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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import library"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import cv2\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from keras import layers, models, optimizers, losses, metrics, preprocessing\n",
"from keras.models import Sequential\n",
"from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1823 images belonging to 2 classes.\n",
"Found 200 images belonging to 2 classes.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Home\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"\u001b[1m 92/100\u001b[0m \u001b[32mββββββββββββββββββ\u001b[0m\u001b[37mββ\u001b[0m \u001b[1m1s\u001b[0m 209ms/step - accuracy: 0.5003 - loss: 0.7080"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Home\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\epoch_iterator.py:107: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
" self._interrupted_warning()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 202ms/step - accuracy: 0.5011 - loss: 0.7071 - val_accuracy: 0.4850 - val_loss: 0.6858\n",
"Epoch 2/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 385ms/step - accuracy: 0.5660 - loss: 0.6854 - val_accuracy: 0.5800 - val_loss: 0.6501\n",
"Epoch 3/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 437ms/step - accuracy: 0.6389 - loss: 0.6303 - val_accuracy: 0.5950 - val_loss: 0.6571\n",
"Epoch 4/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 433ms/step - accuracy: 0.6392 - loss: 0.6369 - val_accuracy: 0.6750 - val_loss: 0.6175\n",
"Epoch 5/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 433ms/step - accuracy: 0.6641 - loss: 0.6072 - val_accuracy: 0.6750 - val_loss: 0.6209\n",
"Epoch 6/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 436ms/step - accuracy: 0.6638 - loss: 0.6029 - val_accuracy: 0.7000 - val_loss: 0.5767\n",
"Epoch 7/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 433ms/step - accuracy: 0.6974 - loss: 0.5602 - val_accuracy: 0.7200 - val_loss: 0.5445\n",
"Epoch 8/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 432ms/step - accuracy: 0.7302 - loss: 0.5413 - val_accuracy: 0.7700 - val_loss: 0.5169\n",
"Epoch 9/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 433ms/step - accuracy: 0.7109 - loss: 0.5401 - val_accuracy: 0.7200 - val_loss: 0.5424\n",
"Epoch 10/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 434ms/step - accuracy: 0.7552 - loss: 0.4997 - val_accuracy: 0.6900 - val_loss: 0.5927\n",
"Epoch 11/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 433ms/step - accuracy: 0.7483 - loss: 0.5010 - val_accuracy: 0.7550 - val_loss: 0.5527\n",
"Epoch 12/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 417ms/step - accuracy: 0.7430 - loss: 0.5135 - val_accuracy: 0.7550 - val_loss: 0.5013\n",
"Epoch 13/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 433ms/step - accuracy: 0.7678 - loss: 0.4847 - val_accuracy: 0.7800 - val_loss: 0.5143\n",
"Epoch 14/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 434ms/step - accuracy: 0.7890 - loss: 0.4463 - val_accuracy: 0.7600 - val_loss: 0.5209\n",
"Epoch 15/15\n",
"\u001b[1m100/100\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 426ms/step - accuracy: 0.8115 - loss: 0.4065 - val_accuracy: 0.7650 - val_loss: 0.4908\n"
]
},
{
"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"
]
}
],
"source": [
"img_width, img_height = 150, 150\n",
"\n",
"# Model CNN ΔΖ‘n giαΊ£n\n",
"model = Sequential([\n",
" # lα»p tΓch chαΊp ΔαΊ§u tiΓͺn vα»i 32 bα» lα»c, kΓch thΖ°α»c kernel (3, 5)\n",
" Conv2D(32, (3, 3), activation='relu', input_shape=(img_width, img_height, 3)),\n",
" MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # lα»p tΓch chαΊp thα»© 2 cΓ³ 64 bα» lα»c\n",
" Conv2D(64, (3, 3), activation='relu'),\n",
" MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # lα»p tΓch chαΊp thα»© 3 cΓ³ 128 bα» lα»c\n",
" Conv2D(128, (3, 3), activation='relu'),\n",
" MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # chuyα»n Δα»i ΔαΊ§u ra -> vector 1d\n",
" Flatten(),\n",
" \n",
" # lα»p dense 512 neuron\n",
" Dense(512, activation='relu'),\n",
" Dropout(0.5),\n",
" \n",
" # lα»p ΔαΊ§u ra: 1 neuron vα»i hΓ m activation lΓ sigmoid -> phΓ’n loαΊ‘i chΓ³/mΓ¨o\n",
" Dense(1, activation='sigmoid')\n",
"])\n",
"\n",
"# biΓͺn dα»ch mΓ΄ hΓ¬nh\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"\n",
"# dΓΉng ImageDataGenerator -> chuαΊ©n hΓ³a αΊ£nh, thα»±c hiα»n data augmentation\n",
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True\n",
")\n",
"\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"# tαΊ‘o generator cho train vΓ valid\n",
"train_generator = train_datagen.flow_from_directory(\n",
" 'data.cat_dog/train',\n",
" target_size=(img_width, img_height),\n",
" batch_size=20,\n",
" class_mode='binary'\n",
")\n",
"\n",
"valid_generator = test_datagen.flow_from_directory(\n",
" 'data.cat_dog/valid',\n",
" target_size=(img_width, img_height),\n",
" batch_size=20,\n",
" class_mode='binary'\n",
")\n",
"\n",
"# huαΊ₯n luyα»n\n",
"history= model.fit(\n",
" train_generator,\n",
" steps_per_epoch=100,\n",
" epochs=15,\n",
" validation_data=valid_generator,\n",
" validation_steps=50\n",
")\n",
"\n",
"model.save('cnn_cats_dogs.h5')"
]
}
],
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