\n",
-    "for i in range(0, 10):\n",
-    "    path = './fer2013/validation/' \n",
-    "    img, final_path, true_label = getRandomImage(path, img_width, img_height)\n",
-    "    files.append(final_path)\n",
-    "    true_labels.append(true_label)\n",
-    "    x = image.img_to_array(img)\n",
-    "    x = x * 1./255\n",
-    "    x = np.expand_dims(x, axis=0)\n",
-    "    images = np.vstack([x])\n",
-    "    classes = model.predict_classes(images, batch_size = 10)\n",
-    "    predictions.append(classes)\n",
-    "    \n",
-    "for i in range(0, len(files)):\n",
-    "    image = cv2.imread((files[i]))\n",
-    "    image = cv2.resize(image, None, fx=3, fy=3, interpolation = cv2.INTER_CUBIC)\n",
-    "    draw_test(\"Prediction\", class_labels[predictions[i][0]], image, true_labels[i])\n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test on a single image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.models import load_model\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "from tensorflow.keras.preprocessing.image import img_to_array\n",
-    "\n",
-    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "def face_detector(img):\n",
-    "    # Convert image to grayscale\n",
-    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
-    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
-    "    if faces is ():\n",
-    "        return (0,0,0,0), np.zeros((48,48), np.uint8), img\n",
-    "    \n",
-    "    allfaces = []   \n",
-    "    rects = []\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
-    "        roi_gray = gray[y:y+h, x:x+w]\n",
-    "        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "        allfaces.append(roi_gray)\n",
-    "        rects.append((x,w,y,h))\n",
-    "    return rects, allfaces, img\n",
-    "\n",
-    "img = cv2.imread(\"rajeev.jpg\")\n",
-    "rects, faces, image = face_detector(img)\n",
-    "\n",
-    "i = 0\n",
-    "for face in faces:\n",
-    "    roi = face.astype(\"float\") / 255.0\n",
-    "    roi = img_to_array(roi)\n",
-    "    roi = np.expand_dims(roi, axis=0)\n",
-    "\n",
-    "    # make a prediction on the ROI, then lookup the class\n",
-    "    preds = classifier.predict(roi)[0]\n",
-    "    label = class_labels[preds.argmax()]   \n",
-    "\n",
-    "    #Overlay our detected emotion on our pic\n",
-    "    label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
-    "    i =+ 1\n",
-    "    cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
-    "    \n",
-    "cv2.imshow(\"Emotion Detector\", image)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's try this on our webcam\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from time import sleep\n",
-    "from tensorflow.keras.preprocessing.image import img_to_array\n",
-    "\n",
-    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "def face_detector(img):\n",
-    "    # Convert image to grayscale\n",
-    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
-    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
-    "    if faces is ():\n",
-    "        return (0,0,0,0), np.zeros((48,48), np.uint8), img\n",
-    "    \n",
-    "    for (x,y,w,h) in faces:\n",
-    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
-    "        roi_gray = gray[y:y+h, x:x+w]\n",
-    "\n",
-    "    try:\n",
-    "        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "    except:\n",
-    "        return (x,w,y,h), np.zeros((48,48), np.uint8), img\n",
-    "    return (x,w,y,h), roi_gray, img\n",
-    "\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "\n",
-    "    ret, frame = cap.read()\n",
-    "    rect, face, image = face_detector(frame)\n",
-    "    if np.sum([face]) != 0.0:\n",
-    "        roi = face.astype(\"float\") / 255.0\n",
-    "        roi = img_to_array(roi)\n",
-    "        roi = np.expand_dims(roi, axis=0)\n",
-    "\n",
-    "        # make a prediction on the ROI, then lookup the class\n",
-    "        preds = classifier.predict(roi)[0]\n",
-    "        label = class_labels[preds.argmax()]  \n",
-    "        label_position = (rect[0] + int((rect[1]/2)), rect[2] + 25)\n",
-    "        cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
-    "    else:\n",
-    "        cv2.putText(image, \"No Face Found\", (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
-    "        \n",
-    "    cv2.imshow('All', image)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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+size 44387

18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3A - Age, Gender Detection.ipynb

@@ -1,174 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's run our Age and Gender Detector\n",
-    "\n",
-    "- Please see https://github.com/yu4u/age-gender-estimation for source code project.\n",
-    "- In this notebook we re-use the model trained by yu4u"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading data from https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\n",
-      "195854336/195848088 [==============================] - 173s 1us/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pathlib import Path\n",
-    "import cv2\n",
-    "import dlib\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import argparse\n",
-    "from contextlib import contextmanager\n",
-    "from wide_resnet import WideResNet\n",
-    "from tensorflow.keras.utils import get_file\n",
-    "\n",
-    "# Load our cassade classifier for faces\n",
-    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# Load our pretrained model for Gender and Age Detection\n",
-    "pretrained_model = \"https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\"\n",
-    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
-    "\n",
-    "# Face Detection function\n",
-    "def face_detector(img):\n",
-    "    # Convert image to grayscale for faster detection\n",
-    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
-    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
-    "    if faces is ():\n",
-    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
-    "    \n",
-    "    allfaces = []   \n",
-    "    rects = []\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
-    "        roi = img[y:y+h, x:x+w]\n",
-    "        roi_groiray = cv2.resize(roi, (64, 64), interpolation = cv2.INTER_AREA)\n",
-    "        allfaces.append(roi)\n",
-    "        rects.append((x,w,y,h))\n",
-    "    return True, rects, allfaces, img\n",
-    "\n",
-    "# Define our model parameters\n",
-    "depth = 16\n",
-    "k = 8\n",
-    "weight_file = None\n",
-    "margin = 0.4\n",
-    "image_dir = None\n",
-    "\n",
-    "# Get our weight file \n",
-    "if not weight_file:\n",
-    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
-    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
-    "\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "model = WideResNet(img_size, depth=depth, k=k)()\n",
-    "model.load_weights(weight_file)\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    ret, rects, faces, image = face_detector(frame)\n",
-    "    preprocessed_faces = []\n",
-    "    i = 0\n",
-    "    if ret:\n",
-    "        for (i,face) in enumerate(faces):\n",
-    "            face = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
-    "            preprocessed_faces.append(face)\n",
-    "\n",
-    "        # make a prediction on the faces detected\n",
-    "        results = model.predict(np.array(preprocessed_faces))\n",
-    "        predicted_genders = results[0]\n",
-    "        ages = np.arange(0, 101).reshape(101, 1)\n",
-    "        predicted_ages = results[1].dot(ages).flatten()\n",
-    "\n",
-    "        # draw results\n",
-    "        for (i, f) in enumerate(faces):\n",
-    "            label = \"{}, {}\".format(int(predicted_ages[i]),\n",
-    "                                        \"F\" if predicted_genders[i][0] > 0.5 else \"M\")\n",
-    "\n",
-    "        #Overlay our detected emotion on our pic\n",
-    "        label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
-    "        i =+ 1\n",
-    "        cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Emotion Detector\", image)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Note if you get the following error, you need to enable your webcam \n",
-    "<img src=\"error.jpg\">\n",
-    "### Enable your webcam by doing the following:\n",
-    "<img src=\"webcam.jpg\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run these lines if your webcam fails to be realesed due to error in code\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
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+size 5569

18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3B Age, Gender with Emotion.ipynb

@@ -1,526 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Age, Gender and Emotion Detection\n",
-    "\n",
-    "### Let's load our classfiers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "import cv2\n",
-    "import dlib\n",
-    "import sys\n",
-    "import numpy as np\n",
-    "import argparse\n",
-    "from contextlib import contextmanager\n",
-    "from wide_resnet import WideResNet\n",
-    "from tensorflow.keras.utils import get_file\n",
-    "from tensorflow.keras.models import load_model\n",
-    "from tensorflow.keras.preprocessing.image import img_to_array\n",
-    "\n",
-    "classifier = load_model('emotion_little_vgg.h5')\n",
-    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "pretrained_model = \"https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our Emotion, Age and Gender Detector - Using Webcam"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
-    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
-    "\n",
-    "def face_detector(img):\n",
-    "    # Convert image to grayscale for faster detection\n",
-    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
-    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
-    "    if faces is ():\n",
-    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
-    "    \n",
-    "    allfaces = []   \n",
-    "    rects = []\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
-    "        roi = img[y:y+h, x:x+w]\n",
-    "        allfaces.append(roi)\n",
-    "        rects.append((x,w,y,h))\n",
-    "    return True, rects, allfaces, img\n",
-    "\n",
-    "# Define our model parameters\n",
-    "depth = 16\n",
-    "k = 8\n",
-    "weight_file = None\n",
-    "margin = 0.4\n",
-    "image_dir = None\n",
-    "\n",
-    "# Get our weight file \n",
-    "if not weight_file:\n",
-    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
-    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
-    "\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "model = WideResNet(img_size, depth=depth, k=k)()\n",
-    "model.load_weights(weight_file)\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    ret, rects, faces, image = face_detector(frame)\n",
-    "    preprocessed_faces_ag = []\n",
-    "    preprocessed_faces_emo = []\n",
-    "    \n",
-    "    if ret:\n",
-    "        for (i,face) in enumerate(faces):\n",
-    "            face_ag = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
-    "            preprocessed_faces_ag.append(face_ag)\n",
-    "\n",
-    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
-    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
-    "            face_gray_emo = img_to_array(face_gray_emo)\n",
-    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
-    "            preprocessed_faces_emo.append(face_gray_emo)\n",
-    "            \n",
-    "        # make a prediction for Age and Gender\n",
-    "        results = model.predict(np.array(preprocessed_faces_ag))\n",
-    "        predicted_genders = results[0]\n",
-    "        ages = np.arange(0, 101).reshape(101, 1)\n",
-    "        predicted_ages = results[1].dot(ages).flatten()\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        emo_labels = []\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
-    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results, for Age and Gender\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
-    "                                        \"F\" if predicted_genders[i][0] > 0.6 else \"M\",\n",
-    "                                        emo_labels[i])\n",
-    "            \n",
-    "        #Overlay our detected emotion on our pic\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
-    "            cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Emotion Detector\", image)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our Emotion, Age and Gender Detector - On Images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[WinError 3] The system cannot find the path specified: './images/'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-5-d6f5a6d6ebc5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mimage_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mimage_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mimage_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: './images/'"
-     ]
-    }
-   ],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
-    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
-    "\n",
-    "def face_detector(img):\n",
-    "    # Convert image to grayscale for faster detection\n",
-    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
-    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
-    "    if faces is ():\n",
-    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
-    "    \n",
-    "    allfaces = []   \n",
-    "    rects = []\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
-    "        roi = img[y:y+h, x:x+w]\n",
-    "        allfaces.append(roi)\n",
-    "        rects.append((x,w,y,h))\n",
-    "    return True, rects, allfaces, img\n",
-    "\n",
-    "# Define our model parameters\n",
-    "depth = 16\n",
-    "k = 8\n",
-    "weight_file = None\n",
-    "margin = 0.4\n",
-    "image_dir = None\n",
-    "\n",
-    "# Get our weight file \n",
-    "if not weight_file:\n",
-    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
-    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "model = WideResNet(img_size, depth=depth, k=k)()\n",
-    "model.load_weights(weight_file)\n",
-    "\n",
-    "image_names = [f for f in listdir(image_path) if isfile(join(image_path, f))]\n",
-    "\n",
-    "for image_name in image_names:\n",
-    "    frame = cv2.imread(\"./images/\" + image_name)\n",
-    "    ret, rects, faces, image = face_detector(frame)\n",
-    "    preprocessed_faces_ag = []\n",
-    "    preprocessed_faces_emo = []\n",
-    "    \n",
-    "    if ret:\n",
-    "        for (i,face) in enumerate(faces):\n",
-    "            face_ag = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
-    "            preprocessed_faces_ag.append(face_ag)\n",
-    "\n",
-    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
-    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
-    "            face_gray_emo = img_to_array(face_gray_emo)\n",
-    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
-    "            preprocessed_faces_emo.append(face_gray_emo)\n",
-    "            \n",
-    "        # make a prediction for Age and Gender\n",
-    "        results = model.predict(np.array(preprocessed_faces_ag))\n",
-    "        predicted_genders = results[0]\n",
-    "        ages = np.arange(0, 101).reshape(101, 1)\n",
-    "        predicted_ages = results[1].dot(ages).flatten()\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        emo_labels = []\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
-    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results, for Age and Gender\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
-    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\",\n",
-    "                                        emo_labels[i])\n",
-    "            \n",
-    "        #Overlay our detected emotion on our pic\n",
-    "        for (i, face) in enumerate(faces):\n",
-    "            label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
-    "            cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Emotion Detector\", image)\n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using Dlib's Face Detection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
-    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "\n",
-    "# Define our model parameters\n",
-    "depth = 16\n",
-    "k = 8\n",
-    "weight_file = None\n",
-    "margin = 0.4\n",
-    "image_dir = None\n",
-    "\n",
-    "# Get our weight file \n",
-    "if not weight_file:\n",
-    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
-    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "model = WideResNet(img_size, depth=depth, k=k)()\n",
-    "model.load_weights(weight_file)\n",
-    "\n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "image_names = [f for f in listdir(image_path) if isfile(join(image_path, f))]\n",
-    "\n",
-    "for image_name in image_names:\n",
-    "    frame = cv2.imread(\"./images/\" + image_name)\n",
-    "    preprocessed_faces_emo = []           \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
-    "    \n",
-    "    preprocessed_faces_emo = []\n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
-    "            faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
-    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
-    "            face_gray_emo = img_to_array(face_gray_emo)\n",
-    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
-    "            preprocessed_faces_emo.append(face_gray_emo)\n",
-    "\n",
-    "        # make a prediction for Age and Gender\n",
-    "        results = model.predict(np.array(faces))\n",
-    "        predicted_genders = results[0]\n",
-    "        ages = np.arange(0, 101).reshape(101, 1)\n",
-    "        predicted_ages = results[1].dot(ages).flatten()\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        emo_labels = []\n",
-    "        for i, d in enumerate(detected):\n",
-    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
-    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results\n",
-    "        for i, d in enumerate(detected):\n",
-    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
-    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\", emo_labels[i])\n",
-    "            draw_label(frame, (d.left(), d.top()), label)\n",
-    "\n",
-    "    cv2.imshow(\"Emotion Detector\", frame)\n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### And now using dlib's detector with our webcam"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
-    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "\n",
-    "# Define our model parameters\n",
-    "depth = 16\n",
-    "k = 8\n",
-    "weight_file = None\n",
-    "margin = 0.4\n",
-    "image_dir = None\n",
-    "\n",
-    "# Get our weight file \n",
-    "if not weight_file:\n",
-    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
-    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "model = WideResNet(img_size, depth=depth, k=k)()\n",
-    "model.load_weights(weight_file)\n",
-    "\n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    preprocessed_faces_emo = []           \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
-    "    \n",
-    "    preprocessed_faces_emo = []\n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
-    "            faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
-    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
-    "            face_gray_emo = img_to_array(face_gray_emo)\n",
-    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
-    "            preprocessed_faces_emo.append(face_gray_emo)\n",
-    "\n",
-    "        # make a prediction for Age and Gender\n",
-    "        results = model.predict(np.array(faces))\n",
-    "        predicted_genders = results[0]\n",
-    "        ages = np.arange(0, 101).reshape(101, 1)\n",
-    "        predicted_ages = results[1].dot(ages).flatten()\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        emo_labels = []\n",
-    "        for i, d in enumerate(detected):\n",
-    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
-    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results\n",
-    "        for i, d in enumerate(detected):\n",
-    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
-    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\", emo_labels[i])\n",
-    "            draw_label(frame, (d.left(), d.top()), label)\n",
-    "\n",
-    "    cv2.imshow(\"Emotion Detector\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
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-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
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-   "outputs": [],
-   "source": []
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-   "execution_count": null,
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-   "outputs": [],
-   "source": []
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18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Detection - Friends Characters.ipynb

@@ -1,526 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Deep Learning Face Recogntion\n",
-    "## Building a Friends TV Show Character Identifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's train our model\n",
-    "I've created a dataset with the faces of 4 Friends characters taken from a handful of different scenes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2663 images belonging to 4 classes.\n",
-      "Found 955 images belonging to 4 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import keras\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "import os\n",
-    "\n",
-    "num_classes = 4\n",
-    "img_rows, img_cols = 48, 48\n",
-    "batch_size = 16\n",
-    "\n",
-    "train_data_dir = './faces/train'\n",
-    "validation_data_dir = './faces/validation'\n",
-    "\n",
-    "# Let's use some data augmentaiton \n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=30,\n",
-    "      shear_range=0.3,\n",
-    "      zoom_range=0.3,\n",
-    "      width_shift_range=0.4,\n",
-    "      height_shift_range=0.4,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Our Keras imports\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers.normalization import BatchNormalization\n",
-    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
-    "from keras.layers.advanced_activations import ELU\n",
-    "from keras.layers.core import Activation, Flatten, Dropout, Dense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a simple VGG based model for Face Recognition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_25 (Conv2D)           (None, 48, 48, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_34 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_31 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_26 (Conv2D)           (None, 48, 48, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_35 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_32 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_13 (MaxPooling (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_19 (Dropout)         (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_27 (Conv2D)           (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_36 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_33 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_28 (Conv2D)           (None, 24, 24, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_37 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_34 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_14 (MaxPooling (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_20 (Dropout)         (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_29 (Conv2D)           (None, 12, 12, 128)       73856     \n",
-      "_________________________________________________________________\n",
-      "activation_38 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_35 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_30 (Conv2D)           (None, 12, 12, 128)       147584    \n",
-      "_________________________________________________________________\n",
-      "activation_39 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_36 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_15 (MaxPooling (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_21 (Dropout)         (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_31 (Conv2D)           (None, 6, 6, 256)         295168    \n",
-      "_________________________________________________________________\n",
-      "activation_40 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_37 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "conv2d_32 (Conv2D)           (None, 6, 6, 256)         590080    \n",
-      "_________________________________________________________________\n",
-      "activation_41 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_38 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_16 (MaxPooling (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_22 (Dropout)         (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_4 (Flatten)          (None, 2304)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_10 (Dense)             (None, 64)                147520    \n",
-      "_________________________________________________________________\n",
-      "activation_42 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_39 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_23 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_11 (Dense)             (None, 64)                4160      \n",
-      "_________________________________________________________________\n",
-      "activation_43 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_40 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_24 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_12 (Dense)             (None, 4)                 260       \n",
-      "_________________________________________________________________\n",
-      "activation_44 (Activation)   (None, 4)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,328,548\n",
-      "Trainable params: 1,326,372\n",
-      "Non-trainable params: 2,176\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #5: first set of FC => RELU layers\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #6: second set of FC => RELU layers\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #7: softmax classifier\n",
-    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training our Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "166/166 [==============================] - 76s 457ms/step - loss: 1.1153 - acc: 0.5700 - val_loss: 1.4428 - val_acc: 0.4841\n",
-      "\n",
-      "Epoch 00001: val_loss improved from inf to 1.44279, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 2/10\n",
-      "166/166 [==============================] - 67s 403ms/step - loss: 0.7034 - acc: 0.7343 - val_loss: 3.7705 - val_acc: 0.2705\n",
-      "\n",
-      "Epoch 00002: val_loss did not improve from 1.44279\n",
-      "Epoch 3/10\n",
-      "166/166 [==============================] - 62s 373ms/step - loss: 0.6037 - acc: 0.7690 - val_loss: 0.9403 - val_acc: 0.6912\n",
-      "\n",
-      "Epoch 00003: val_loss improved from 1.44279 to 0.94025, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 4/10\n",
-      "166/166 [==============================] - 62s 373ms/step - loss: 0.5432 - acc: 0.7988 - val_loss: 1.3018 - val_acc: 0.5548\n",
-      "\n",
-      "Epoch 00004: val_loss did not improve from 0.94025\n",
-      "Epoch 5/10\n",
-      "166/166 [==============================] - 69s 414ms/step - loss: 0.4715 - acc: 0.8301 - val_loss: 3.8879 - val_acc: 0.1534\n",
-      "\n",
-      "Epoch 00005: val_loss did not improve from 0.94025\n",
-      "Epoch 6/10\n",
-      "166/166 [==============================] - 77s 467ms/step - loss: 0.4233 - acc: 0.8524 - val_loss: 0.6878 - val_acc: 0.7093\n",
-      "\n",
-      "Epoch 00006: val_loss improved from 0.94025 to 0.68784, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 7/10\n",
-      "166/166 [==============================] - 71s 429ms/step - loss: 0.4130 - acc: 0.8636 - val_loss: 3.3402 - val_acc: 0.2971\n",
-      "\n",
-      "Epoch 00007: val_loss did not improve from 0.68784\n",
-      "Epoch 8/10\n",
-      "166/166 [==============================] - 79s 477ms/step - loss: 0.3821 - acc: 0.8748 - val_loss: 2.6729 - val_acc: 0.6283\n",
-      "\n",
-      "Epoch 00008: val_loss did not improve from 0.68784\n",
-      "Epoch 9/10\n",
-      "166/166 [==============================] - 86s 519ms/step - loss: 0.3622 - acc: 0.8709 - val_loss: 1.5067 - val_acc: 0.5197\n",
-      "Restoring model weights from the end of the best epoch\n",
-      "\n",
-      "Epoch 00009: val_loss did not improve from 0.68784\n",
-      "\n",
-      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
-      "Epoch 00009: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.optimizers import RMSprop, SGD, Adam\n",
-    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = Adam(lr=0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 2663\n",
-    "nb_validation_samples = 955\n",
-    "epochs = 10\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Getting our Class Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_labels = validation_generator.class_indices\n",
-    "class_labels = {v: k for k, v in class_labels.items()}\n",
-    "classes = list(class_labels.values())\n",
-    "class_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load our model\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our model on some real video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "face_classes = {0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "margin = 0.2\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "\n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
-    "    preprocessed_faces = []           \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
-    "    \n",
-    "    preprocessed_faces_emo = []\n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
-    "            #faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            face = cv2.resize(face, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face = face.astype(\"float\") / 255.0\n",
-    "            face = img_to_array(face)\n",
-    "            face = np.expand_dims(face, axis=0)\n",
-    "            preprocessed_faces.append(face)\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        face_labels = []\n",
-    "        for i, d in enumerate(detected):\n",
-    "            preds = classifier.predict(preprocessed_faces[i])[0]\n",
-    "            face_labels.append(face_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results\n",
-    "        for i, d in enumerate(detected):\n",
-    "            label = \"{}\".format(face_labels[i])\n",
-    "            draw_label(frame, (d.left(), d.top()), label)\n",
-    "\n",
-    "    cv2.imshow(\"Friend Character Identifier\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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+size 22647

18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Extraction from Video.ipynb

@@ -1,93 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extracting the faces from a video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import dlib\n",
-    "import numpy as np\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "img_size = 64\n",
-    "margin = 0.2\n",
-    "frame_count = 0\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame_count += 1\n",
-    "    print(frame_count)   \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = []\n",
-    "    \n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            file_name = \"./faces/\"+str(frame_count)+\"_\"+str(i)+\".jpg\"\n",
-    "            cv2.imwrite(file_name, face)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Face Detector\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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+size 2867

23. DeepDream & Neural Style Transfers/.~24.1 DeepDream.ipynb

@@ -1,309 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing DeepDream in Keras\n",
-    "\n",
-    "We firstly load the InceptionV3 model which tends to produce some of the best visuals. Feel free to try VGG16, VGG19, Xception and ResNet50.\n",
-    "\n",
-    "Code obtained and edited from F. Chollet (Created of Keras)\n",
-    "- \n",
-    "https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/8.2-deep-dream.ipynb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.applications import inception_v3, resnet50\n",
-    "from keras import backend as K\n",
-    "\n",
-    "# This settings disables all training specific operations\n",
-    "K.set_learning_phase(0)\n",
-    "\n",
-    "# Load InceptionV3\n",
-    "model = inception_v3.InceptionV3(weights = 'imagenet', include_top = False)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### We create a dictionary of coefficients quantifying, how much the layer’s activation contributes to the loss you’ll seek to maximize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "layer_contributions = {\n",
-    "    'mixed2': 0.7,\n",
-    "    'mixed3': 2.2,\n",
-    "    'mixed4': 1.2,\n",
-    "    'mixed5': .2,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Define our tensor that contains the maximized Loss (the weighted sum of the L2 norm of the activations of the layer desfined above)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:Variable += will be deprecated. Use variable.assign_add if you want assignment to the variable value or 'x = x + y' if you want a new python Tensor object.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Map layer names to layer instances\n",
-    "layer_dict = dict([(layer.name, layer) for layer in model.layers])\n",
-    "\n",
-    "# loss defined by adding layer contributions\n",
-    "loss = K.variable(0.)\n",
-    "\n",
-    "for layer_name in layer_contributions:\n",
-    "    coeff = layer_contributions[layer_name]\n",
-    "    \n",
-    "    # activation gets the layer output\n",
-    "    activation = layer_dict[layer_name].output\n",
-    "    scaling = K.prod(K.cast(K.shape(activation), 'float32'))\n",
-    "\n",
-    "    # we add the l2 norm\n",
-    "    loss += coeff * K.sum(K.square(activation[:, 2: -2, 2: -2, :])) / scaling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating the Gradient-Ascent Process"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this is the image or 'dream' :) that is stored in this tensor \n",
-    "dream = model.input\n",
-    "\n",
-    "# Obtains the gradients wrt to the loss\n",
-    "grads = K.gradients(loss, dream)[0]\n",
-    "\n",
-    "# Normalizes the gradient \n",
-    "grads /= K.maximum(K.mean(K.abs(grads)), 1e-7)\n",
-    "\n",
-    "# Creates a Keras function to get the value of the loss & gradients wrt to the input\n",
-    "outputs = [loss, grads]\n",
-    "fetch_loss_and_grads = K.function([dream], outputs)\n",
-    "\n",
-    "def eval_loss_and_grads(x):\n",
-    "    \"\"\"returns the loss and gradient values\"\"\"\n",
-    "    outs = fetch_loss_and_grads([x])\n",
-    "    loss_value = outs[0]\n",
-    "    grad_values = outs[1]\n",
-    "    return loss_value, grad_values\n",
-    "\n",
-    "def gradient_ascent(x, iterations, step, max_loss=None):\n",
-    "    \"\"\"Implements gradient access for a specified number of iterations\"\"\"\n",
-    "    for i in range(iterations):\n",
-    "        loss_value, grad_values = eval_loss_and_grads(x)\n",
-    "        if max_loss is not None and loss_value > max_loss:\n",
-    "            break\n",
-    "        print('...Loss value at', i, ':', loss_value)\n",
-    "        x += step * grad_values\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Implementing the Deep Dream Algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing image shape (353, 529)\n",
-      "...Loss value at 0 : 1.0304297\n",
-      "...Loss value at 1 : 1.2510272\n",
-      "...Loss value at 2 : 1.6979636\n",
-      "...Loss value at 3 : 2.271832\n",
-      "...Loss value at 4 : 2.8770106\n",
-      "...Loss value at 5 : 3.4737113\n",
-      "...Loss value at 6 : 4.0769672\n",
-      "...Loss value at 7 : 4.617928\n",
-      "...Loss value at 8 : 5.15317\n",
-      "...Loss value at 9 : 5.685014\n",
-      "...Loss value at 10 : 6.1906824\n",
-      "...Loss value at 11 : 6.645993\n",
-      "...Loss value at 12 : 7.0928683\n",
-      "...Loss value at 13 : 7.5481906\n",
-      "...Loss value at 14 : 7.9534693\n",
-      "...Loss value at 15 : 8.386046\n",
-      "...Loss value at 16 : 8.765054\n",
-      "...Loss value at 17 : 9.139767\n",
-      "...Loss value at 18 : 9.5182705\n",
-      "...Loss value at 19 : 9.86855\n",
-      "Processing image shape (494, 740)\n",
-      "...Loss value at 0 : 2.864695\n",
-      "...Loss value at 1 : 4.0144286\n",
-      "...Loss value at 2 : 4.9185443\n",
-      "...Loss value at 3 : 5.688157\n",
-      "...Loss value at 4 : 6.3830137\n",
-      "...Loss value at 5 : 7.0187526\n",
-      "...Loss value at 6 : 7.596686\n",
-      "...Loss value at 7 : 8.150836\n",
-      "...Loss value at 8 : 8.67249\n",
-      "...Loss value at 9 : 9.186928\n",
-      "...Loss value at 10 : 9.649975\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/deeplearningcv/anaconda3/envs/cv/lib/python3.6/site-packages/scipy/ndimage/interpolation.py:583: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.\n",
-      "  \"the returned array has changed.\", UserWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing image shape (692, 1037)\n",
-      "...Loss value at 0 : 2.9274173\n",
-      "...Loss value at 1 : 4.0277452\n",
-      "...Loss value at 2 : 4.9098253\n",
-      "...Loss value at 3 : 5.6878753\n",
-      "...Loss value at 4 : 6.426328\n",
-      "...Loss value at 5 : 7.1218004\n",
-      "...Loss value at 6 : 7.773466\n",
-      "...Loss value at 7 : 8.408107\n",
-      "...Loss value at 8 : 9.042133\n",
-      "...Loss value at 9 : 9.666492\n",
-      "DeepDreaming Complete\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import scipy\n",
-    "from keras.preprocessing import image\n",
-    "import imageio\n",
-    "\n",
-    "def resize_img(img, size):\n",
-    "    img = np.copy(img)\n",
-    "    factors = (1,\n",
-    "               float(size[0]) / img.shape[1],\n",
-    "               float(size[1]) / img.shape[2], 1)\n",
-    "    return scipy.ndimage.zoom(img, factors, order=1)\n",
-    "\n",
-    "def save_img(img, fname):\n",
-    "    pil_img = deprocess_image(np.copy(img))\n",
-    "    imageio.imwrite(fname, pil_img)\n",
-    "\n",
-    "def preprocess_image(image_path):\n",
-    "    img = image.load_img(image_path)\n",
-    "    img = image.img_to_array(img)\n",
-    "    img = np.expand_dims(img, axis=0)\n",
-    "    img = inception_v3.preprocess_input(img)\n",
-    "    return img\n",
-    "\n",
-    "def deprocess_image(x):\n",
-    "    if K.image_data_format() == 'channels_first':\n",
-    "        x = x.reshape((3, x.shape[2], x.shape[3]))\n",
-    "        x = x.transpose((1, 2, 0))\n",
-    "    else:\n",
-    "        x = x.reshape((x.shape[1], x.shape[2], 3))\n",
-    "    x /= 2.\n",
-    "    x += 0.5\n",
-    "    x *= 255.\n",
-    "    x = np.clip(x, 0, 255).astype('uint8')\n",
-    "    return x\n",
-    "\n",
-    "step = 0.01 #Step size for gradient ascent\n",
-    "num_octave = 3 #number of octaves to be run\n",
-    "octave_scale = 1.4 #this is the scale for each ensuing octive will be 1.4 times large than the previous\n",
-    "iterations = 20 #number of gradient ascent operations we execute \n",
-    "max_loss = 10.0 #our early stoping metric, if loss is > max_loss we break the gradient ascent loop\n",
-    "\n",
-    "base_image_path = './images/aurora_norway.jpg'\n",
-    "\n",
-    "# Load our image \n",
-    "img = preprocess_image(base_image_path)\n",
-    "\n",
-    "# Initialize a list of tuples for our different images sizes/scales \n",
-    "original_shape = img.shape[1:3]\n",
-    "successive_shapes = [original_shape]\n",
-    "for i in range(1, num_octave):\n",
-    "    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\n",
-    "    successive_shapes.append(shape)\n",
-    "\n",
-    "# Reverse list of shapes, so that they are in increasing order\n",
-    "successive_shapes = successive_shapes[::-1]\n",
-    "\n",
-    "# Resize the Numpy array of the image to our smallest scale\n",
-    "original_img = np.copy(img)\n",
-    "shrunk_original_img = resize_img(img, successive_shapes[0])\n",
-    "\n",
-    "for shape in successive_shapes:\n",
-    "    print('Processing image shape', shape)\n",
-    "    img = resize_img(img, shape)\n",
-    "    img = gradient_ascent(img,\n",
-    "                          iterations=iterations,\n",
-    "                          step=step,\n",
-    "                          max_loss=max_loss)\n",
-    "\n",
-    "    upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)\n",
-    "    same_size_original = resize_img(original_img, shape)\n",
-    "    lost_detail = same_size_original - upscaled_shrunk_original_img\n",
-    "\n",
-    "    img += lost_detail\n",
-    "    shrunk_original_img = resize_img(original_img, shape)\n",
-    "    save_img(img, fname='./deepdream_outputs/dream_at_scale_' + str(shape) + '.png')\n",
-    "    \n",
-    "save_img(img, fname='./deepdream_outputs/final_dream.png')\n",
-    "print(\"DeepDreaming Complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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24. GANS_Generative_Networks/MNIST DCGAN.ipynb

@@ -1,918 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "_jQ1tEQCxwRx"
-   },
-   "source": [
-    "##### Copyright 2019 The TensorFlow Authors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "cellView": "form",
-    "colab": {},
-    "colab_type": "code",
-    "id": "V_sgB_5dx1f1"
-   },
-   "outputs": [],
-   "source": [
-    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#\n",
-    "# https://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rF2x3qooyBTI"
-   },
-   "source": [
-    "# Deep Convolutional Generative Adversarial Network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ITZuApL56Mny"
-   },
-   "source": [
-    "This tutorial demonstrates how to generate images of handwritten digits using a [Deep Convolutional Generative Adversarial Network](https://arxiv.org/pdf/1511.06434.pdf) (DCGAN). The code is written using the [Keras Sequential API](https://www.tensorflow.org/guide/keras) with a `tf.GradientTape` training loop."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2MbKJY38Puy9"
-   },
-   "source": [
-    "## What are GANs?\n",
-    "[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A *generator* (\"the artist\") learns to create images that look real, while a *discriminator* (\"the art critic\") learns to tell real images apart from fakes.\n",
-    "\n",
-    "![A diagram of a generator and discriminator](./images/gan1.png)\n",
-    "\n",
-    "During training, the *generator* progressively becomes better at creating images that look real, while the *discriminator* becomes better at telling them apart. The process reaches equilibrium when the *discriminator* can no longer distinguish real images from fakes.\n",
-    "\n",
-    "![A second diagram of a generator and discriminator](./images/gan2.png)\n",
-    "\n",
-    "This notebook demonstrates this process on the MNIST dataset. The following animation shows a series of images produced by the *generator* as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.\n",
-    "\n",
-    "![sample output](https://tensorflow.org/images/gan/dcgan.gif)\n",
-    "\n",
-    "To learn more about GANs, we recommend MIT's [Intro to Deep Learning](http://introtodeeplearning.com/) course."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "e1_Y75QXJS6h"
-   },
-   "source": [
-    "### Import TensorFlow and other libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WZKbyU2-AiY-"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wx-zNbLqB4K8"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2.1.0'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tf.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "YzTlj4YdCip_"
-   },
-   "outputs": [],
-   "source": [
-    "# To generate GIFs\n",
-    "!pip install -q imageio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "YfIk2es3hJEd"
-   },
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import imageio\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import PIL\n",
-    "from tensorflow.keras import layers\n",
-    "import time\n",
-    "\n",
-    "from IPython import display"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "iYn4MdZnKCey"
-   },
-   "source": [
-    "### Load and prepare the dataset\n",
-    "\n",
-    "You will use the MNIST dataset to train the generator and the discriminator. The generator will generate handwritten digits resembling the MNIST data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "a4fYMGxGhrna"
-   },
-   "outputs": [],
-   "source": [
-    "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "NFC2ghIdiZYE"
-   },
-   "outputs": [],
-   "source": [
-    "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n",
-    "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "S4PIDhoDLbsZ"
-   },
-   "outputs": [],
-   "source": [
-    "BUFFER_SIZE = 60000\n",
-    "BATCH_SIZE = 256"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "-yKCCQOoJ7cn"
-   },
-   "outputs": [],
-   "source": [
-    "# Batch and shuffle the data\n",
-    "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "THY-sZMiQ4UV"
-   },
-   "source": [
-    "## Create the models\n",
-    "\n",
-    "Both the generator and discriminator are defined using the [Keras Sequential API](https://www.tensorflow.org/guide/keras#sequential_model)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "-tEyxE-GMC48"
-   },
-   "source": [
-    "### The Generator\n",
-    "\n",
-    "The generator uses `tf.keras.layers.Conv2DTranspose` (upsampling) layers to produce an image from a seed (random noise). Start with a `Dense` layer that takes this seed as input, then upsample several times until you reach the desired image size of 28x28x1. Notice the `tf.keras.layers.LeakyReLU` activation for each layer, except the output layer which uses tanh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "6bpTcDqoLWjY"
-   },
-   "outputs": [],
-   "source": [
-    "def make_generator_model():\n",
-    "    model = tf.keras.Sequential()\n",
-    "    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n",
-    "    model.add(layers.BatchNormalization())\n",
-    "    model.add(layers.LeakyReLU())\n",
-    "\n",
-    "    model.add(layers.Reshape((7, 7, 256)))\n",
-    "    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n",
-    "\n",
-    "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
-    "    assert model.output_shape == (None, 7, 7, 128)\n",
-    "    model.add(layers.BatchNormalization())\n",
-    "    model.add(layers.LeakyReLU())\n",
-    "\n",
-    "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
-    "    assert model.output_shape == (None, 14, 14, 64)\n",
-    "    model.add(layers.BatchNormalization())\n",
-    "    model.add(layers.LeakyReLU())\n",
-    "\n",
-    "    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n",
-    "    assert model.output_shape == (None, 28, 28, 1)\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "GyWgG09LCSJl"
-   },
-   "source": [
-    "Use the (as yet untrained) generator to create an image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gl7jcC7TdPTG"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x29e15c11dc8>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "generator = make_generator_model()\n",
-    "\n",
-    "noise = tf.random.normal([1, 100])\n",
-    "generated_image = generator(noise, training=False)\n",
-    "\n",
-    "plt.imshow(generated_image[0, :, :, 0], cmap='gray')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "D0IKnaCtg6WE"
-   },
-   "source": [
-    "### The Discriminator\n",
-    "\n",
-    "The discriminator is a CNN-based image classifier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dw2tPLmk2pEP"
-   },
-   "outputs": [],
-   "source": [
-    "def make_discriminator_model():\n",
-    "    model = tf.keras.Sequential()\n",
-    "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
-    "                                     input_shape=[28, 28, 1]))\n",
-    "    model.add(layers.LeakyReLU())\n",
-    "    model.add(layers.Dropout(0.3))\n",
-    "\n",
-    "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
-    "    model.add(layers.LeakyReLU())\n",
-    "    model.add(layers.Dropout(0.3))\n",
-    "\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dense(1))\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "QhPneagzCaQv"
-   },
-   "source": [
-    "Use the (as yet untrained) discriminator to classify the generated images as real or fake. The model will be trained to output positive values for real images, and negative values for fake images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gDkA05NE6QMs"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tf.Tensor([[0.00099409]], shape=(1, 1), dtype=float32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "discriminator = make_discriminator_model()\n",
-    "decision = discriminator(generated_image)\n",
-    "print (decision)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0FMYgY_mPfTi"
-   },
-   "source": [
-    "## Define the loss and optimizers\n",
-    "\n",
-    "Define loss functions and optimizers for both models.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "psQfmXxYKU3X"
-   },
-   "outputs": [],
-   "source": [
-    "# This method returns a helper function to compute cross entropy loss\n",
-    "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PKY_iPSPNWoj"
-   },
-   "source": [
-    "### Discriminator loss\n",
-    "\n",
-    "This method quantifies how well the discriminator is able to distinguish real images from fakes. It compares the discriminator's predictions on real images to an array of 1s, and the discriminator's predictions on fake (generated) images to an array of 0s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "wkMNfBWlT-PV"
-   },
-   "outputs": [],
-   "source": [
-    "def discriminator_loss(real_output, fake_output):\n",
-    "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
-    "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
-    "    total_loss = real_loss + fake_loss\n",
-    "    return total_loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Jd-3GCUEiKtv"
-   },
-   "source": [
-    "### Generator loss\n",
-    "The generator's loss quantifies how well it was able to trick the discriminator. Intuitively, if the generator is performing well, the discriminator will classify the fake images as real (or 1). Here, we will compare the discriminators decisions on the generated images to an array of 1s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "90BIcCKcDMxz"
-   },
-   "outputs": [],
-   "source": [
-    "def generator_loss(fake_output):\n",
-    "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "MgIc7i0th_Iu"
-   },
-   "source": [
-    "The discriminator and the generator optimizers are different since we will train two networks separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "iWCn_PVdEJZ7"
-   },
-   "outputs": [],
-   "source": [
-    "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
-    "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "mWtinsGDPJlV"
-   },
-   "source": [
-    "### Save checkpoints\n",
-    "This notebook also demonstrates how to save and restore models, which can be helpful in case a long running training task is interrupted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "CA1w-7s2POEy"
-   },
-   "outputs": [],
-   "source": [
-    "checkpoint_dir = './training_checkpoints'\n",
-    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
-    "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
-    "                                 discriminator_optimizer=discriminator_optimizer,\n",
-    "                                 generator=generator,\n",
-    "                                 discriminator=discriminator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Rw1fkAczTQYh"
-   },
-   "source": [
-    "## Define the training loop\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "NS2GWywBbAWo"
-   },
-   "outputs": [],
-   "source": [
-    "EPOCHS = 50\n",
-    "noise_dim = 100\n",
-    "num_examples_to_generate = 16\n",
-    "\n",
-    "# We will reuse this seed overtime (so it's easier)\n",
-    "# to visualize progress in the animated GIF)\n",
-    "seed = tf.random.normal([num_examples_to_generate, noise_dim])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jylSonrqSWfi"
-   },
-   "source": [
-    "The training loop begins with generator receiving a random seed as input. That seed is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "3t5ibNo05jCB"
-   },
-   "outputs": [],
-   "source": [
-    "# Notice the use of `tf.function`\n",
-    "# This annotation causes the function to be \"compiled\".\n",
-    "@tf.function\n",
-    "def train_step(images):\n",
-    "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
-    "\n",
-    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
-    "      generated_images = generator(noise, training=True)\n",
-    "\n",
-    "      real_output = discriminator(images, training=True)\n",
-    "      fake_output = discriminator(generated_images, training=True)\n",
-    "\n",
-    "      gen_loss = generator_loss(fake_output)\n",
-    "      disc_loss = discriminator_loss(real_output, fake_output)\n",
-    "\n",
-    "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
-    "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
-    "\n",
-    "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
-    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "2M7LmLtGEMQJ"
-   },
-   "outputs": [],
-   "source": [
-    "def train(dataset, epochs):\n",
-    "  for epoch in range(epochs):\n",
-    "    start = time.time()\n",
-    "\n",
-    "    for image_batch in dataset:\n",
-    "      train_step(image_batch)\n",
-    "\n",
-    "    # Produce images for the GIF as we go\n",
-    "    display.clear_output(wait=True)\n",
-    "    generate_and_save_images(generator,\n",
-    "                             epoch + 1,\n",
-    "                             seed)\n",
-    "\n",
-    "    # Save the model every 15 epochs\n",
-    "    if (epoch + 1) % 15 == 0:\n",
-    "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
-    "\n",
-    "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
-    "\n",
-    "  # Generate after the final epoch\n",
-    "  display.clear_output(wait=True)\n",
-    "  generate_and_save_images(generator,\n",
-    "                           epochs,\n",
-    "                           seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2aFF7Hk3XdeW"
-   },
-   "source": [
-    "**Generate and save images**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "RmdVsmvhPxyy"
-   },
-   "outputs": [],
-   "source": [
-    "def generate_and_save_images(model, epoch, test_input):\n",
-    "  # Notice `training` is set to False.\n",
-    "  # This is so all layers run in inference mode (batchnorm).\n",
-    "  predictions = model(test_input, training=False)\n",
-    "\n",
-    "  fig = plt.figure(figsize=(4,4))\n",
-    "\n",
-    "  for i in range(predictions.shape[0]):\n",
-    "      plt.subplot(4, 4, i+1)\n",
-    "      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
-    "      plt.axis('off')\n",
-    "\n",
-    "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
-    "  plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "dZrd4CdjR-Fp"
-   },
-   "source": [
-    "## Train the model\n",
-    "Call the `train()` method defined above to train the generator and discriminator simultaneously. Note, training GANs can be tricky. It's important that the generator and discriminator do not overpower each other (e.g., that they train at a similar rate).\n",
-    "\n",
-    "At the beginning of the training, the generated images look like random noise. As training progresses, the generated digits will look increasingly real. After about 50 epochs, they resemble MNIST digits. This may take about one minute / epoch with the default settings on Colab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Ly3UN0SLLY2l"
-   },
-   "outputs": [],
-   "source": [
-    "train(train_dataset, EPOCHS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rfM4YcPVPkNO"
-   },
-   "source": [
-    "Restore the latest checkpoint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "XhXsd0srPo8c"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f89c41bfba8>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "P4M_vIbUi7c0"
-   },
-   "source": [
-    "## Create a GIF\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "WfO5wCdclHGL"
-   },
-   "outputs": [],
-   "source": [
-    "# Display a single image using the epoch number\n",
-    "def display_image(epoch_no):\n",
-    "  return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "5x3q9_Oe5q0A"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=288x288 at 0x7F8AECFBC7F0>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "display_image(EPOCHS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "NywiH3nL8guF"
-   },
-   "source": [
-    "Use `imageio` to create an animated gif using the images saved during training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "IGKQgENQ8lEI"
-   },
-   "outputs": [],
-   "source": [
-    "anim_file = 'dcgan.gif'\n",
-    "\n",
-    "with imageio.get_writer(anim_file, mode='I') as writer:\n",
-    "  filenames = glob.glob('image*.png')\n",
-    "  filenames = sorted(filenames)\n",
-    "  last = -1\n",
-    "  for i,filename in enumerate(filenames):\n",
-    "    frame = 2*(i**0.5)\n",
-    "    if round(frame) > round(last):\n",
-    "      last = frame\n",
-    "    else:\n",
-    "      continue\n",
-    "    image = imageio.imread(filename)\n",
-    "    writer.append_data(image)\n",
-    "  image = imageio.imread(filename)\n",
-    "  writer.append_data(image)\n",
-    "\n",
-    "import IPython\n",
-    "if IPython.version_info > (6,2,0,''):\n",
-    "  display.Image(filename=anim_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "cGhC3-fMWSwl"
-   },
-   "source": [
-    "If you're working in Colab you can download the animation with the code below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uV0yiKpzNP1b"
-   },
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "  from google.colab import files\n",
-    "except ImportError:\n",
-    "   pass\n",
-    "else:\n",
-    "  files.download(anim_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "k6qC-SbjK0yW"
-   },
-   "source": [
-    "## Next steps\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xjjkT9KAK6H7"
-   },
-   "source": [
-    "This tutorial has shown the complete code necessary to write and train a GAN. As a next step, you might like to experiment with a different dataset, for example the Large-scale Celeb Faces Attributes (CelebA) dataset [available on Kaggle](https://www.kaggle.com/jessicali9530/celeba-dataset). To learn more about GANs we recommend the [NIPS 2016 Tutorial: Generative Adversarial Networks](https://arxiv.org/abs/1701.00160).\n"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "name": "dcgan.ipynb",
-   "private_outputs": true,
-   "provenance": [],
-   "toc_visible": true
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:759d8be78ea02a4d58803b933fa469d1735368195f60434e29491001b0aee55c
+size 70339

25. Face Recognition/.ipynb_checkpoints/25.0 Face Extraction from Video - Build Dataset-checkpoint.ipynb

@@ -1,93 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extracting the faces from a video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import dlib\n",
-    "import numpy as np\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "img_size = 64\n",
-    "margin = 0.2\n",
-    "frame_count = 0\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame_count += 1\n",
-    "    print(frame_count)   \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = []\n",
-    "    \n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            file_name = \"./faces/\"+str(frame_count)+\"_\"+str(i)+\".jpg\"\n",
-    "            cv2.imwrite(file_name, face)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Face Detector\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b36b2231a5feaa366136c6aa0dc9026c9ec3b2b83bce6e52eaa64a462fc9786
+size 2867

25. Face Recognition/.ipynb_checkpoints/25.1 Face Recognition - Friends Characters - Train and Test-checkpoint.ipynb

@@ -1,536 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Deep Learning Face Recogntion\n",
-    "## Building a Friends TV Show Character Identifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The learning objective of this lesson (25.1) is the create a 'dumb' face classifer using our LittleVGG model. We are simply training it with 100s of pictures of each Friends Character, and testing our model using a Test Video. \n",
-    "\n",
-    "## You will see how this is an in-effective way to do Face Recognition, why?\n",
-    "## Because a traditional N"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's train our model\n",
-    "I've created a dataset with the faces of 4 Friends characters taken from a handful of different scenes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2663 images belonging to 4 classes.\n",
-      "Found 955 images belonging to 4 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import keras\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "import os\n",
-    "\n",
-    "num_classes = 4\n",
-    "img_rows, img_cols = 48, 48\n",
-    "batch_size = 16\n",
-    "\n",
-    "train_data_dir = './faces/train'\n",
-    "validation_data_dir = './faces/validation'\n",
-    "\n",
-    "# Let's use some data augmentaiton \n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=30,\n",
-    "      shear_range=0.3,\n",
-    "      zoom_range=0.3,\n",
-    "      width_shift_range=0.4,\n",
-    "      height_shift_range=0.4,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Our Keras imports\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers.normalization import BatchNormalization\n",
-    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
-    "from keras.layers.advanced_activations import ELU\n",
-    "from keras.layers.core import Activation, Flatten, Dropout, Dense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a simple VGG based model for Face Recognition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_25 (Conv2D)           (None, 48, 48, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_34 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_31 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_26 (Conv2D)           (None, 48, 48, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_35 (Activation)   (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_32 (Batc (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_13 (MaxPooling (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_19 (Dropout)         (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_27 (Conv2D)           (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_36 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_33 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_28 (Conv2D)           (None, 24, 24, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_37 (Activation)   (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_34 (Batc (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_14 (MaxPooling (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_20 (Dropout)         (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_29 (Conv2D)           (None, 12, 12, 128)       73856     \n",
-      "_________________________________________________________________\n",
-      "activation_38 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_35 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_30 (Conv2D)           (None, 12, 12, 128)       147584    \n",
-      "_________________________________________________________________\n",
-      "activation_39 (Activation)   (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_36 (Batc (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_15 (MaxPooling (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_21 (Dropout)         (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_31 (Conv2D)           (None, 6, 6, 256)         295168    \n",
-      "_________________________________________________________________\n",
-      "activation_40 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_37 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "conv2d_32 (Conv2D)           (None, 6, 6, 256)         590080    \n",
-      "_________________________________________________________________\n",
-      "activation_41 (Activation)   (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_38 (Batc (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_16 (MaxPooling (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_22 (Dropout)         (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_4 (Flatten)          (None, 2304)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_10 (Dense)             (None, 64)                147520    \n",
-      "_________________________________________________________________\n",
-      "activation_42 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_39 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_23 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_11 (Dense)             (None, 64)                4160      \n",
-      "_________________________________________________________________\n",
-      "activation_43 (Activation)   (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_40 (Batc (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_24 (Dropout)         (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_12 (Dense)             (None, 4)                 260       \n",
-      "_________________________________________________________________\n",
-      "activation_44 (Activation)   (None, 4)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,328,548\n",
-      "Trainable params: 1,326,372\n",
-      "Non-trainable params: 2,176\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #5: first set of FC => RELU layers\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #6: second set of FC => RELU layers\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #7: softmax classifier\n",
-    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training our Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "166/166 [==============================] - 76s 457ms/step - loss: 1.1153 - acc: 0.5700 - val_loss: 1.4428 - val_acc: 0.4841\n",
-      "\n",
-      "Epoch 00001: val_loss improved from inf to 1.44279, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 2/10\n",
-      "166/166 [==============================] - 67s 403ms/step - loss: 0.7034 - acc: 0.7343 - val_loss: 3.7705 - val_acc: 0.2705\n",
-      "\n",
-      "Epoch 00002: val_loss did not improve from 1.44279\n",
-      "Epoch 3/10\n",
-      "166/166 [==============================] - 62s 373ms/step - loss: 0.6037 - acc: 0.7690 - val_loss: 0.9403 - val_acc: 0.6912\n",
-      "\n",
-      "Epoch 00003: val_loss improved from 1.44279 to 0.94025, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 4/10\n",
-      "166/166 [==============================] - 62s 373ms/step - loss: 0.5432 - acc: 0.7988 - val_loss: 1.3018 - val_acc: 0.5548\n",
-      "\n",
-      "Epoch 00004: val_loss did not improve from 0.94025\n",
-      "Epoch 5/10\n",
-      "166/166 [==============================] - 69s 414ms/step - loss: 0.4715 - acc: 0.8301 - val_loss: 3.8879 - val_acc: 0.1534\n",
-      "\n",
-      "Epoch 00005: val_loss did not improve from 0.94025\n",
-      "Epoch 6/10\n",
-      "166/166 [==============================] - 77s 467ms/step - loss: 0.4233 - acc: 0.8524 - val_loss: 0.6878 - val_acc: 0.7093\n",
-      "\n",
-      "Epoch 00006: val_loss improved from 0.94025 to 0.68784, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
-      "Epoch 7/10\n",
-      "166/166 [==============================] - 71s 429ms/step - loss: 0.4130 - acc: 0.8636 - val_loss: 3.3402 - val_acc: 0.2971\n",
-      "\n",
-      "Epoch 00007: val_loss did not improve from 0.68784\n",
-      "Epoch 8/10\n",
-      "166/166 [==============================] - 79s 477ms/step - loss: 0.3821 - acc: 0.8748 - val_loss: 2.6729 - val_acc: 0.6283\n",
-      "\n",
-      "Epoch 00008: val_loss did not improve from 0.68784\n",
-      "Epoch 9/10\n",
-      "166/166 [==============================] - 86s 519ms/step - loss: 0.3622 - acc: 0.8709 - val_loss: 1.5067 - val_acc: 0.5197\n",
-      "Restoring model weights from the end of the best epoch\n",
-      "\n",
-      "Epoch 00009: val_loss did not improve from 0.68784\n",
-      "\n",
-      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
-      "Epoch 00009: early stopping\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.optimizers import RMSprop, SGD, Adam\n",
-    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = Adam(lr=0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 2663\n",
-    "nb_validation_samples = 955\n",
-    "epochs = 10\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Getting our Class Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_labels = validation_generator.class_indices\n",
-    "class_labels = {v: k for k, v in class_labels.items()}\n",
-    "classes = list(class_labels.values())\n",
-    "class_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load our model\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our model on some real video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "face_classes = {0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "margin = 0.2\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "\n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
-    "    preprocessed_faces = []           \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
-    "    \n",
-    "    preprocessed_faces_emo = []\n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
-    "            #faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            face = cv2.resize(face, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face = face.astype(\"float\") / 255.0\n",
-    "            face = img_to_array(face)\n",
-    "            face = np.expand_dims(face, axis=0)\n",
-    "            preprocessed_faces.append(face)\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        face_labels = []\n",
-    "        for i, d in enumerate(detected):\n",
-    "            preds = classifier.predict(preprocessed_faces[i])[0]\n",
-    "            face_labels.append(face_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results\n",
-    "        for i, d in enumerate(detected):\n",
-    "            label = \"{}\".format(face_labels[i])\n",
-    "            draw_label(frame, (d.left(), d.top()), label)\n",
-    "\n",
-    "    cv2.imshow(\"Friend Character Identifier\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
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25. Face Recognition/.ipynb_checkpoints/25.3 Face Recogition - One Shot Learning-checkpoint.ipynb

@@ -1,406 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Extract faces from pictures of people \n",
-    "### Instrutions:\n",
-    "- Place photos of people (one face visible) in the folder called \"./people\"\n",
-    "- Replace my photo titled \"Rajeev.jpg\" with a piture of your face for testing on a webcam\n",
-    "- Faces are extracted using the haarcascade_frontalface_default detector model\n",
-    "- Extracted faces are placed in the folder called \"./group_of_faces\"\n",
-    "#### We are extracting the faces needed for our one-shot learning model, it will load 5 extracted faces"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collected image names\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The code below extracts faces from images and places them in the folder\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import cv2\n",
-    "\n",
-    "# Loading out HAARCascade Face Detector \n",
-    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# Directory of image of persons we'll be extracting faces frommy\n",
-    "mypath = \"./people/\"\n",
-    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "print(\"Collected image names\")\n",
-    "\n",
-    "for image_name in image_file_names:\n",
-    "    person_image = cv2.imread(mypath+image_name)\n",
-    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
-    "    for (x,y,w,h) in face_info:\n",
-    "        face = person_image[y:y+h, x:x+w]\n",
-    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
-    "    path = \"./group_of_faces/\" + \"face_\" + image_name \n",
-    "    cv2.imwrite(path, roi)\n",
-    "    cv2.imshow(\"face\", roi)\n",
-    "    \n",
-    "    cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Load our VGGFaceModel \n",
-    "- This block of code defines the VGGFace model (which we use later) and loads the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model Loaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "#author Sefik Ilkin Serengil\n",
-    "#you can find the documentation of this code from the following link: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
-    "\n",
-    "import numpy as np\n",
-    "import cv2\n",
-    "\n",
-    "from tensorflow.keras.models import Model, Sequential\n",
-    "from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation\n",
-    "from PIL import Image\n",
-    "from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array\n",
-    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "import matplotlib.pyplot as plt\n",
-    "from os import listdir\n",
-    "\n",
-    "def preprocess_image(image_path):\n",
-    "    \"\"\"Loads image from path and resizes it\"\"\"\n",
-    "    img = load_img(image_path, target_size=(224, 224))\n",
-    "    img = img_to_array(img)\n",
-    "    img = np.expand_dims(img, axis=0)\n",
-    "    img = preprocess_input(img)\n",
-    "    return img\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
-    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Convolution2D(2622, (1, 1)))\n",
-    "model.add(Flatten())\n",
-    "model.add(Activation('softmax'))\n",
-    "\n",
-    "#you can download pretrained weights from https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
-    "from tensorflow.keras.models import model_from_json\n",
-    "model.load_weights('vgg_face_weights.h5')\n",
-    "\n",
-    "vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)\n",
-    "\n",
-    "model = vgg_face_descriptor\n",
-    "\n",
-    " \n",
-    "print(\"Model Loaded\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Test model using your Webcam\n",
-    "This code looks up the faces you extracted in the \"group_of_faces\" folder and uses the similarity (Cosine Similarity) to detect which faces is most similar to the one being extracted with your webcam."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Face representations retrieved successfully\n"
-     ]
-    }
-   ],
-   "source": [
-    "#points to your extracted faces\n",
-    "people_pictures = \"./group_of_faces/\"\n",
-    "\n",
-    "all_people_faces = dict()\n",
-    "\n",
-    "for file in listdir(people_pictures):\n",
-    "    person_face, extension = file.split(\".\")\n",
-    "    all_people_faces[person_face] = model.predict(preprocess_image('./group_of_faces/%s.jpg' % (person_face)))[0,:]\n",
-    "\n",
-    "print(\"Face representations retrieved successfully\")\n",
-    "\n",
-    "def findCosineSimilarity(source_representation, test_representation):\n",
-    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
-    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
-    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
-    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
-    "\n",
-    "#Open Webcam\n",
-    "cap = cv2.VideoCapture(0) \n",
-    "\n",
-    "while(True):\n",
-    "    ret, img = cap.read()\n",
-    "    faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
-    "\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        if w > 100: #Adjust accordingly if your webcam resoluation is higher\n",
-    "            cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
-    "            detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
-    "            detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
-    "\n",
-    "            img_pixels = image.img_to_array(detected_face)\n",
-    "            img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
-    "            img_pixels /= 255\n",
-    "\n",
-    "            captured_representation = model.predict(img_pixels)[0,:]\n",
-    "\n",
-    "            found = 0\n",
-    "            for i in all_people_faces:\n",
-    "                person_name = i\n",
-    "                representation = all_people_faces[i]\n",
-    "\n",
-    "                similarity = findCosineSimilarity(representation, captured_representation)\n",
-    "                if(similarity < 0.30):\n",
-    "                    cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
-    "                    found = 1\n",
-    "                    break\n",
-    "\n",
-    "            #connect face and text\n",
-    "            cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
-    "            cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
-    "\n",
-    "            if(found == 0): #if found image is not in our people database\n",
-    "                cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
-    "\n",
-    "    cv2.imshow('img',img)\n",
-    "\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test on a video\n",
-    "### Since we're using the Friends TV Series characters, let's extract the faces from the images I placed in the \"./friends\" folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collected image names\n"
-     ]
-    }
-   ],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import cv2\n",
-    "\n",
-    "# Loading out HAARCascade Face Detector \n",
-    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# Directory of image of persons we'll be extracting faces frommy\n",
-    "mypath = \"./friends/\"\n",
-    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "print(\"Collected image names\")\n",
-    "\n",
-    "for image_name in image_file_names:\n",
-    "    person_image = cv2.imread(mypath+image_name)\n",
-    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
-    "    for (x,y,w,h) in face_info:\n",
-    "        face = person_image[y:y+h, x:x+w]\n",
-    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
-    "    path = \"./friends_faces/\" + \"face_\" + image_name \n",
-    "    cv2.imwrite(path, roi)\n",
-    "    cv2.imshow(\"face\", roi)\n",
-    "    \n",
-    "    cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Again, we load our faces from the \"friends_faces\" directory and we run our face classifier model our test video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Face representations retrieved successfully\n"
-     ]
-    }
-   ],
-   "source": [
-    "#points to your extracted faces\n",
-    "people_pictures = \"./friends_faces/\"\n",
-    "\n",
-    "all_people_faces = dict()\n",
-    "\n",
-    "for file in listdir(people_pictures):\n",
-    "    person_face, extension = file.split(\".\")\n",
-    "    all_people_faces[person_face] = model.predict(preprocess_image('./friends_faces/%s.jpg' % (person_face)))[0,:]\n",
-    "\n",
-    "print(\"Face representations retrieved successfully\")\n",
-    "\n",
-    "def findCosineSimilarity(source_representation, test_representation):\n",
-    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
-    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
-    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
-    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
-    "\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "\n",
-    "while(True):\n",
-    "        ret, img = cap.read()\n",
-    "        img = cv2.resize(img, (320, 180)) # Re-size video to as smaller size to improve face detection speed\n",
-    "        faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
-    "\n",
-    "        for (x,y,w,h) in faces:\n",
-    "            if w > 13: \n",
-    "                cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
-    "\n",
-    "                detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
-    "                detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
-    "\n",
-    "                img_pixels = image.img_to_array(detected_face)\n",
-    "                img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
-    "                img_pixels /= 255\n",
-    "\n",
-    "                captured_representation = model.predict(img_pixels)[0,:]\n",
-    "\n",
-    "                found = 0\n",
-    "                for i in all_people_faces:\n",
-    "                    person_name = i\n",
-    "                    representation = all_people_faces[i]\n",
-    "\n",
-    "                    similarity = findCosineSimilarity(representation, captured_representation)\n",
-    "                    if(similarity < 0.30):\n",
-    "                        cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
-    "                        found = 1\n",
-    "                        break\n",
-    "\n",
-    "                #connect face and text\n",
-    "                cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
-    "                cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
-    "\n",
-    "                if(found == 0): #if found image is not in our people database\n",
-    "                    cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
-    "\n",
-    "        cv2.imshow('img',img)\n",
-    "        if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "            break\n",
-    "\n",
-    "#kill open cv things\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": null,
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-  }
- },
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- "nbformat_minor": 2
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25. Face Recognition/.ipynb_checkpoints/Face Recogition - Matching Faces-checkpoint.ipynb

@@ -1,6 +1,3 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
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25. Face Recognition/25.0 Face Extraction from Video - Build Dataset.ipynb

@@ -1,93 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extracting the faces from a video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import dlib\n",
-    "import numpy as np\n",
-    "\n",
-    "# Define Image Path Here\n",
-    "image_path = \"./images/\"\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "# Initialize Webcam\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "img_size = 64\n",
-    "margin = 0.2\n",
-    "frame_count = 0\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame_count += 1\n",
-    "    print(frame_count)   \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = []\n",
-    "    \n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            file_name = \"./faces/\"+str(frame_count)+\"_\"+str(i)+\".jpg\"\n",
-    "            cv2.imwrite(file_name, face)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "\n",
-    "    cv2.imshow(\"Face Detector\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  }
- ],
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-   "mimetype": "text/x-python",
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-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
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25. Face Recognition/25.1 Face Recognition - Friends Characters - Train and Test.ipynb

@@ -1,521 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Deep Learning Face Recogntion\n",
-    "## Building a Friends TV Show Character Identifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The learning objective of this lesson (25.1) is the create a 'dumb' face classifer using our LittleVGG model. We are simply training it with 100s of pictures of each Friends Character, and testing our model using a Test Video. \n",
-    "\n",
-    "## You will see how this is an in-effective way to do Face Recognition, why?\n",
-    "Because a traditional CNN will be looking at small subsections of a face to identify it. However, the angle and deformation of a face can easily throw off our model, making it mis-classify and match it to faces that aren't correct - this will happen a lot especially when our training data looks different to our test data. Imagine if the training data, one face was titled downward mostly. Our CNN will more likely identify any future face on our test data that looks downward, to be that face."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's train our model\n",
-    "I've created a dataset with the faces of 4 Friends characters taken from a handful of different scenes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 2663 images belonging to 4 classes.\n",
-      "Found 955 images belonging to 4 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import tensorflow as tf \n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "import os\n",
-    "\n",
-    "num_classes = 4\n",
-    "img_rows, img_cols = 48, 48\n",
-    "batch_size = 16\n",
-    "\n",
-    "train_data_dir = './faces/train'\n",
-    "validation_data_dir = './faces/validation'\n",
-    "\n",
-    "# Let's use some data augmentaiton \n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale=1./255,\n",
-    "      rotation_range=30,\n",
-    "      shear_range=0.3,\n",
-    "      zoom_range=0.3,\n",
-    "      width_shift_range=0.4,\n",
-    "      height_shift_range=0.4,\n",
-    "      horizontal_flip=True,\n",
-    "      fill_mode='nearest')\n",
-    " \n",
-    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
-    " \n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)\n",
-    " \n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size=(img_rows, img_cols),\n",
-    "        batch_size=batch_size,\n",
-    "        class_mode='categorical',\n",
-    "        shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Our Keras imports\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import BatchNormalization\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.layers import ELU\n",
-    "from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating a simple VGG based model for Face Recognition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d (Conv2D)              (None, 48, 48, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation (Activation)      (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization (BatchNo (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_1 (Conv2D)            (None, 48, 48, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 48, 48, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 48, 48, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d (MaxPooling2D) (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout (Dropout)            (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 24, 24, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 24, 24, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 24, 24, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 12, 12, 128)       73856     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 12, 12, 128)       147584    \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 12, 12, 128)       0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 12, 12, 128)       512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 6, 6, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 6, 6, 256)         295168    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "conv2d_7 (Conv2D)            (None, 6, 6, 256)         590080    \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 6, 6, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 6, 6, 256)         1024      \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 3, 3, 256)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten (Flatten)            (None, 2304)              0         \n",
-      "_________________________________________________________________\n",
-      "dense (Dense)                (None, 64)                147520    \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 64)                4160      \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 64)                256       \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 64)                0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 4)                 260       \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 4)                 0         \n",
-      "=================================================================\n",
-      "Total params: 1,328,548\n",
-      "Trainable params: 1,326,372\n",
-      "Non-trainable params: 2,176\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
-    "                 input_shape = (img_rows, img_cols, 3)))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
-    "# layer set\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.2))\n",
-    "\n",
-    "# Block #5: first set of FC => RELU layers\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #6: second set of FC => RELU layers\n",
-    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation('elu'))\n",
-    "model.add(BatchNormalization())\n",
-    "model.add(Dropout(0.5))\n",
-    "\n",
-    "# Block #7: softmax classifier\n",
-    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training our Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 166 steps, validate for 59 steps\n",
-      "165/166 [============================>.] - ETA: 0s - loss: 0.8249 - accuracy: 0.6743\n",
-      "Epoch 00001: val_loss improved from inf to 2.41761, saving model to face_recognition_friends_vgg.h5\n",
-      "166/166 [==============================] - 73s 437ms/step - loss: 0.8229 - accuracy: 0.6747 - val_loss: 2.4176 - val_accuracy: 0.3623\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
-    "\n",
-    "                     \n",
-    "checkpoint = ModelCheckpoint(\"face_recognition_friends_vgg.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
-    "\n",
-    "# We use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = Adam(lr=0.01),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 2663\n",
-    "nb_validation_samples = 955\n",
-    "epochs = 1\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Getting our Class Labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_labels = validation_generator.class_indices\n",
-    "class_labels = {v: k for k, v in class_labels.items()}\n",
-    "classes = list(class_labels.values())\n",
-    "class_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load our model\n",
-    "from tensorflow.keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('face_recognition_friends_vgg.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our model on some real video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'dlib' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-d316c51cf110>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mimg_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mdetector\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_frontal_face_detector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mcap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVideoCapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'testfriends.mp4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mNameError\u001b[0m: name 'dlib' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import os\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "face_classes = {0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}\n",
-    "\n",
-    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
-    "               font_scale=0.8, thickness=1):\n",
-    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
-    "    x, y = point\n",
-    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
-    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
-    "    \n",
-    "margin = 0.2\n",
-    "# load model and weights\n",
-    "img_size = 64\n",
-    "\n",
-    "detector = dlib.get_frontal_face_detector()\n",
-    "\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
-    "    preprocessed_faces = []           \n",
-    " \n",
-    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
-    "    img_h, img_w, _ = np.shape(input_img)\n",
-    "    detected = detector(frame, 1)\n",
-    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
-    "    \n",
-    "    preprocessed_faces_emo = []\n",
-    "    if len(detected) > 0:\n",
-    "        for i, d in enumerate(detected):\n",
-    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
-    "            xw1 = max(int(x1 - margin * w), 0)\n",
-    "            yw1 = max(int(y1 - margin * h), 0)\n",
-    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
-    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
-    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
-    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
-    "            #faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
-    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
-    "            face = cv2.resize(face, (48, 48), interpolation = cv2.INTER_AREA)\n",
-    "            face = face.astype(\"float\") / 255.0\n",
-    "            face = img_to_array(face)\n",
-    "            face = np.expand_dims(face, axis=0)\n",
-    "            preprocessed_faces.append(face)\n",
-    "\n",
-    "        # make a prediction for Emotion \n",
-    "        face_labels = []\n",
-    "        for i, d in enumerate(detected):\n",
-    "            preds = classifier.predict(preprocessed_faces[i])[0]\n",
-    "            face_labels.append(face_classes[preds.argmax()])\n",
-    "        \n",
-    "        # draw results\n",
-    "        for i, d in enumerate(detected):\n",
-    "            label = \"{}\".format(face_labels[i])\n",
-    "            draw_label(frame, (d.left(), d.top()), label)\n",
-    "\n",
-    "    cv2.imshow(\"Friend Character Identifier\", frame)\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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+size 23012

25. Face Recognition/25.3 Face Recogition - One Shot Learning.ipynb

@@ -1,406 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Extract faces from pictures of people \n",
-    "### Instrutions:\n",
-    "- Place photos of people (one face visible) in the folder called \"./people\"\n",
-    "- Replace my photo titled \"Rajeev.jpg\" with a piture of your face for testing on a webcam\n",
-    "- Faces are extracted using the haarcascade_frontalface_default detector model\n",
-    "- Extracted faces are placed in the folder called \"./group_of_faces\"\n",
-    "#### We are extracting the faces needed for our one-shot learning model, it will load 5 extracted faces"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collected image names\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The code below extracts faces from images and places them in the folder\n",
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import cv2\n",
-    "\n",
-    "# Loading out HAARCascade Face Detector \n",
-    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# Directory of image of persons we'll be extracting faces frommy\n",
-    "mypath = \"./people/\"\n",
-    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "print(\"Collected image names\")\n",
-    "\n",
-    "for image_name in image_file_names:\n",
-    "    person_image = cv2.imread(mypath+image_name)\n",
-    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
-    "    for (x,y,w,h) in face_info:\n",
-    "        face = person_image[y:y+h, x:x+w]\n",
-    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
-    "    path = \"./group_of_faces/\" + \"face_\" + image_name \n",
-    "    cv2.imwrite(path, roi)\n",
-    "    cv2.imshow(\"face\", roi)\n",
-    "    \n",
-    "    cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Load our VGGFaceModel \n",
-    "- This block of code defines the VGGFace model (which we use later) and loads the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model Loaded\n"
-     ]
-    }
-   ],
-   "source": [
-    "#author Sefik Ilkin Serengil\n",
-    "#you can find the documentation of this code from the following link: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
-    "\n",
-    "import numpy as np\n",
-    "import cv2\n",
-    "\n",
-    "from tensorflow.keras.models import Model, Sequential\n",
-    "from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation\n",
-    "from PIL import Image\n",
-    "from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array\n",
-    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "import matplotlib.pyplot as plt\n",
-    "from os import listdir\n",
-    "\n",
-    "def preprocess_image(image_path):\n",
-    "    \"\"\"Loads image from path and resizes it\"\"\"\n",
-    "    img = load_img(image_path, target_size=(224, 224))\n",
-    "    img = img_to_array(img)\n",
-    "    img = np.expand_dims(img, axis=0)\n",
-    "    img = preprocess_input(img)\n",
-    "    return img\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
-    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(ZeroPadding2D((1,1)))\n",
-    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
-    "\n",
-    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Convolution2D(2622, (1, 1)))\n",
-    "model.add(Flatten())\n",
-    "model.add(Activation('softmax'))\n",
-    "\n",
-    "#you can download pretrained weights from https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
-    "from tensorflow.keras.models import model_from_json\n",
-    "model.load_weights('vgg_face_weights.h5')\n",
-    "\n",
-    "vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)\n",
-    "\n",
-    "model = vgg_face_descriptor\n",
-    "\n",
-    " \n",
-    "print(\"Model Loaded\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Test model using your Webcam\n",
-    "This code looks up the faces you extracted in the \"group_of_faces\" folder and uses the similarity (Cosine Similarity) to detect which faces is most similar to the one being extracted with your webcam."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Face representations retrieved successfully\n"
-     ]
-    }
-   ],
-   "source": [
-    "#points to your extracted faces\n",
-    "people_pictures = \"./group_of_faces/\"\n",
-    "\n",
-    "all_people_faces = dict()\n",
-    "\n",
-    "for file in listdir(people_pictures):\n",
-    "    person_face, extension = file.split(\".\")\n",
-    "    all_people_faces[person_face] = model.predict(preprocess_image('./group_of_faces/%s.jpg' % (person_face)))[0,:]\n",
-    "\n",
-    "print(\"Face representations retrieved successfully\")\n",
-    "\n",
-    "def findCosineSimilarity(source_representation, test_representation):\n",
-    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
-    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
-    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
-    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
-    "\n",
-    "#Open Webcam\n",
-    "cap = cv2.VideoCapture(0) \n",
-    "\n",
-    "while(True):\n",
-    "    ret, img = cap.read()\n",
-    "    faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
-    "\n",
-    "    for (x,y,w,h) in faces:\n",
-    "        if w > 100: #Adjust accordingly if your webcam resoluation is higher\n",
-    "            cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
-    "            detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
-    "            detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
-    "\n",
-    "            img_pixels = image.img_to_array(detected_face)\n",
-    "            img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
-    "            img_pixels /= 255\n",
-    "\n",
-    "            captured_representation = model.predict(img_pixels)[0,:]\n",
-    "\n",
-    "            found = 0\n",
-    "            for i in all_people_faces:\n",
-    "                person_name = i\n",
-    "                representation = all_people_faces[i]\n",
-    "\n",
-    "                similarity = findCosineSimilarity(representation, captured_representation)\n",
-    "                if(similarity < 0.30):\n",
-    "                    cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
-    "                    found = 1\n",
-    "                    break\n",
-    "\n",
-    "            #connect face and text\n",
-    "            cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
-    "            cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
-    "\n",
-    "            if(found == 0): #if found image is not in our people database\n",
-    "                cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
-    "\n",
-    "    cv2.imshow('img',img)\n",
-    "\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test on a video\n",
-    "### Since we're using the Friends TV Series characters, let's extract the faces from the images I placed in the \"./friends\" folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collected image names\n"
-     ]
-    }
-   ],
-   "source": [
-    "from os import listdir\n",
-    "from os.path import isfile, join\n",
-    "import cv2\n",
-    "\n",
-    "# Loading out HAARCascade Face Detector \n",
-    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
-    "\n",
-    "# Directory of image of persons we'll be extracting faces frommy\n",
-    "mypath = \"./friends/\"\n",
-    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
-    "print(\"Collected image names\")\n",
-    "\n",
-    "for image_name in image_file_names:\n",
-    "    person_image = cv2.imread(mypath+image_name)\n",
-    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
-    "    for (x,y,w,h) in face_info:\n",
-    "        face = person_image[y:y+h, x:x+w]\n",
-    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
-    "    path = \"./friends_faces/\" + \"face_\" + image_name \n",
-    "    cv2.imwrite(path, roi)\n",
-    "    cv2.imshow(\"face\", roi)\n",
-    "    \n",
-    "    cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Again, we load our faces from the \"friends_faces\" directory and we run our face classifier model our test video"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Face representations retrieved successfully\n"
-     ]
-    }
-   ],
-   "source": [
-    "#points to your extracted faces\n",
-    "people_pictures = \"./friends_faces/\"\n",
-    "\n",
-    "all_people_faces = dict()\n",
-    "\n",
-    "for file in listdir(people_pictures):\n",
-    "    person_face, extension = file.split(\".\")\n",
-    "    all_people_faces[person_face] = model.predict(preprocess_image('./friends_faces/%s.jpg' % (person_face)))[0,:]\n",
-    "\n",
-    "print(\"Face representations retrieved successfully\")\n",
-    "\n",
-    "def findCosineSimilarity(source_representation, test_representation):\n",
-    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
-    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
-    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
-    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
-    "\n",
-    "cap = cv2.VideoCapture('testfriends.mp4')\n",
-    "\n",
-    "while(True):\n",
-    "        ret, img = cap.read()\n",
-    "        img = cv2.resize(img, (320, 180)) # Re-size video to as smaller size to improve face detection speed\n",
-    "        faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
-    "\n",
-    "        for (x,y,w,h) in faces:\n",
-    "            if w > 13: \n",
-    "                cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
-    "\n",
-    "                detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
-    "                detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
-    "\n",
-    "                img_pixels = image.img_to_array(detected_face)\n",
-    "                img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
-    "                img_pixels /= 255\n",
-    "\n",
-    "                captured_representation = model.predict(img_pixels)[0,:]\n",
-    "\n",
-    "                found = 0\n",
-    "                for i in all_people_faces:\n",
-    "                    person_name = i\n",
-    "                    representation = all_people_faces[i]\n",
-    "\n",
-    "                    similarity = findCosineSimilarity(representation, captured_representation)\n",
-    "                    if(similarity < 0.30):\n",
-    "                        cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
-    "                        found = 1\n",
-    "                        break\n",
-    "\n",
-    "                #connect face and text\n",
-    "                cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
-    "                cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
-    "\n",
-    "                if(found == 0): #if found image is not in our people database\n",
-    "                    cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
-    "\n",
-    "        cv2.imshow('img',img)\n",
-    "        if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "            break\n",
-    "\n",
-    "#kill open cv things\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-  "kernelspec": {
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-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
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-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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+size 15335

26. Credit Card/26. Credit Card Reader.ipynb

@@ -1,1066 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Let's Create Our Credit Card Dataset\n",
-    "- There two main font variations used in credit cards"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "\n",
-    "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
-    "cv2.imshow(\"Digits 1\", cc1)\n",
-    "cv2.waitKey(0)\n",
-    "cc2 = cv2.imread('creditcard_digits2.jpg', 0)\n",
-    "cv2.imshow(\"Digits 2\", cc2)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"Digits 2 Thresholded\", th2)\n",
-    "cv2.waitKey(0)\n",
-    "    \n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Now let's get generate an Augumentated Dataset from these two samples \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "./credit_card/train/0\n",
-      "./credit_card/train/1\n",
-      "./credit_card/train/2\n",
-      "./credit_card/train/3\n",
-      "./credit_card/train/4\n",
-      "./credit_card/train/5\n",
-      "./credit_card/train/6\n",
-      "./credit_card/train/7\n",
-      "./credit_card/train/8\n",
-      "./credit_card/train/9\n",
-      "./credit_card/test/0\n",
-      "./credit_card/test/1\n",
-      "./credit_card/test/2\n",
-      "./credit_card/test/3\n",
-      "./credit_card/test/4\n",
-      "./credit_card/test/5\n",
-      "./credit_card/test/6\n",
-      "./credit_card/test/7\n",
-      "./credit_card/test/8\n",
-      "./credit_card/test/9\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Create our dataset directories\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "def makedir(directory):\n",
-    "    \"\"\"Creates a new directory if it does not exist\"\"\"\n",
-    "    if not os.path.exists(directory):\n",
-    "        os.makedirs(directory)\n",
-    "        return None, 0\n",
-    "    \n",
-    "for i in range(0,10):\n",
-    "    directory_name = \"./credit_card/train/\"+str(i)\n",
-    "    print(directory_name)\n",
-    "    makedir(directory_name) \n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    directory_name = \"./credit_card/test/\"+str(i)\n",
-    "    print(directory_name)\n",
-    "    makedir(directory_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Let's make our Data Augmentation Functions\n",
-    "These are used to perform image manipulation and pre-processing tasks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np \n",
-    "import random\n",
-    "import cv2\n",
-    "from scipy.ndimage import convolve\n",
-    "\n",
-    "def DigitAugmentation(frame, dim = 32):\n",
-    "    \"\"\"Randomly alters the image using noise, pixelation and streching image functions\"\"\"\n",
-    "    frame = cv2.resize(frame, None, fx=2, fy=2, interpolation = cv2.INTER_CUBIC)\n",
-    "    frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)\n",
-    "    random_num = np.random.randint(0,9)\n",
-    "\n",
-    "    if (random_num % 2 == 0):\n",
-    "        frame = add_noise(frame)\n",
-    "    if(random_num % 3 == 0):\n",
-    "        frame = pixelate(frame)\n",
-    "    if(random_num % 2 == 0):\n",
-    "        frame = stretch(frame)\n",
-    "    frame = cv2.resize(frame, (dim, dim), interpolation = cv2.INTER_AREA)\n",
-    "\n",
-    "    return frame \n",
-    "\n",
-    "def add_noise(image):\n",
-    "    \"\"\"Addings noise to image\"\"\"\n",
-    "    prob = random.uniform(0.01, 0.05)\n",
-    "    rnd = np.random.rand(image.shape[0], image.shape[1])\n",
-    "    noisy = image.copy()\n",
-    "    noisy[rnd < prob] = 0\n",
-    "    noisy[rnd > 1 - prob] = 1\n",
-    "    return noisy\n",
-    "\n",
-    "def pixelate(image):\n",
-    "    \"Pixelates an image by reducing the resolution then upscaling it\"\n",
-    "    dim = np.random.randint(8,12)\n",
-    "    image = cv2.resize(image, (dim, dim), interpolation = cv2.INTER_AREA)\n",
-    "    image = cv2.resize(image, (16, 16), interpolation = cv2.INTER_AREA)\n",
-    "    return image\n",
-    "\n",
-    "def stretch(image):\n",
-    "    \"Randomly applies different degrees of stretch to image\"\n",
-    "    ran = np.random.randint(0,3)*2\n",
-    "    if np.random.randint(0,2) == 0:\n",
-    "        frame = cv2.resize(image, (32, ran+32), interpolation = cv2.INTER_AREA)\n",
-    "        return frame[int(ran/2):int(ran+32)-int(ran/2), 0:32]\n",
-    "    else:\n",
-    "        frame = cv2.resize(image, (ran+32, 32), interpolation = cv2.INTER_AREA)\n",
-    "        return frame[0:32, int(ran/2):int(ran+32)-int(ran/2)]\n",
-    "    \n",
-    "def pre_process(image, inv = False):\n",
-    "    \"\"\"Uses OTSU binarization on an image\"\"\"\n",
-    "    try:\n",
-    "        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    except:\n",
-    "        gray_image = image\n",
-    "        pass\n",
-    "    \n",
-    "    if inv == False:\n",
-    "        _, th2 = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
-    "    else:\n",
-    "        _, th2 = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "    resized = cv2.resize(th2, (32,32), interpolation = cv2.INTER_AREA)\n",
-    "    return resized"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Testing our augmentation functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"cc1\", th2)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()\n",
-    "\n",
-    "# This is the coordinates of the region enclosing  the first digit\n",
-    "# This is preset and was done manually based on this specific image\n",
-    "region = [(0, 0), (35, 48)]\n",
-    "\n",
-    "# Assigns values to each region for ease of interpretation\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "for i in range(0,1): #We only look at the first digit in testing out augmentation functions\n",
-    "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "    for j in range(0,10):\n",
-    "        roi2 = DigitAugmentation(roi)\n",
-    "        roi_otsu = pre_process(roi2, inv = False)\n",
-    "        cv2.imshow(\"otsu\", roi_otsu)\n",
-    "        cv2.waitKey(0)\n",
-    "        \n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating our Training Data (1000 variations of each font type)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Augmenting Digit -  0\n",
-      "Augmenting Digit -  1\n",
-      "Augmenting Digit -  2\n",
-      "Augmenting Digit -  3\n",
-      "Augmenting Digit -  4\n",
-      "Augmenting Digit -  5\n",
-      "Augmenting Digit -  6\n",
-      "Augmenting Digit -  7\n",
-      "Augmenting Digit -  8\n",
-      "Augmenting Digit -  9\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating 2000 Images for each digit in creditcard_digits1 - TRAINING DATA\n",
-    "\n",
-    "# Load our first image\n",
-    "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
-    "\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"cc1\", th2)\n",
-    "cv2.imshow(\"creditcard_digits1\", cc1)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()\n",
-    "\n",
-    "region = [(2, 19), (50, 72)]\n",
-    "\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "for i in range(0,10):   \n",
-    "    # We jump the next digit each time we loop\n",
-    "    if i > 0:\n",
-    "        top_left_x = top_left_x + 59\n",
-    "        bottom_right_x = bottom_right_x + 59\n",
-    "\n",
-    "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "    print(\"Augmenting Digit - \", str(i))\n",
-    "    # We create 200 versions of each image for our dataset\n",
-    "    for j in range(0,2000):\n",
-    "        roi2 = DigitAugmentation(roi)\n",
-    "        roi_otsu = pre_process(roi2, inv = True)\n",
-    "        cv2.imwrite(\"./credit_card/train/\"+str(i)+\"./_1_\"+str(j)+\".jpg\", roi_otsu)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creating 2000 Images for each digit in creditcard_digits2 - TRAINING DATA\n",
-    "\n",
-    "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"cc1\", th2)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()\n",
-    "\n",
-    "region = [(0, 0), (35, 48)]\n",
-    "\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "for i in range(0,10):   \n",
-    "    if i > 0:\n",
-    "        # We jump the next digit each time we loop\n",
-    "        top_left_x = top_left_x + 35\n",
-    "        bottom_right_x = bottom_right_x + 35\n",
-    "\n",
-    "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "    print(\"Augmenting Digit - \", str(i))\n",
-    "    # We create 200 versions of each image for our dataset\n",
-    "    for j in range(0,2000):\n",
-    "        roi2 = DigitAugmentation(roi)\n",
-    "        roi_otsu = pre_process(roi2, inv = False)\n",
-    "        cv2.imwrite(\"./credit_card/train/\"+str(i)+\"./_2_\"+str(j)+\".jpg\", roi_otsu)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Augmenting Digit -  0\n",
-      "Augmenting Digit -  1\n",
-      "Augmenting Digit -  2\n",
-      "Augmenting Digit -  3\n",
-      "Augmenting Digit -  4\n",
-      "Augmenting Digit -  5\n",
-      "Augmenting Digit -  6\n",
-      "Augmenting Digit -  7\n",
-      "Augmenting Digit -  8\n",
-      "Augmenting Digit -  9\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating 200 Images for each digit in creditcard_digits1 - TEST DATA\n",
-    "\n",
-    "# Load our first image\n",
-    "cc1 = cv2.imread('creditcard_digits1.jpg', 0)\n",
-    "\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"cc1\", th2)\n",
-    "cv2.imshow(\"creditcard_digits1\", cc1)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()\n",
-    "\n",
-    "region = [(2, 19), (50, 72)]\n",
-    "\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "for i in range(0,10):   \n",
-    "    # We jump the next digit each time we loop\n",
-    "    if i > 0:\n",
-    "        top_left_x = top_left_x + 59\n",
-    "        bottom_right_x = bottom_right_x + 59\n",
-    "\n",
-    "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "    print(\"Augmenting Digit -\", str(i))\n",
-    "    # We create 200 versions of each image for our dataset\n",
-    "    for j in range(0,2000):\n",
-    "        roi2 = DigitAugmentation(roi)\n",
-    "        roi_otsu = pre_process(roi2, inv = True)\n",
-    "        cv2.imwrite(\"./credit_card/test/\"+str(i)+\"./_1_\"+str(j)+\".jpg\", roi_otsu)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Augmenting Digit -  0\n",
-      "Augmenting Digit -  1\n",
-      "Augmenting Digit -  2\n",
-      "Augmenting Digit -  3\n",
-      "Augmenting Digit -  4\n",
-      "Augmenting Digit -  5\n",
-      "Augmenting Digit -  6\n",
-      "Augmenting Digit -  7\n",
-      "Augmenting Digit -  8\n",
-      "Augmenting Digit -  9\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating 200 Images for each digit in creditcard_digits2 - TEST DATA\n",
-    "\n",
-    "cc1 = cv2.imread('creditcard_digits2.jpg', 0)\n",
-    "_, th2 = cv2.threshold(cc1, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n",
-    "cv2.imshow(\"cc1\", th2)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()\n",
-    "\n",
-    "region = [(0, 0), (35, 48)]\n",
-    "\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "for i in range(0,10):   \n",
-    "    if i > 0:\n",
-    "        # We jump the next digit each time we loop\n",
-    "        top_left_x = top_left_x + 35\n",
-    "        bottom_right_x = bottom_right_x + 35\n",
-    "\n",
-    "    roi = cc1[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "    print(\"Augmenting Digit - \", str(i))\n",
-    "    # We create 200 versions of each image for our dataset\n",
-    "    for j in range(0,2000):\n",
-    "        roi2 = DigitAugmentation(roi)\n",
-    "        roi_otsu = pre_process(roi2, inv = False)\n",
-    "        cv2.imwrite(\"./credit_card/test/\"+str(i)+\"./_2_\"+str(j)+\".jpg\", roi_otsu)\n",
-    "        #cv2.imshow(\"otsu\", roi_otsu)\n",
-    "        #print(\"-\")\n",
-    "        #cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Creating our Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found 20254 images belonging to 10 classes.\n",
-      "Found 40000 images belonging to 10 classes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D\n",
-    "from tensorflow.keras import optimizers\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "input_shape = (32, 32, 3)\n",
-    "img_width = 32\n",
-    "img_height = 32\n",
-    "num_classes = 10\n",
-    "nb_train_samples = 10000\n",
-    "nb_validation_samples = 2000\n",
-    "batch_size = 16\n",
-    "epochs = 1\n",
-    "\n",
-    "train_data_dir = './credit_card/train'\n",
-    "validation_data_dir = './credit_card/test'\n",
-    "\n",
-    "# Creating our data generator for our test data\n",
-    "validation_datagen = ImageDataGenerator(\n",
-    "    # used to rescale the pixel values from [0, 255] to [0, 1] interval\n",
-    "    rescale = 1./255)\n",
-    "\n",
-    "# Creating our data generator for our training data\n",
-    "train_datagen = ImageDataGenerator(\n",
-    "      rescale = 1./255,              # normalize pixel values to [0,1]\n",
-    "      rotation_range = 10,           # randomly applies rotations\n",
-    "      width_shift_range = 0.25,       # randomly applies width shifting\n",
-    "      height_shift_range = 0.25,      # randomly applies height shifting\n",
-    "      shear_range=0.5,\n",
-    "      zoom_range=0.5,\n",
-    "      horizontal_flip = False,        # randonly flips the image\n",
-    "      fill_mode = 'nearest')         # uses the fill mode nearest to fill gaps created by the above\n",
-    "\n",
-    "# Specify criteria about our training data, such as the directory, image size, batch size and type \n",
-    "# automagically retrieve images and their classes for train and validation sets\n",
-    "train_generator = train_datagen.flow_from_directory(\n",
-    "        train_data_dir,\n",
-    "        target_size = (img_width, img_height),\n",
-    "        batch_size = batch_size,\n",
-    "        class_mode = 'categorical')\n",
-    "\n",
-    "validation_generator = validation_datagen.flow_from_directory(\n",
-    "        validation_data_dir,\n",
-    "        target_size = (img_width, img_height),\n",
-    "        batch_size = batch_size,\n",
-    "        class_mode = 'categorical',\n",
-    "        shuffle = False)    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating out Model based on the LeNet CNN Architecture"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_1\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 20)        1520      \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 32, 32, 20)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 20)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 50)        25050     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 16, 16, 50)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 50)          0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 3200)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 500)               1600500   \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 500)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 10)                5010      \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 1,632,080\n",
-      "Trainable params: 1,632,080\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "# 2 sets of CRP (Convolution, RELU, Pooling)\n",
-    "model.add(Conv2D(20, (5, 5),\n",
-    "                 padding = \"same\", \n",
-    "                 input_shape = input_shape))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
-    "\n",
-    "model.add(Conv2D(50, (5, 5),\n",
-    "                 padding = \"same\"))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2)))\n",
-    "\n",
-    "# Fully connected layers (w/ RELU)\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(500))\n",
-    "model.add(Activation(\"relu\"))\n",
-    "\n",
-    "# Softmax (for classification)\n",
-    "model.add(Dense(num_classes))\n",
-    "model.add(Activation(\"softmax\"))\n",
-    "           \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = tf.keras.optimizers.Adadelta(),\n",
-    "              metrics = ['accuracy'])\n",
-    "    \n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training our Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "WARNING:tensorflow:sample_weight modes were coerced from\n",
-      "  ...\n",
-      "    to  \n",
-      "  ['...']\n",
-      "Train for 1250 steps, validate for 250 steps\n",
-      "1249/1250 [============================>.] - ETA: 0s - loss: 0.2923 - accuracy: 0.9020\n",
-      "Epoch 00001: val_loss improved from inf to 0.00030, saving model to creditcard.h5\n",
-      "1250/1250 [==============================] - 150s 120ms/step - loss: 0.2922 - accuracy: 0.9020 - val_loss: 3.0485e-04 - val_accuracy: 1.0000\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.optimizers import RMSprop\n",
-    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
-    "                   \n",
-    "checkpoint = ModelCheckpoint(\"creditcard.h5\",\n",
-    "                             monitor=\"val_loss\",\n",
-    "                             mode=\"min\",\n",
-    "                             save_best_only = True,\n",
-    "                             verbose=1)\n",
-    "\n",
-    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
-    "                          min_delta = 0, \n",
-    "                          patience = 3,\n",
-    "                          verbose = 1,\n",
-    "                          restore_best_weights = True)\n",
-    "\n",
-    "# we put our call backs into a callback list\n",
-    "callbacks = [earlystop, checkpoint]\n",
-    "\n",
-    "# Note we use a very small learning rate \n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = RMSprop(lr = 0.001),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "nb_train_samples = 20000\n",
-    "nb_validation_samples = 4000\n",
-    "epochs = 1\n",
-    "batch_size = 16\n",
-    "\n",
-    "history = model.fit_generator(\n",
-    "    train_generator,\n",
-    "    steps_per_epoch = nb_train_samples // batch_size,\n",
-    "    epochs = epochs,\n",
-    "    callbacks = callbacks,\n",
-    "    validation_data = validation_generator,\n",
-    "    validation_steps = nb_validation_samples // batch_size)\n",
-    "\n",
-    "model.save(\"creditcard.h5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Extract a Credit Card from the backgroud\n",
-    "#### NOTE:\n",
-    "You may need to install imutils \n",
-    "run *pip install imutils* in terminal and restart your kernal to install"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting scikit-image\n",
-      "  Downloading scikit_image-0.17.2-cp37-cp37m-win_amd64.whl (11.5 MB)\n",
-      "Requirement already satisfied, skipping upgrade: numpy>=1.15.1 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (1.16.5)\n",
-      "Requirement already satisfied, skipping upgrade: networkx>=2.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (2.3)\n",
-      "Requirement already satisfied, skipping upgrade: pillow!=7.1.0,!=7.1.1,>=4.3.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (6.2.0)\n",
-      "Requirement already satisfied, skipping upgrade: scipy>=1.0.1 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (1.4.1)\n",
-      "Requirement already satisfied, skipping upgrade: matplotlib!=3.0.0,>=2.0.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (3.1.1)\n",
-      "Collecting tifffile>=2019.7.26\n",
-      "  Downloading tifffile-2020.6.3-py3-none-any.whl (133 kB)\n",
-      "Requirement already satisfied, skipping upgrade: imageio>=2.3.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from scikit-image) (2.6.0)\n",
-      "Collecting PyWavelets>=1.1.1\n",
-      "  Downloading PyWavelets-1.1.1-cp37-cp37m-win_amd64.whl (4.2 MB)\n",
-      "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from networkx>=2.0->scikit-image) (4.4.0)\n",
-      "Requirement already satisfied, skipping upgrade: cycler>=0.10 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
-      "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.1.0)\n",
-      "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.4.2)\n",
-      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.8.0)\n",
-      "Requirement already satisfied, skipping upgrade: six in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from cycler>=0.10->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.12.0)\n",
-      "Requirement already satisfied, skipping upgrade: setuptools in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image) (41.4.0)\n",
-      "Installing collected packages: tifffile, PyWavelets, scikit-image\n",
-      "  Attempting uninstall: PyWavelets\n",
-      "    Found existing installation: PyWavelets 1.0.3\n",
-      "    Uninstalling PyWavelets-1.0.3:\n",
-      "      Successfully uninstalled PyWavelets-1.0.3\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.VerifiedHTTPSConnection object at 0x000002C6B8509088>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /packages/6e/c7/6411a4ce983bf06db8c3b8093b04b268c2580816f61156a5848e24e97118/scikit_image-0.17.2-cp37-cp37m-win_amd64.whl\n",
-      "ERROR: keras-vis 0.4.1 requires keras, which is not installed.\n",
-      "ERROR: Could not install packages due to an EnvironmentError: [WinError 5] Access is denied: 'c:\\\\programdata\\\\anaconda3\\\\envs\\\\cv\\\\lib\\\\site-packages\\\\~ywt\\\\_extensions\\\\_cwt.cp37-win_amd64.pyd'\n",
-      "Consider using the `--user` option or check the permissions.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install -U scikit-image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'filter' from 'skimage' (C:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\skimage\\__init__.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-24-83690ff69298>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mimutils\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#from skimage.filters import threshold_adaptive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mskimage\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfilter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mImportError\u001b[0m: cannot import name 'filter' from 'skimage' (C:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\skimage\\__init__.py)"
-     ]
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "import imutils\n",
-    "#from skimage.filters import threshold_adaptive\n",
-    "from skimage import filter\n",
-    "import os\n",
-    "\n",
-    "def order_points(pts):\n",
-    "    # initialzie a list of coordinates that will be ordered\n",
-    "    # such that the first entry in the list is the top-left,\n",
-    "    # the second entry is the top-right, the third is the\n",
-    "    # bottom-right, and the fourth is the bottom-left\n",
-    "    rect = np.zeros((4, 2), dtype = \"float32\")\n",
-    "\n",
-    "    # the top-left point will have the smallest sum, whereas\n",
-    "    # the bottom-right point will have the largest sum\n",
-    "    s = pts.sum(axis = 1)\n",
-    "    rect[0] = pts[np.argmin(s)]\n",
-    "    rect[2] = pts[np.argmax(s)]\n",
-    "\n",
-    "    # now, compute the difference between the points, the\n",
-    "    # top-right point will have the smallest difference,\n",
-    "    # whereas the bottom-left will have the largest difference\n",
-    "    diff = np.diff(pts, axis = 1)\n",
-    "    rect[1] = pts[np.argmin(diff)]\n",
-    "    rect[3] = pts[np.argmax(diff)]\n",
-    "\n",
-    "    # return the ordered coordinates\n",
-    "    return rect\n",
-    "\n",
-    "def four_point_transform(image, pts):\n",
-    "    # obtain a consistent order of the points and unpack them\n",
-    "    # individually\n",
-    "    rect = order_points(pts)\n",
-    "    (tl, tr, br, bl) = rect\n",
-    "\n",
-    "    # compute the width of the new image, which will be the\n",
-    "    # maximum distance between bottom-right and bottom-left\n",
-    "    # x-coordiates or the top-right and top-left x-coordinates\n",
-    "    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n",
-    "    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n",
-    "    maxWidth = max(int(widthA), int(widthB))\n",
-    "\n",
-    "    # compute the height of the new image, which will be the\n",
-    "    # maximum distance between the top-right and bottom-right\n",
-    "    # y-coordinates or the top-left and bottom-left y-coordinates\n",
-    "    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n",
-    "    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n",
-    "    maxHeight = max(int(heightA), int(heightB))\n",
-    "\n",
-    "    # now that we have the dimensions of the new image, construct\n",
-    "    # the set of destination points to obtain a \"birds eye view\",\n",
-    "    # (i.e. top-down view) of the image, again specifying points\n",
-    "    # in the top-left, top-right, bottom-right, and bottom-left\n",
-    "    # order\n",
-    "    dst = np.array([\n",
-    "        [0, 0],\n",
-    "        [maxWidth - 1, 0],\n",
-    "        [maxWidth - 1, maxHeight - 1],\n",
-    "        [0, maxHeight - 1]], dtype = \"float32\")\n",
-    "\n",
-    "    # compute the perspective transform matrix and then apply it\n",
-    "    M = cv2.getPerspectiveTransform(rect, dst)\n",
-    "    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n",
-    "\n",
-    "    # return the warped image\n",
-    "    return warped\n",
-    "\n",
-    "def doc_Scan(image):\n",
-    "    orig_height, orig_width = image.shape[:2]\n",
-    "    ratio = image.shape[0] / 500.0\n",
-    "\n",
-    "    orig = image.copy()\n",
-    "    image = imutils.resize(image, height = 500)\n",
-    "    orig_height, orig_width = image.shape[:2]\n",
-    "    Original_Area = orig_height * orig_width\n",
-    "    \n",
-    "    # convert the image to grayscale, blur it, and find edges\n",
-    "    # in the image\n",
-    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    gray = cv2.GaussianBlur(gray, (5, 5), 0)\n",
-    "    edged = cv2.Canny(gray, 75, 200)\n",
-    "\n",
-    "    cv2.imshow(\"Image\", image)\n",
-    "    cv2.imshow(\"Edged\", edged)\n",
-    "    cv2.waitKey(0)\n",
-    "    # show the original image and the edge detected image\n",
-    "\n",
-    "    # find the contours in the edged image, keeping only the\n",
-    "    # largest ones, and initialize the screen contour\n",
-    "    _, contours, hierarchy  = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
-    "    contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]\n",
-    "    \n",
-    "    # loop over the contours\n",
-    "    for c in contours:\n",
-    "\n",
-    "        # approximate the contour\n",
-    "        area = cv2.contourArea(c)\n",
-    "        if area < (Original_Area/3):\n",
-    "            print(\"Error Image Invalid\")\n",
-    "            return(\"ERROR\")\n",
-    "        peri = cv2.arcLength(c, True)\n",
-    "        approx = cv2.approxPolyDP(c, 0.02 * peri, True)\n",
-    "\n",
-    "        # if our approximated contour has four points, then we\n",
-    "        # can assume that we have found our screen\n",
-    "        if len(approx) == 4:\n",
-    "            screenCnt = approx\n",
-    "            break\n",
-    "\n",
-    "    # show the contour (outline) of the piece of paper\n",
-    "    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)\n",
-    "    cv2.imshow(\"Outline\", image)\n",
-    "\n",
-    "    warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)\n",
-    "    # convert the warped image to grayscale, then threshold it\n",
-    "    # to give it that 'black and white' paper effect\n",
-    "    cv2.resize(warped, (640,403), interpolation = cv2.INTER_AREA)\n",
-    "    cv2.imwrite(\"credit_card_color.jpg\", warped)\n",
-    "    warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)\n",
-    "    warped = warped.astype(\"uint8\") * 255\n",
-    "    cv2.imshow(\"Extracted Credit Card\", warped)\n",
-    "    cv2.waitKey(0)\n",
-    "    cv2.destroyAllWindows()\n",
-    "    return warped"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extract our Credit Card and the Region of Interest (ROI)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "image = cv2.imread('test_card.jpg')\n",
-    "image = doc_Scan(image)\n",
-    "\n",
-    "region = [(55, 210), (640, 290)]\n",
-    "\n",
-    "top_left_y = region[0][1]\n",
-    "bottom_right_y = region[1][1]\n",
-    "top_left_x = region[0][0]\n",
-    "bottom_right_x = region[1][0]\n",
-    "\n",
-    "# Extracting the area were the credit numbers are located\n",
-    "roi = image[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n",
-    "cv2.imshow(\"Region\", roi)\n",
-    "cv2.imwrite(\"credit_card_extracted_digits.jpg\", roi)\n",
-    "cv2.waitKey(0)\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading our trained model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.models import load_model\n",
-    "import keras\n",
-    "\n",
-    "classifier = load_model('creditcard.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Let's test on our extracted image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "3\n",
-      "5\n",
-      "5\n",
-      "2\n",
-      "2\n",
-      "0\n",
-      "3\n",
-      "2\n",
-      "3\n",
-      "9\n",
-      "0\n"
-     ]
-    }
-   ],
-   "source": [
-    "def x_cord_contour(contours):\n",
-    "    #Returns the X cordinate for the contour centroid\n",
-    "    if cv2.contourArea(contours) > 10:\n",
-    "        M = cv2.moments(contours)\n",
-    "        return (int(M['m10']/M['m00']))\n",
-    "    else:\n",
-    "        pass\n",
-    "\n",
-    "img = cv2.imread('credit_card_extracted_digits.jpg')\n",
-    "orig_img = cv2.imread('credit_card_color.jpg')\n",
-    "gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
-    "cv2.imshow(\"image\", img)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "# Blur image then find edges using Canny \n",
-    "blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n",
-    "#cv2.imshow(\"blurred\", blurred)\n",
-    "#cv2.waitKey(0)\n",
-    "\n",
-    "edged = cv2.Canny(blurred, 30, 150)\n",
-    "#cv2.imshow(\"edged\", edged)\n",
-    "#cv2.waitKey(0)\n",
-    "\n",
-    "# Find Contours\n",
-    "_, contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
-    "\n",
-    "#Sort out contours left to right by using their x cordinates\n",
-    "contours = sorted(contours, key=cv2.contourArea, reverse=True)[:13] #Change this to 16 to get all digits\n",
-    "contours = sorted(contours, key = x_cord_contour, reverse = False)\n",
-    "\n",
-    "# Create empty array to store entire number\n",
-    "full_number = []\n",
-    "\n",
-    "# loop over the contours\n",
-    "for c in contours:\n",
-    "    # compute the bounding box for the rectangle\n",
-    "    (x, y, w, h) = cv2.boundingRect(c)    \n",
-    "    if w >= 5 and h >= 25 and cv2.contourArea(c) < 1000:\n",
-    "        roi = blurred[y:y + h, x:x + w]\n",
-    "        #ret, roi = cv2.threshold(roi, 20, 255,cv2.THRESH_BINARY_INV)\n",
-    "        cv2.imshow(\"ROI1\", roi)\n",
-    "        roi_otsu = pre_process(roi, True)\n",
-    "        cv2.imshow(\"ROI2\", roi_otsu)\n",
-    "        roi_otsu = cv2.cvtColor(roi_otsu, cv2.COLOR_GRAY2RGB)\n",
-    "        roi_otsu = keras.preprocessing.image.img_to_array(roi_otsu)\n",
-    "        roi_otsu = roi_otsu * 1./255\n",
-    "        roi_otsu = np.expand_dims(roi_otsu, axis=0)\n",
-    "        image = np.vstack([roi_otsu])\n",
-    "        label = str(classifier.predict_classes(image, batch_size = 10))[1]\n",
-    "        print(label)\n",
-    "        (x, y, w, h) = (x+region[0][0], y+region[0][1], w, h)\n",
-    "        cv2.rectangle(orig_img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
-    "        cv2.putText(orig_img, label, (x , y + 90), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)\n",
-    "        cv2.imshow(\"image\", orig_img)\n",
-    "        cv2.waitKey(0) \n",
-    "        \n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
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-   "display_name": "Python 3",
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-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/.ipynb_checkpoints/4.1 - Handwritten Digit Classification Demo (MNIST)-checkpoint.ipynb

@@ -1,327 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's load a Handwritten Digit classifier we'll be building very soon!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/mnist_simple_cnn.h5')\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "def draw_test(name, pred, input_im):\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "\n",
-    "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC) \n",
-    "    input_im = input_im.reshape(1,28,28,1) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(classifier.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "    draw_test(\"Prediction\", res, imageL) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'<' not supported between instances of 'NoneType' and 'NoneType'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-ad16a07b4c0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m#Sort out contours left to right by using their x cordinates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mcontours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_cord_contour\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;31m# Create empty array to store entire number\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'NoneType' and 'NoneType'"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import cv2\n",
-    "from preprocessors import x_cord_contour, makeSquare, resize_to_pixel\n",
-    "\n",
-    "image = cv2.imread('images/numbers.jpg')\n",
-    "gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
-    "cv2.imshow(\"image\", image)\n",
-    "cv2.imshow(\"gray\", gray)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "# Blur image then find edges using Canny \n",
-    "blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n",
-    "cv2.imshow(\"blurred\", blurred)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "edged = cv2.Canny(blurred, 30, 150)\n",
-    "cv2.imshow(\"edged\", edged)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "# Fint Contours\n",
-    "_, contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
-    "\n",
-    "#Sort out contours left to right by using their x cordinates\n",
-    "contours = sorted(contours, key = x_cord_contour, reverse = False)\n",
-    "\n",
-    "# Create empty array to store entire number\n",
-    "full_number = []\n",
-    "\n",
-    "# loop over the contours\n",
-    "for c in contours:\n",
-    "    # compute the bounding box for the rectangle\n",
-    "    (x, y, w, h) = cv2.boundingRect(c)    \n",
-    "    \n",
-    "    #cv2.drawContours(image, contours, -1, (0,255,0), 3)\n",
-    "    #cv2.imshow(\"Contours\", image)\n",
-    "\n",
-    "    if w >= 5 and h >= 25:\n",
-    "        roi = blurred[y:y + h, x:x + w]\n",
-    "        ret, roi = cv2.threshold(roi, 127, 255,cv2.THRESH_BINARY_INV)\n",
-    "        squared = makeSquare(roi)\n",
-    "        final = resize_to_pixel(20, squared)\n",
-    "        cv2.imshow(\"final\", final)\n",
-    "        final_array = final.reshape((1,400))\n",
-    "        final_array = final_array.astype(np.float32)\n",
-    "        #ret, result, neighbours, dist = knn.find_nearest(final_array, k=1)\n",
-    "        #number = str(int(float(result[0])))\n",
-    "        #full_number.append(number)\n",
-    "        # draw a rectangle around the digit, the show what the\n",
-    "        # digit was classified as\n",
-    "        #cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
-    "        #cv2.putText(image, number, (x , y + 155),\n",
-    "        #    cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n",
-    "        cv2.imshow(\"image\", image)\n",
-    "        cv2.waitKey(0) \n",
-    "        \n",
-    "cv2.destroyAllWindows()\n",
-    "print (\"The number is: \" + ''.join(full_number))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (60000, 28, 28, 1)\n",
-      "60000 train samples\n",
-      "10000 test samples\n",
-      "Number of Classes: 10\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 9216)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 128)               1179776   \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                1290      \n",
-      "=================================================================\n",
-      "Total params: 1,199,882\n",
-      "Trainable params: 1,199,882\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/5\n",
-      "60000/60000 [==============================] - 189s 3ms/step - loss: 0.2667 - acc: 0.9187 - val_loss: 0.0556 - val_acc: 0.9819\n",
-      "Epoch 2/5\n",
-      "60000/60000 [==============================] - 170s 3ms/step - loss: 0.0889 - acc: 0.9740 - val_loss: 0.0396 - val_acc: 0.9864\n",
-      "Epoch 3/5\n",
-      "60000/60000 [==============================] - 192s 3ms/step - loss: 0.0670 - acc: 0.9805 - val_loss: 0.0350 - val_acc: 0.9884\n",
-      "Epoch 4/5\n",
-      "60000/60000 [==============================] - 194s 3ms/step - loss: 0.0553 - acc: 0.9838 - val_loss: 0.0321 - val_acc: 0.9897\n",
-      "Epoch 5/5\n",
-      "60000/60000 [==============================] - 204s 3ms/step - loss: 0.0464 - acc: 0.9856 - val_loss: 0.0282 - val_acc: 0.9907\n",
-      "Test loss: 0.028205487172584982\n",
-      "Test accuracy: 0.9907\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.datasets import mnist\n",
-    "from keras.utils import np_utils\n",
-    "import keras\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, Flatten\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras import backend as K\n",
-    "\n",
-    "# Training Parameters\n",
-    "batch_size = 128\n",
-    "epochs = 5\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = np_utils.to_categorical(y_train)\n",
-    "y_test = np_utils.to_categorical(y_test)\n",
-    "\n",
-    "# Let's count the number columns in our hot encoded matrix \n",
-    "print (\"Number of Classes: \" + str(y_test.shape[1]))\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
-    "\n",
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
-    "                 activation='relu',\n",
-    "                 input_shape=input_shape))\n",
-    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(128, activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = keras.optimizers.Adadelta(),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "print(model.summary())\n",
-    "\n",
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          verbose=1,\n",
-    "          validation_data=(x_test, y_test))\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/.ipynb_checkpoints/4.2 - Image Classifier - CIFAR10-checkpoint.ipynb

@@ -1,6 +1,3 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/.ipynb_checkpoints/4.3. Live Sketching-checkpoint.ipynb

@@ -1,101 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.4.3\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "import matplotlib\n",
-    "print (cv2.__version__)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "# Our sketch generating function\n",
-    "def sketch(image):\n",
-    "    # Convert image to grayscale\n",
-    "    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    \n",
-    "    # Clean up image using Guassian Blur\n",
-    "    img_gray_blur = cv2.GaussianBlur(img_gray, (5,5), 0)\n",
-    "    \n",
-    "    # Extract edges\n",
-    "    canny_edges = cv2.Canny(img_gray_blur, 10, 70)\n",
-    "    \n",
-    "    # Do an invert binarize the image \n",
-    "    ret, mask = cv2.threshold(canny_edges, 70, 255, cv2.THRESH_BINARY_INV)\n",
-    "    return mask\n",
-    "\n",
-    "\n",
-    "# Initialize webcam, cap is the object provided by VideoCapture\n",
-    "# It contains a boolean indicating if it was sucessful (ret)\n",
-    "# It also contains the images collected from the webcam (frame)\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    cv2.imshow('Our Live Sketcher', sketch(frame))\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "# Release camera and close windows\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
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-   "name": "python",
-   "nbconvert_exporter": "python",
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/.ipynb_checkpoints/Test - Imports Keras, OpenCV and tests webcam-checkpoint.ipynb

@@ -1,73 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "# Our sketch generating function\n",
-    "def sketch(image):\n",
-    "    # Convert image to grayscale\n",
-    "    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    \n",
-    "    # Clean up image using Guassian Blur\n",
-    "    img_gray_blur = cv2.GaussianBlur(img_gray, (5,5), 0)\n",
-    "    \n",
-    "    # Extract edges\n",
-    "    canny_edges = cv2.Canny(img_gray_blur, 10, 70)\n",
-    "    \n",
-    "    # Do an invert binarize the image \n",
-    "    ret, mask = cv2.threshold(canny_edges, 70, 255, cv2.THRESH_BINARY_INV)\n",
-    "    return mask\n",
-    "\n",
-    "\n",
-    "# Initialize webcam, cap is the object provided by VideoCapture\n",
-    "# It contains a boolean indicating if it was sucessful (ret)\n",
-    "# It also contains the images collected from the webcam (frame)\n",
-    "cap = cv2.VideoCapture(0)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    cv2.imshow('Our Live Sketcher', sketch(frame))\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "# Release camera and close windows\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/4.1 - Handwritten Digit Classification Demo (MNIST).ipynb

@@ -1,656 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's load a Handwritten Digit classifier we'll be building very soon!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T16:02:55.674903Z",
-     "start_time": "2025-03-13T16:02:41.962641Z"
-    }
-   },
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "import tensorflow.keras\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.models import load_model\n",
-    "\n"
-   ],
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-13 21:32:43.419874: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-03-13 21:32:43.428343: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-03-13 21:32:43.459714: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1741881763.520586   24229 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1741881763.535534   24229 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2025-03-13 21:32:43.581360: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T15:53:32.218716Z",
-     "start_time": "2025-03-13T15:53:31.940957Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "classifier = load_model('mnist_simple_cnn.h5' )",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-13 21:23:31.971561: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n",
-      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "start_time": "2025-03-13T15:53:54.412494Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "print(classifier.summary())\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "def draw_test(name, pred, input_im):\n",
-    "    BLACK = [0,0,0]\n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "\n",
-    "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC)\n",
-    "    input_im = input_im.reshape(1,28,28,1)\n",
-    "\n",
-    "    ## Get Prediction\n",
-    "    res = str(classifier.predict(input_im, 1, verbose = 0)[0])\n",
-    "    draw_test(\"Prediction\", res, imageL)\n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "\n",
-    "cv2.destroyAllWindows()\n"
-   ],
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1mModel: \"sequential_3\"\u001B[0m\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_2 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m26\u001B[0m, \u001B[38;5;34m26\u001B[0m, \u001B[38;5;34m32\u001B[0m)     │           \u001B[38;5;34m320\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_3 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m24\u001B[0m, \u001B[38;5;34m24\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │        \u001B[38;5;34m18,496\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_1 (\u001B[38;5;33mMaxPooling2D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten_1 (\u001B[38;5;33mFlatten\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m9216\u001B[0m)           │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m)            │     \u001B[38;5;34m1,179,776\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m)            │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m10\u001B[0m)             │         \u001B[38;5;34m1,290\u001B[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,179,776</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
-       "└──���──────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m1,199,884\u001B[0m (4.58 MB)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,199,884</span> (4.58 MB)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m1,199,882\u001B[0m (4.58 MB)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,199,882</span> (4.58 MB)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Optimizer params: \u001B[0m\u001B[38;5;34m2\u001B[0m (12.00 B)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (12.00 B)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n"
-     ]
-    }
-   ],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T16:05:45.557030Z",
-     "start_time": "2025-03-13T16:05:45.541940Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "import numpy",
-   "outputs": [],
-   "execution_count": 2
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing our classifier on a real image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "start_time": "2025-03-13T16:05:47.832541Z"
-    }
-   },
-   "source": [
-    "import numpy as np\n",
-    "import cv2\n",
-    "from preprocessors import x_cord_contour, makeSquare, resize_to_pixel\n",
-    "       \n",
-    "image = cv2.imread('images/numbers.jpg')\n",
-    "gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n",
-    "cv2.imshow(\"image\", image)\n",
-    "cv2.waitKey(0)\n",
-    "\n",
-    "# Blur image then find edges using Canny \n",
-    "blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n",
-    "#cv2.imshow(\"blurred\", blurred)\n",
-    "#cv2.waitKey(0)\n",
-    "\n",
-    "edged = cv2.Canny(blurred, 30, 150)\n",
-    "#cv2.imshow(\"edged\", edged)\n",
-    "#cv2.waitKey(0)\n",
-    "\n",
-    "# Find Contours\n",
-    "contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
-    "\n",
-    "#Sort out contours left to right by using their x cordinates\n",
-    "contours = sorted(contours, key = x_cord_contour, reverse = False)\n",
-    "\n",
-    "# Create empty array to store entire number\n",
-    "full_number = []\n",
-    "\n",
-    "# loop over the contours\n",
-    "for c in contours:\n",
-    "    # compute the bounding box for the rectangle\n",
-    "    (x, y, w, h) = cv2.boundingRect(c)    \n",
-    "\n",
-    "    if w >= 5 and h >= 25:\n",
-    "        roi = blurred[y:y + h, x:x + w]\n",
-    "        ret, roi = cv2.threshold(roi, 127, 255,cv2.THRESH_BINARY_INV)\n",
-    "        roi = makeSquare(roi)\n",
-    "        roi = resize_to_pixel(28, roi)\n",
-    "        cv2.imshow(\"ROI\", roi)\n",
-    "        roi = roi / 255.0       \n",
-    "        roi = roi.reshape(1,28,28,1) \n",
-    "\n",
-    "        ## Get Prediction\n",
-    "        res = str(classifier.predict_classes(roi, 1, verbose = 0)[0])\n",
-    "        full_number.append(res)\n",
-    "        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\n",
-    "        cv2.putText(image, res, (x , y + 155), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n",
-    "        cv2.imshow(\"image\", image)\n",
-    "        cv2.waitKey(0) \n",
-    "        \n",
-    "cv2.destroyAllWindows()\n",
-    "print (\"The number is: \" + ''.join(full_number))"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training this Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T13:12:31.511869Z",
-     "start_time": "2025-03-13T12:29:08.572403Z"
-    }
-   },
-   "source": [
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "from tensorflow.keras.optimizers import Adadelta\n",
-    "from tensorflow.keras.datasets import mnist\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D ,Input\n",
-    "from tensorflow.keras import backend as K\n",
-    "\n",
-    "# Training Parameters\n",
-    "batch_size = 128\n",
-    "epochs = 20\n",
-    "\n",
-    "# loads the MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
-    "\n",
-    "# Lets store the number of rows and columns\n",
-    "img_rows = x_train[0].shape[0]\n",
-    "img_cols = x_train[1].shape[0]\n",
-    "\n",
-    "# Getting our date in the right 'shape' needed for Keras\n",
-    "# We need to add a 4th dimenion to our date thereby changing our\n",
-    "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
-    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
-    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
-    "\n",
-    "# store the shape of a single image \n",
-    "input_shape = (img_rows, img_cols, 1)\n",
-    "\n",
-    "# change our image type to float32 data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "# Let's count the number columns in our hot encoded matrix \n",
-    "print (\"Number of Classes: \" + str(y_test.shape[1]))\n",
-    "\n",
-    "num_classes = y_test.shape[1]\n",
-    "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
-    "\n",
-    "# create model\n",
-    "model = Sequential()\n",
-    "\n",
-    "model.add(Input(shape=(28,28,1)))\n",
-    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
-    "                 activation='relu',\n",
-    "                 input_shape=input_shape))\n",
-    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(128, activation='relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = Adadelta(),\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "print(model.summary())\n",
-    "\n",
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          verbose=1,\n",
-    "          validation_data=(x_test, y_test))\n",
-    "\n",
-    "score = model.evaluate(x_test, y_test, verbose=0)\n",
-    "print('Test loss:', score[0])\n",
-    "print('Test accuracy:', score[1])"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (60000, 28, 28, 1)\n",
-      "60000 train samples\n",
-      "10000 test samples\n",
-      "Number of Classes: 10\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1mModel: \"sequential_3\"\u001B[0m\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_2 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m26\u001B[0m, \u001B[38;5;34m26\u001B[0m, \u001B[38;5;34m32\u001B[0m)     │           \u001B[38;5;34m320\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_3 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m24\u001B[0m, \u001B[38;5;34m24\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │        \u001B[38;5;34m18,496\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_1 (\u001B[38;5;33mMaxPooling2D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m12\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten_1 (\u001B[38;5;33mFlatten\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m9216\u001B[0m)           │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m)            │     \u001B[38;5;34m1,179,776\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (\u001B[38;5;33mDropout\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m)            │             \u001B[38;5;34m0\u001B[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m10\u001B[0m)             │         \u001B[38;5;34m1,290\u001B[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9216</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,179,776</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m1,199,882\u001B[0m (4.58 MB)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,199,882</span> (4.58 MB)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m1,199,882\u001B[0m (4.58 MB)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,199,882</span> (4.58 MB)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n",
-      "Epoch 1/20\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-13 17:59:09.869544: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m110s\u001B[0m 229ms/step - accuracy: 0.1052 - loss: 2.3000 - val_accuracy: 0.2693 - val_loss: 2.2488\n",
-      "Epoch 2/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 271ms/step - accuracy: 0.2265 - loss: 2.2399 - val_accuracy: 0.4505 - val_loss: 2.1755\n",
-      "Epoch 3/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 215ms/step - accuracy: 0.3401 - loss: 2.1674 - val_accuracy: 0.5460 - val_loss: 2.0756\n",
-      "Epoch 4/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m98s\u001B[0m 208ms/step - accuracy: 0.4168 - loss: 2.0707 - val_accuracy: 0.6309 - val_loss: 1.9400\n",
-      "Epoch 5/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m101s\u001B[0m 216ms/step - accuracy: 0.4859 - loss: 1.9388 - val_accuracy: 0.7004 - val_loss: 1.7650\n",
-      "Epoch 6/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m148s\u001B[0m 228ms/step - accuracy: 0.5514 - loss: 1.7740 - val_accuracy: 0.7517 - val_loss: 1.5543\n",
-      "Epoch 7/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 225ms/step - accuracy: 0.5989 - loss: 1.5901 - val_accuracy: 0.7860 - val_loss: 1.3342\n",
-      "Epoch 8/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m141s\u001B[0m 224ms/step - accuracy: 0.6395 - loss: 1.4047 - val_accuracy: 0.8053 - val_loss: 1.1347\n",
-      "Epoch 9/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m106s\u001B[0m 226ms/step - accuracy: 0.6621 - loss: 1.2518 - val_accuracy: 0.8213 - val_loss: 0.9769\n",
-      "Epoch 10/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m118s\u001B[0m 252ms/step - accuracy: 0.6848 - loss: 1.1299 - val_accuracy: 0.8310 - val_loss: 0.8569\n",
-      "Epoch 11/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m140s\u001B[0m 298ms/step - accuracy: 0.7070 - loss: 1.0295 - val_accuracy: 0.8393 - val_loss: 0.7659\n",
-      "Epoch 12/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m139s\u001B[0m 297ms/step - accuracy: 0.7191 - loss: 0.9594 - val_accuracy: 0.8467 - val_loss: 0.6961\n",
-      "Epoch 13/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m139s\u001B[0m 297ms/step - accuracy: 0.7353 - loss: 0.8954 - val_accuracy: 0.8541 - val_loss: 0.6418\n",
-      "Epoch 14/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m141s\u001B[0m 301ms/step - accuracy: 0.7501 - loss: 0.8407 - val_accuracy: 0.8598 - val_loss: 0.5981\n",
-      "Epoch 15/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m145s\u001B[0m 309ms/step - accuracy: 0.7608 - loss: 0.8010 - val_accuracy: 0.8637 - val_loss: 0.5631\n",
-      "Epoch 16/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m140s\u001B[0m 299ms/step - accuracy: 0.7708 - loss: 0.7630 - val_accuracy: 0.8675 - val_loss: 0.5345\n",
-      "Epoch 17/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m140s\u001B[0m 294ms/step - accuracy: 0.7818 - loss: 0.7294 - val_accuracy: 0.8720 - val_loss: 0.5091\n",
-      "Epoch 18/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m136s\u001B[0m 290ms/step - accuracy: 0.7874 - loss: 0.7058 - val_accuracy: 0.8745 - val_loss: 0.4890\n",
-      "Epoch 19/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m141s\u001B[0m 301ms/step - accuracy: 0.7911 - loss: 0.6918 - val_accuracy: 0.8777 - val_loss: 0.4711\n",
-      "Epoch 20/20\n",
-      "\u001B[1m469/469\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m144s\u001B[0m 306ms/step - accuracy: 0.7922 - loss: 0.6763 - val_accuracy: 0.8803 - val_loss: 0.4562\n",
-      "Test loss: 0.4561978578567505\n",
-      "Test accuracy: 0.880299985408783\n"
-     ]
-    }
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-   "execution_count": 19
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-   "source": "model.save(\"mnist_simple_cnn.h5\")",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
-     ]
-    }
-   ],
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-   "source": "score = model.evaluate(x_test, y_test, verbose=0)\n",
-   "outputs": [],
-   "execution_count": 22
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/4.2 - Image Classifier - CIFAR10.ipynb

@@ -1,167 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's run a simple image classifier, using the CIFAR 10 dataset of 10 image categories\n",
-    "* CIFAR's 10 categories:\n",
-    " * airplane\n",
-    " * automobile\n",
-    " * bird\n",
-    " * cat\n",
-    " * deer\n",
-    " * dog\n",
-    " * frog\n",
-    " * horse\n",
-    " * ship\n",
-    " * truck"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T16:11:52.999533Z",
-     "start_time": "2025-03-13T16:11:47.171043Z"
-    }
-   },
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.models import load_model\n",
-    "from tensorflow.keras.datasets import cifar10 \n",
-    "\n",
-    "img_row, img_height, img_depth = 32,32,3\n",
-    "\n",
-    "classifier = load_model('cifar_simple_cnn.h5')\n",
-    "\n",
-    "# Loads the CIFAR dataset\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "color = True \n",
-    "scale = 8\n",
-    "\n",
-    "def draw_test(name, res, input_im, scale, img_row, img_height):\n",
-    "    BLACK = [0,0,0]\n",
-    "    res = int(res)\n",
-    "    if res == 0:\n",
-    "        pred = \"airplane\"\n",
-    "    if res == 1:\n",
-    "        pred = \"automobile\"\n",
-    "    if res == 2:\n",
-    "        pred = \"bird\"\n",
-    "    if res == 3:\n",
-    "        pred = \"cat\"\n",
-    "    if res == 4:\n",
-    "        pred = \"deer\"\n",
-    "    if res == 5:\n",
-    "        pred = \"dog\"\n",
-    "    if res == 6:\n",
-    "        pred = \"frog\"\n",
-    "    if res == 7:\n",
-    "        pred = \"horse\"\n",
-    "    if res == 8:\n",
-    "        pred = \"ship\"\n",
-    "    if res == 9:\n",
-    "        pred = \"truck\"\n",
-    "        \n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0]*2 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    if color == False:\n",
-    "        expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (300, 80) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "    imageL = cv2.resize(input_im, None, fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC) \n",
-    "    input_im = input_im.reshape(1,img_row, img_height, img_depth) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(classifier.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "    \n",
-    "    draw_test(\"Prediction\", res, imageL, scale, img_row, img_height) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ],
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-03-13 21:41:47.742478: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-03-13 21:41:47.746738: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
-      "2025-03-13 21:41:47.759288: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "E0000 00:00:1741882307.783990   24522 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "E0000 00:00:1741882307.790137   24522 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2025-03-13 21:41:47.814207: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "/home/newton/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Kernel shape must have the same length as input, but received kernel of shape (3, 3, 3, 32) and input of shape (None, None, 32, 32, 3).",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[1], line 8\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtensorflow\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mkeras\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdatasets\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m cifar10 \n\u001B[1;32m      6\u001B[0m img_row, img_height, img_depth \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m32\u001B[39m,\u001B[38;5;241m32\u001B[39m,\u001B[38;5;241m3\u001B[39m\n\u001B[0;32m----> 8\u001B[0m classifier \u001B[38;5;241m=\u001B[39m load_model(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcifar_simple_cnn.h5\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m     10\u001B[0m \u001B[38;5;66;03m# Loads the CIFAR dataset\u001B[39;00m\n\u001B[1;32m     11\u001B[0m (x_train, y_train), (x_test, y_test) \u001B[38;5;241m=\u001B[39m cifar10\u001B[38;5;241m.\u001B[39mload_data()\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/saving/saving_api.py:196\u001B[0m, in \u001B[0;36mload_model\u001B[0;34m(filepath, custom_objects, compile, safe_mode)\u001B[0m\n\u001B[1;32m    189\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m saving_lib\u001B[38;5;241m.\u001B[39mload_model(\n\u001B[1;32m    190\u001B[0m         filepath,\n\u001B[1;32m    191\u001B[0m         custom_objects\u001B[38;5;241m=\u001B[39mcustom_objects,\n\u001B[1;32m    192\u001B[0m         \u001B[38;5;28mcompile\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mcompile\u001B[39m,\n\u001B[1;32m    193\u001B[0m         safe_mode\u001B[38;5;241m=\u001B[39msafe_mode,\n\u001B[1;32m    194\u001B[0m     )\n\u001B[1;32m    195\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mstr\u001B[39m(filepath)\u001B[38;5;241m.\u001B[39mendswith((\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.h5\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.hdf5\u001B[39m\u001B[38;5;124m\"\u001B[39m)):\n\u001B[0;32m--> 196\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m legacy_h5_format\u001B[38;5;241m.\u001B[39mload_model_from_hdf5(\n\u001B[1;32m    197\u001B[0m         filepath, custom_objects\u001B[38;5;241m=\u001B[39mcustom_objects, \u001B[38;5;28mcompile\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mcompile\u001B[39m\n\u001B[1;32m    198\u001B[0m     )\n\u001B[1;32m    199\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mstr\u001B[39m(filepath)\u001B[38;5;241m.\u001B[39mendswith(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.keras\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m    200\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m    201\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFile not found: filepath=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfilepath\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m. \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    202\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPlease ensure the file is an accessible `.keras` \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    203\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mzip file.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    204\u001B[0m     )\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/legacy/saving/legacy_h5_format.py:133\u001B[0m, in \u001B[0;36mload_model_from_hdf5\u001B[0;34m(filepath, custom_objects, compile)\u001B[0m\n\u001B[1;32m    130\u001B[0m model_config \u001B[38;5;241m=\u001B[39m json_utils\u001B[38;5;241m.\u001B[39mdecode(model_config)\n\u001B[1;32m    132\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m saving_options\u001B[38;5;241m.\u001B[39mkeras_option_scope(use_legacy_config\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[0;32m--> 133\u001B[0m     model \u001B[38;5;241m=\u001B[39m saving_utils\u001B[38;5;241m.\u001B[39mmodel_from_config(\n\u001B[1;32m    134\u001B[0m         model_config, custom_objects\u001B[38;5;241m=\u001B[39mcustom_objects\n\u001B[1;32m    135\u001B[0m     )\n\u001B[1;32m    137\u001B[0m     \u001B[38;5;66;03m# set weights\u001B[39;00m\n\u001B[1;32m    138\u001B[0m     load_weights_from_hdf5_group(f[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel_weights\u001B[39m\u001B[38;5;124m\"\u001B[39m], model)\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/legacy/saving/saving_utils.py:85\u001B[0m, in \u001B[0;36mmodel_from_config\u001B[0;34m(config, custom_objects)\u001B[0m\n\u001B[1;32m     81\u001B[0m \u001B[38;5;66;03m# TODO(nkovela): Swap find and replace args during Keras 3.0 release\u001B[39;00m\n\u001B[1;32m     82\u001B[0m \u001B[38;5;66;03m# Replace keras refs with keras\u001B[39;00m\n\u001B[1;32m     83\u001B[0m config \u001B[38;5;241m=\u001B[39m _find_replace_nested_dict(config, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkeras.\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkeras.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m---> 85\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m serialization\u001B[38;5;241m.\u001B[39mdeserialize_keras_object(\n\u001B[1;32m     86\u001B[0m     config,\n\u001B[1;32m     87\u001B[0m     module_objects\u001B[38;5;241m=\u001B[39mMODULE_OBJECTS\u001B[38;5;241m.\u001B[39mALL_OBJECTS,\n\u001B[1;32m     88\u001B[0m     custom_objects\u001B[38;5;241m=\u001B[39mcustom_objects,\n\u001B[1;32m     89\u001B[0m     printable_module_name\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlayer\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m     90\u001B[0m )\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/legacy/saving/serialization.py:495\u001B[0m, in \u001B[0;36mdeserialize_keras_object\u001B[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001B[0m\n\u001B[1;32m    490\u001B[0m cls_config \u001B[38;5;241m=\u001B[39m _find_replace_nested_dict(\n\u001B[1;32m    491\u001B[0m     cls_config, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkeras.\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkeras.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    492\u001B[0m )\n\u001B[1;32m    494\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcustom_objects\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m arg_spec\u001B[38;5;241m.\u001B[39margs:\n\u001B[0;32m--> 495\u001B[0m     deserialized_obj \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mfrom_config(\n\u001B[1;32m    496\u001B[0m         cls_config,\n\u001B[1;32m    497\u001B[0m         custom_objects\u001B[38;5;241m=\u001B[39m{\n\u001B[1;32m    498\u001B[0m             \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mobject_registration\u001B[38;5;241m.\u001B[39mGLOBAL_CUSTOM_OBJECTS,\n\u001B[1;32m    499\u001B[0m             \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mcustom_objects,\n\u001B[1;32m    500\u001B[0m         },\n\u001B[1;32m    501\u001B[0m     )\n\u001B[1;32m    502\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    503\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m object_registration\u001B[38;5;241m.\u001B[39mCustomObjectScope(custom_objects):\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/models/sequential.py:359\u001B[0m, in \u001B[0;36mSequential.from_config\u001B[0;34m(cls, config, custom_objects)\u001B[0m\n\u001B[1;32m    354\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    355\u001B[0m         layer \u001B[38;5;241m=\u001B[39m serialization_lib\u001B[38;5;241m.\u001B[39mdeserialize_keras_object(\n\u001B[1;32m    356\u001B[0m             layer_config,\n\u001B[1;32m    357\u001B[0m             custom_objects\u001B[38;5;241m=\u001B[39mcustom_objects,\n\u001B[1;32m    358\u001B[0m         )\n\u001B[0;32m--> 359\u001B[0m     model\u001B[38;5;241m.\u001B[39madd(layer)\n\u001B[1;32m    360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[1;32m    361\u001B[0m     \u001B[38;5;129;01mnot\u001B[39;00m model\u001B[38;5;241m.\u001B[39m_functional\n\u001B[1;32m    362\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbuild_input_shape\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mlocals\u001B[39m()\n\u001B[1;32m    363\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m build_input_shape\n\u001B[1;32m    364\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(build_input_shape, (\u001B[38;5;28mtuple\u001B[39m, \u001B[38;5;28mlist\u001B[39m))\n\u001B[1;32m    365\u001B[0m ):\n\u001B[1;32m    366\u001B[0m     model\u001B[38;5;241m.\u001B[39mbuild(build_input_shape)\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/models/sequential.py:122\u001B[0m, in \u001B[0;36mSequential.add\u001B[0;34m(self, layer, rebuild)\u001B[0m\n\u001B[1;32m    120\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers\u001B[38;5;241m.\u001B[39mappend(layer)\n\u001B[1;32m    121\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m rebuild:\n\u001B[0;32m--> 122\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_maybe_rebuild()\n\u001B[1;32m    123\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    124\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbuilt \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/models/sequential.py:141\u001B[0m, in \u001B[0;36mSequential._maybe_rebuild\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    139\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers[\u001B[38;5;241m0\u001B[39m], InputLayer) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m    140\u001B[0m     input_shape \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mbatch_shape\n\u001B[0;32m--> 141\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbuild(input_shape)\n\u001B[1;32m    142\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers[\u001B[38;5;241m0\u001B[39m], \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput_shape\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m    143\u001B[0m     \u001B[38;5;66;03m# We can build the Sequential model if the first layer has the\u001B[39;00m\n\u001B[1;32m    144\u001B[0m     \u001B[38;5;66;03m# `input_shape` property. This is most commonly found in Functional\u001B[39;00m\n\u001B[1;32m    145\u001B[0m     \u001B[38;5;66;03m# model.\u001B[39;00m\n\u001B[1;32m    146\u001B[0m     input_shape \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39minput_shape\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/layers/layer.py:228\u001B[0m, in \u001B[0;36mLayer.__new__.<locals>.build_wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    226\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m obj\u001B[38;5;241m.\u001B[39m_open_name_scope():\n\u001B[1;32m    227\u001B[0m     obj\u001B[38;5;241m.\u001B[39m_path \u001B[38;5;241m=\u001B[39m current_path()\n\u001B[0;32m--> 228\u001B[0m     original_build_method(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m    229\u001B[0m \u001B[38;5;66;03m# Record build config.\u001B[39;00m\n\u001B[1;32m    230\u001B[0m signature \u001B[38;5;241m=\u001B[39m inspect\u001B[38;5;241m.\u001B[39msignature(original_build_method)\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/models/sequential.py:187\u001B[0m, in \u001B[0;36mSequential.build\u001B[0;34m(self, input_shape)\u001B[0m\n\u001B[1;32m    185\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m layer \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_layers[\u001B[38;5;241m1\u001B[39m:]:\n\u001B[1;32m    186\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 187\u001B[0m         x \u001B[38;5;241m=\u001B[39m layer(x)\n\u001B[1;32m    188\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m:\n\u001B[1;32m    189\u001B[0m         \u001B[38;5;66;03m# Can happen if shape inference is not implemented.\u001B[39;00m\n\u001B[1;32m    190\u001B[0m         \u001B[38;5;66;03m# TODO: consider reverting inbound nodes on layers processed.\u001B[39;00m\n\u001B[1;32m    191\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:122\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    119\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[1;32m    120\u001B[0m     \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[1;32m    121\u001B[0m     \u001B[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001B[39;00m\n\u001B[0;32m--> 122\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m    123\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m    124\u001B[0m     \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
-      "File \u001B[0;32m~/miniconda3/envs/dl/lib/python3.12/site-packages/keras/src/ops/operation_utils.py:184\u001B[0m, in \u001B[0;36mcompute_conv_output_shape\u001B[0;34m(input_shape, filters, kernel_size, strides, padding, data_format, dilation_rate)\u001B[0m\n\u001B[1;32m    182\u001B[0m     kernel_shape \u001B[38;5;241m=\u001B[39m kernel_size \u001B[38;5;241m+\u001B[39m (input_shape[\u001B[38;5;241m1\u001B[39m], filters)\n\u001B[1;32m    183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(kernel_shape) \u001B[38;5;241m!=\u001B[39m \u001B[38;5;28mlen\u001B[39m(input_shape):\n\u001B[0;32m--> 184\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m    185\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mKernel shape must have the same length as input, but received \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    186\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mkernel of shape \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkernel_shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m and \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    187\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput of shape \u001B[39m\u001B[38;5;132;01m{\u001B[39;00minput_shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    188\u001B[0m     )\n\u001B[1;32m    189\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(dilation_rate, \u001B[38;5;28mint\u001B[39m):\n\u001B[1;32m    190\u001B[0m     dilation_rate \u001B[38;5;241m=\u001B[39m (dilation_rate,) \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mlen\u001B[39m(spatial_shape)\n",
-      "\u001B[0;31mValueError\u001B[0m: Kernel shape must have the same length as input, but received kernel of shape (3, 3, 3, 32) and input of shape (None, None, 32, 32, 3)."
-     ]
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
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-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
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- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/4.3. Live Sketching.ipynb

@@ -1,126 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T12:18:54.614963Z",
-     "start_time": "2025-03-13T12:18:52.126661Z"
-    }
-   },
-   "source": [
-    "#import keras\n",
-    "import cv2\n",
-    "import numpy as np\n",
-    "import matplotlib\n",
-    "print (cv2.__version__)"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.11.0\n"
-     ]
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T12:25:16.378140Z",
-     "start_time": "2025-03-13T12:25:15.608514Z"
-    }
-   },
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "# Our sketch generating function\n",
-    "def sketch(image):\n",
-    "    # Convert image to grayscale\n",
-    "    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    \n",
-    "    # Clean up image using Guassian Blur\n",
-    "    img_gray_blur = cv2.GaussianBlur(img_gray, (5,5), 0)\n",
-    "    \n",
-    "    # Extract edges\n",
-    "    canny_edges = cv2.Canny(img_gray_blur, 20, 50)\n",
-    "    \n",
-    "    # Do an invert binarize the image \n",
-    "    ret, mask = cv2.threshold(canny_edges, 70, 255, cv2.THRESH_BINARY_INV)\n",
-    "    return mask\n",
-    "\n",
-    "\n",
-    "# Initialize webcam, cap is the object provided by VideoCapture\n",
-    "# It contains a boolean indicating if it was sucessful (ret)\n",
-    "# It also contains the images collected from the webcam (frame)\n",
-    "cap = cv2.VideoCapture(2)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    cv2.imshow('Our Live Sketcher', sketch(frame))\n",
-    "    if cv2.waitKey(1) == 13 or cv2.waitKey(1) == 27: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "# Release camera and close windows\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ],
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[ WARN:0@382.189] global cap_v4l.cpp:913 open VIDEOIO(V4L2:/dev/video2): can't open camera by index\n",
-      "[ERROR:0@382.862] global obsensor_uvc_stream_channel.cpp:158 getStreamChannelGroup Camera index out of range\n",
-      "[ WARN:0@382.866] global cap_v4l.cpp:803 requestBuffers VIDEOIO(V4L2:/dev/video2): failed VIDIOC_REQBUFS: errno=19 (No such device)\n"
-     ]
-    },
-    {
-     "ename": "error",
-     "evalue": "OpenCV(4.11.0) /io/opencv/modules/imgproc/src/color.cpp:199: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31merror\u001B[0m                                     Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[6], line 27\u001B[0m\n\u001B[1;32m     25\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m     26\u001B[0m     ret, frame \u001B[38;5;241m=\u001B[39m cap\u001B[38;5;241m.\u001B[39mread()\n\u001B[0;32m---> 27\u001B[0m     cv2\u001B[38;5;241m.\u001B[39mimshow(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mOur Live Sketcher\u001B[39m\u001B[38;5;124m'\u001B[39m, sketch(frame))\n\u001B[1;32m     28\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m cv2\u001B[38;5;241m.\u001B[39mwaitKey(\u001B[38;5;241m1\u001B[39m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m13\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m cv2\u001B[38;5;241m.\u001B[39mwaitKey(\u001B[38;5;241m1\u001B[39m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m27\u001B[39m: \u001B[38;5;66;03m#13 is the Enter Key\u001B[39;00m\n\u001B[1;32m     29\u001B[0m         \u001B[38;5;28;01mbreak\u001B[39;00m\n",
-      "Cell \u001B[0;32mIn[6], line 7\u001B[0m, in \u001B[0;36msketch\u001B[0;34m(image)\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21msketch\u001B[39m(image):\n\u001B[1;32m      6\u001B[0m     \u001B[38;5;66;03m# Convert image to grayscale\u001B[39;00m\n\u001B[0;32m----> 7\u001B[0m     img_gray \u001B[38;5;241m=\u001B[39m cv2\u001B[38;5;241m.\u001B[39mcvtColor(image, cv2\u001B[38;5;241m.\u001B[39mCOLOR_BGR2GRAY)\n\u001B[1;32m      9\u001B[0m     \u001B[38;5;66;03m# Clean up image using Guassian Blur\u001B[39;00m\n\u001B[1;32m     10\u001B[0m     img_gray_blur \u001B[38;5;241m=\u001B[39m cv2\u001B[38;5;241m.\u001B[39mGaussianBlur(img_gray, (\u001B[38;5;241m5\u001B[39m,\u001B[38;5;241m5\u001B[39m), \u001B[38;5;241m0\u001B[39m)\n",
-      "\u001B[0;31merror\u001B[0m: OpenCV(4.11.0) /io/opencv/modules/imgproc/src/color.cpp:199: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'\n"
-     ]
-    }
-   ],
-   "execution_count": 6
-  },
-  {
-   "cell_type": "code",
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-   "pygments_lexer": "ipython3",
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-  }
- },
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- "nbformat_minor": 2
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4. Get Started! Handwritting Recognition, Simple Object Classification & OpenCV Demo/Test - Imports Keras, OpenCV and tests webcam.ipynb

@@ -1,122 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-03-13T12:26:00.769321Z",
-     "start_time": "2025-03-13T12:25:51.814601Z"
-    }
-   },
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "\n",
-    "# Our sketch generating function\n",
-    "def sketch(image):\n",
-    "    # Convert image to grayscale\n",
-    "    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
-    "    \n",
-    "    # Clean up image using Gaussian Blur\n",
-    "    img_gray_blur = cv2.GaussianBlur(img_gray, (5,5), 0)\n",
-    "    \n",
-    "    # Extract edges\n",
-    "    canny_edges = cv2.Canny(img_gray_blur, 10, 70)\n",
-    "    \n",
-    "    # Do an invert binarize the image \n",
-    "    ret, mask = cv2.threshold(canny_edges, 70, 255, cv2.THRESH_BINARY_INV)\n",
-    "    return mask\n",
-    "\n",
-    "\n",
-    "# Initialize webcam, cap is the object provided by VideoCapture\n",
-    "# It contains a boolean indicating if it was sucessful (ret)\n",
-    "# It also contains the images collected from the webcam (frame)\n",
-    "cap = cv2.VideoCapture(3)\n",
-    "\n",
-    "while True:\n",
-    "    ret, frame = cap.read()\n",
-    "    cv2.imshow('Our Live Sketcher', sketch(frame))\n",
-    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
-    "        break\n",
-    "        \n",
-    "# Release camera and close windows\n",
-    "cap.release()\n",
-    "cv2.destroyAllWindows()      "
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "code",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-24T14:10:10.316586Z",
-     "start_time": "2025-02-24T14:10:10.209728Z"
-    }
-   },
-   "source": "cap = cv2.VideoCapture(0)",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[ WARN:0@1028.418] global cap_v4l.cpp:913 open VIDEOIO(V4L2:/dev/video0): can't open camera by index\n",
-      "[ERROR:0@1028.522] global obsensor_uvc_stream_channel.cpp:158 getStreamChannelGroup Camera index out of range\n"
-     ]
-    }
-   ],
-   "execution_count": 24
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-24T14:10:11.179764Z",
-     "start_time": "2025-02-24T14:10:11.162834Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "cap.read()\n",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(False, None)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 25
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "cv2.imshow('Our Live Sketcher', sketch(frame))"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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8. Making a CNN in Keras/8.11 - Building a CNN for Image Classification - CIFAR10.ipynb

@@ -1,379 +1,3 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The CIFAR-10 Dataset\n",
-    "* Contains 10 categories of images\n",
-    " * airplane\n",
-    " * automobile\n",
-    " * bird\n",
-    " * cat\n",
-    " * deer\n",
-    " * dog\n",
-    " * frog\n",
-    " * horse\n",
-    " * ship\n",
-    " * truck"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's Begin training out model for CIFAR-10 using a deeper CNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x_train shape: (50000, 32, 32, 3)\n",
-      "50000 train samples\n",
-      "10000 test samples\n",
-      "Model: \"sequential_5\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_20 (Conv2D)           (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_30 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_21 (Conv2D)           (None, 30, 30, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_31 (Activation)   (None, 30, 30, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_10 (MaxPooling (None, 15, 15, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_15 (Dropout)         (None, 15, 15, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_22 (Conv2D)           (None, 15, 15, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_32 (Activation)   (None, 15, 15, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_23 (Conv2D)           (None, 13, 13, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_33 (Activation)   (None, 13, 13, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_11 (MaxPooling (None, 6, 6, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_16 (Dropout)         (None, 6, 6, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "flatten_5 (Flatten)          (None, 2304)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_10 (Dense)             (None, 512)               1180160   \n",
-      "_________________________________________________________________\n",
-      "activation_34 (Activation)   (None, 512)               0         \n",
-      "_________________________________________________________________\n",
-      "dropout_17 (Dropout)         (None, 512)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_11 (Dense)             (None, 10)                5130      \n",
-      "_________________________________________________________________\n",
-      "activation_35 (Activation)   (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 1,250,858\n",
-      "Trainable params: 1,250,858\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.datasets import cifar10\n",
-    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
-    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
-    "from tensorflow.keras.models import load_model\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import os\n",
-    "\n",
-    "batch_size = 32\n",
-    "num_classes = 10\n",
-    "epochs = 1\n",
-    "\n",
-    "# Loads the CIFAR dataset\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "\n",
-    "# Display our data shape/dimensions\n",
-    "print('x_train shape:', x_train.shape)\n",
-    "print(x_train.shape[0], 'train samples')\n",
-    "print(x_test.shape[0], 'test samples')\n",
-    "\n",
-    "# Format our training data by Normalizing and changing data type\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "x_train /= 255\n",
-    "x_test /= 255\n",
-    "\n",
-    "# Now we one hot encode outputs\n",
-    "y_train = to_categorical(y_train)\n",
-    "y_test = to_categorical(y_test)\n",
-    "\n",
-    "model = Sequential()\n",
-    "# Padding = 'same'  results in padding the input such that\n",
-    "# the output has the same length as the original input\n",
-    "model.add(Conv2D(32, (3, 3), padding='same',\n",
-    "                 input_shape=x_train.shape[1:]))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Conv2D(32, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "\n",
-    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Conv2D(64, (3, 3)))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
-    "model.add(Dropout(0.25))\n",
-    "\n",
-    "model.add(Flatten())\n",
-    "model.add(Dense(512))\n",
-    "model.add(Activation('relu'))\n",
-    "model.add(Dropout(0.5))\n",
-    "model.add(Dense(num_classes))\n",
-    "model.add(Activation('softmax'))\n",
-    "\n",
-    "# initiate RMSprop optimizer and configure some parameters\n",
-    "opt = tf.keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)\n",
-    "\n",
-    "# Let's create our model\n",
-    "model.compile(loss = 'categorical_crossentropy',\n",
-    "              optimizer = opt,\n",
-    "              metrics = ['accuracy'])\n",
-    "\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Training Our Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "50000/50000 [==============================] - 174s 3ms/sample - loss: 1.4921 - accuracy: 0.4595 - val_loss: 1.5935 - val_accuracy: 0.4478\n",
-      "10000/10000 [==============================] - 5s 490us/sample - loss: 1.5935 - accuracy: 0.4478\n",
-      "Test loss: 1.5935393712997437\n",
-      "Test accuracy: 0.4478\n"
-     ]
-    }
-   ],
-   "source": [
-    "history = model.fit(x_train, y_train,\n",
-    "          batch_size=batch_size,\n",
-    "          epochs=epochs,\n",
-    "          validation_data=(x_test, y_test),\n",
-    "          shuffle=True)\n",
-    "\n",
-    "model.save(\"cifar_simple_cnn_2.h5\")\n",
-    "\n",
-    "# Evaluate the performance of our trained model\n",
-    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
-    "print('Test loss:', scores[0])\n",
-    "print('Test accuracy:', scores[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting our Accuracy and Loss Charts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our loss charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "loss_values = history_dict['loss']\n",
-    "val_loss_values = history_dict['val_loss']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_loss_values, label='Validation/Test Loss')\n",
-    "line2 = plt.plot(epochs, loss_values, label='Training Loss')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Loss')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting our accuracy charts\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "history_dict = history.history\n",
-    "\n",
-    "acc_values = history_dict['accuracy']\n",
-    "val_acc_values = history_dict['val_accuracy']\n",
-    "epochs = range(1, len(loss_values) + 1)\n",
-    "\n",
-    "line1 = plt.plot(epochs, val_acc_values, label='Validation/Test Accuracy')\n",
-    "line2 = plt.plot(epochs, acc_values, label='Training Accuracy')\n",
-    "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
-    "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
-    "plt.xlabel('Epochs') \n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.grid(True)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's run some tests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cv2\n",
-    "import numpy as np\n",
-    "from tensorflow.keras.models import load_model\n",
-    "\n",
-    "img_row, img_height, img_depth = 32,32,3\n",
-    "classifier = load_model('cifar_simple_cnn_2.h5')\n",
-    "color = True \n",
-    "scale = 8\n",
-    "\n",
-    "def draw_test(name, res, input_im, scale, img_row, img_height):\n",
-    "    BLACK = [0,0,0]\n",
-    "    res = int(res)\n",
-    "    if res == 0:\n",
-    "        pred = \"airplane\"\n",
-    "    if res == 1:\n",
-    "        pred = \"automobile\"\n",
-    "    if res == 2:\n",
-    "        pred = \"bird\"\n",
-    "    if res == 3:\n",
-    "        pred = \"cat\"\n",
-    "    if res == 4:\n",
-    "        pred = \"deer\"\n",
-    "    if res == 5:\n",
-    "        pred = \"dog\"\n",
-    "    if res == 6:\n",
-    "        pred = \"frog\"\n",
-    "    if res == 7:\n",
-    "        pred = \"horse\"\n",
-    "    if res == 8:\n",
-    "        pred = \"ship\"\n",
-    "    if res == 9:\n",
-    "        pred = \"truck\"\n",
-    "        \n",
-    "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0]*2 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
-    "    if color == False:\n",
-    "        expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
-    "    cv2.putText(expanded_image, str(pred), (300, 80) , cv2.FONT_HERSHEY_COMPLEX_SMALL,3, (0,255,0), 2)\n",
-    "    cv2.imshow(name, expanded_image)\n",
-    "\n",
-    "\n",
-    "for i in range(0,10):\n",
-    "    rand = np.random.randint(0,len(x_test))\n",
-    "    input_im = x_test[rand]\n",
-    "    imageL = cv2.resize(input_im, None, fx=scale, fy=scale, interpolation = cv2.INTER_CUBIC) \n",
-    "    input_im = input_im.reshape(1,img_row, img_height, img_depth) \n",
-    "    \n",
-    "    ## Get Prediction\n",
-    "    res = str(classifier.predict_classes(input_im, 1, verbose = 0)[0])\n",
-    "              \n",
-    "    draw_test(\"Prediction\", res, imageL, scale, img_row, img_height) \n",
-    "    cv2.waitKey(0)\n",
-    "\n",
-    "cv2.destroyAllWindows()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
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