Updated cnn

CNN.ipynb

@@ -2,31 +2,275 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Dataset Building Imports\n",
-    "from requests import exceptions\n",
-    "import argparse\n",
-    "import requests\n",
-    "import cv2\n",
-    "import os"
+    "# File renaming for consistency\n",
+    "import os\n",
+    "from os import path\n",
+    "\n",
+    "for count, filename in enumerate(os.listdir(\"deadlift2\")):\n",
+    "    src = \"deadlift2/\" + filename\n",
+    "    string = \"deadlift2/deadlift_\" + str(count) + \".jpg\"\n",
+    "    os.rename(src, string)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-photos-of-dogs-and-cats/"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(549,) ()\n"
+     ]
+    }
+   ],
+   "source": [
+    "from os import listdir\n",
+    "from numpy import asarray\n",
+    "from numpy import save\n",
+    "from keras.preprocessing.image import load_img\n",
+    "from keras.preprocessing.image import img_to_array\n",
+    "\n",
+    "folder = \"train/\"\n",
+    "photos, labels = list(), list()\n",
+    "\n",
+    "for file in listdir(folder):\n",
+    "    output = 0.0\n",
+    "    if file.startswith(\"squat\"):\n",
+    "        output = 1.0\n",
+    "    if file.startswith(\"deadlift\"):\n",
+    "        output = 2.0\n",
+    "    photo = load_img(folder + file, target_size=(150,150))\n",
+    "    photo = img_to_array\n",
+    "    \n",
+    "    photos.append(photo)\n",
+    "    labels.append(output)\n",
+    "photos = asarray(photos)\n",
+    "labels = asarray(output)\n",
+    "print(photos.shape, labels.shape)\n",
+    "\n",
+    "save(\"exercise_photos.npy\", photos)\n",
+    "save(\"exercise_labels.npy\", photos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(549,) (549,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from numpy import load\n",
+    "photos = load(\"exercise_photos.npy\",allow_pickle=True)\n",
+    "labels = load(\"exercise_labels.npy\",allow_pickle=True)\n",
+    "\n",
+    "print(photos.shape, labels.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Directory Generation\n",
+    "from os import makedirs\n",
+    "dataset_home = \"dataset/\"\n",
+    "subdirs = [\"train/\", \"test/\"]\n",
+    "for subdir in subdirs:\n",
+    "    # create label subdirectories\n",
+    "    labeldirs = [\"bench/\", \"squat/\", \"deadlift/\"]\n",
+    "    for labldir in labeldirs:\n",
+    "        newdir = dataset_home + subdir + labldir\n",
+    "        makedirs(newdir)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Segment into testing and training images\n",
+    "import random\n",
+    "from shutil import copyfile\n",
+    "random.seed(1)\n",
+    "ratio = 0.2\n",
+    "dataset_home = \"dataset/\"\n",
+    "src_directory = \"images/\"\n",
+    "for file in listdir(src_directory):\n",
+    "    src = src_directory + '/' + file\n",
+    "    dst_dir = \"train/\"\n",
+    "    if random.random() < ratio:\n",
+    "        dst_dir = \"test/\"\n",
+    "    if file.startswith(\"bench\"):\n",
+    "        dst = dataset_home + dst_dir + \"bench/\" + file\n",
+    "    elif file.startswith(\"squat\"):\n",
+    "        dst = dataset_home + dst_dir + \"squat/\" + file\n",
+    "    else:\n",
+    "        dst = dataset_home + dst_dir + \"deadlift/\" + file\n",
+    "    copyfile(src, dst)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 448 images belonging to 3 classes.\n",
+      "Found 101 images belonging to 3 classes.\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "in user code:\n\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:806 train_function  *\n        return step_function(self, iterator)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1211 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2585 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2945 _call_for_each_replica\n        return fn(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:789 run_step  **\n        outputs = model.train_step(data)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:748 train_step\n        loss = self.compiled_loss(\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\compile_utils.py:204 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:149 __call__\n        losses = ag_call(y_true, y_pred)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:253 call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:201 wrapper\n        return target(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:1535 categorical_crossentropy\n        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:201 wrapper\n        return target(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:4687 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_shape.py:1134 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-58-08697642c99a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;31m# entry point, run the test harness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 101\u001b[1;33m \u001b[0mrun_test_harness\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[1;32m<ipython-input-58-08697642c99a>\u001b[0m in \u001b[0;36mrun_test_harness\u001b[1;34m()\u001b[0m\n\u001b[0;32m     90\u001b[0m         class_mode='binary', batch_size=64, target_size=(150, 150))\n\u001b[0;32m     91\u001b[0m     \u001b[1;31m# fit model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m     history = model.fit(train_it, steps_per_epoch=len(train_it),\n\u001b[0m\u001b[0;32m     93\u001b[0m         validation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=0)\n\u001b[0;32m     94\u001b[0m     \u001b[1;31m# evaluate model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\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;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\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;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    821\u001b[0m       \u001b[1;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    822\u001b[0m       \u001b[0minitializers\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[1;32m--> 823\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitializers\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    824\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    825\u001b[0m       \u001b[1;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    694\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_deleter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFunctionDeleter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lifted_initializer_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    695\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m--> 696\u001b[1;33m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m    697\u001b[0m             *args, **kwds))\n\u001b[0;32m    698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2853\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2854\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2855\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   2856\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2857\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3212\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3213\u001b[1;33m       \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   3214\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3215\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3063\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3064\u001b[0m     graph_function = ConcreteFunction(\n\u001b[1;32m-> 3065\u001b[1;33m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[0;32m   3066\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3067\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m    984\u001b[0m         \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    985\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 986\u001b[1;33m       \u001b[0mfunc_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\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    987\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    988\u001b[0m       \u001b[1;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    598\u001b[0m         \u001b[1;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    599\u001b[0m         \u001b[1;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\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    601\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\python38-64\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    971\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint:disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ag_error_metadata\"\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--> 973\u001b[1;33m               \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\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    974\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m               \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:806 train_function  *\n        return step_function(self, iterator)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:796 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:1211 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2585 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2945 _call_for_each_replica\n        return fn(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:789 run_step  **\n        outputs = model.train_step(data)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:748 train_step\n        loss = self.compiled_loss(\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\compile_utils.py:204 __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:149 __call__\n        losses = ag_call(y_true, y_pred)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:253 call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:201 wrapper\n        return target(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:1535 categorical_crossentropy\n        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:201 wrapper\n        return target(*args, **kwargs)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:4687 categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n    c:\\python38-64\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_shape.py:1134 assert_is_compatible_with\n        raise ValueError(\"Shapes %s and %s are incompatible\" % (self, other))\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Baseline CNN Model\n",
+    "import sys\n",
+    "from matplotlib import pyplot\n",
+    "import keras\n",
+    "from keras.utils import to_categorical\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Conv2D\n",
+    "from keras.layers import MaxPooling2D\n",
+    "from keras.layers import Dense\n",
+    "from keras.layers import Flatten\n",
+    "from keras.layers import Dropout\n",
+    "from keras.optimizers import SGD\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    " \n",
+    "# one block VGG\n",
+    "\"\"\"\n",
+    "def define_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(150, 150, 3)))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "    # compile model\n",
+    "    opt = SGD(lr=0.001, momentum=0.9)\n",
+    "    model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "    return model\n",
+    "\"\"\"\n",
+    "\"\"\"\n",
+    "# two block VGG\n",
+    "def define_model():\n",
+    "    model = Sequential()\n",
+    "    model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(150, 150, 3)))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
+    "    model.add(MaxPooling2D((2, 2)))\n",
+    "    model.add(Flatten())\n",
+    "    model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
+    "    model.add(Dense(1, activation='sigmoid'))\n",
+    "    # compile model\n",
+    "    opt = SGD(lr=0.001, momentum=0.9)\n",
+    "    model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "    return model\n",
+    "\"\"\"\n",
+    "# three block VGG\n",
+    "def define_model():\n",
+    "\n",
+    "    cnn1 = Sequential()\n",
+    "    cnn1.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))\n",
+    "    cnn1.add(MaxPooling2D((2, 2)))\n",
+    "    cnn1.add(Dropout(0.2))\n",
+    "\n",
+    "    cnn1.add(Flatten())\n",
+    "\n",
+    "    cnn1.add(Dense(128, activation='relu'))\n",
+    "    cnn1.add(Dense(10, activation='softmax'))\n",
+    "\n",
+    "    cnn1.compile(loss=keras.losses.categorical_crossentropy,\n",
+    "              optimizer=keras.optimizers.Adam(),\n",
+    "              metrics=['accuracy'])\n",
+    "    return cnn1\n",
+    "\n",
+    "# plot diagnostic learning curves\n",
+    "def summarize_diagnostics(history):\n",
+    "    # plot loss\n",
+    "    pyplot.subplot(211)\n",
+    "    pyplot.title('Cross Entropy Loss')\n",
+    "    pyplot.plot(history.history['loss'], color='blue', label='train')\n",
+    "    pyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
+    "    # plot accuracy\n",
+    "    pyplot.subplot(212)\n",
+    "    pyplot.title('Classification Accuracy')\n",
+    "    pyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
+    "    pyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
+    "    # save plot to file\n",
+    "    filename = sys.argv[0].split('/')[-1]\n",
+    "    pyplot.savefig(filename + '_plot.png')\n",
+    "    pyplot.close()\n",
+    " \n",
+    "# run the test harness for evaluating a model\n",
+    "def run_test_harness():\n",
+    "    # define model\n",
+    "    model = define_model()\n",
+    "    # create data generator\n",
+    "    datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
+    "    # prepare iterators\n",
+    "    train_it = datagen.flow_from_directory('dataset/train/',\n",
+    "        class_mode='binary', batch_size=64, target_size=(150, 150))\n",
+    "    test_it = datagen.flow_from_directory('dataset/test/',\n",
+    "        class_mode='binary', batch_size=64, target_size=(150, 150))\n",
+    "    # fit model\n",
+    "    history = model.fit(train_it, steps_per_epoch=len(train_it),\n",
+    "        validation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=0)\n",
+    "    # evaluate model\n",
+    "    _, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
+    "    print('> %.3f' % (acc * 100.0))\n",
+    "    # learning curves\n",
+    "    summarize_diagnostics(history)\n",
+    " \n",
+    "# entry point, run the test harness\n",
+    "run_test_harness()\n"
+   ]
   },
   {
    "cell_type": "code",