5.CatDogTF2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'\n",
    "path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip', origin=_URL, extract=True)\n",
    "PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = os.path.join(PATH, 'train')\n",
    "validation_dir = os.path.join(PATH, 'validation')\n",
    "\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "\n",
    "num_cats_tr = len(os.listdir(train_cats_dir))\n",
    "num_dogs_tr = len(os.listdir(train_dogs_dir))\n",
    "\n",
    "num_cats_val = len(os.listdir(validation_cats_dir))\n",
    "num_dogs_val = len(os.listdir(validation_dogs_dir))\n",
    "\n",
    "total_train = num_cats_tr + num_dogs_tr\n",
    "total_val = num_cats_val + num_dogs_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
    "validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "epochs = 5\n",
    "IMG_HEIGHT = 150\n",
    "IMG_WIDTH = 150\n",
    "\n",
    "IMG_SHAPE = (IMG_WIDTH, IMG_HEIGHT, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                           directory=train_dir,\n",
    "                                                           shuffle=True,\n",
    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                           class_mode='binary')\n",
    "\n",
    "val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=validation_dir,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-trained model with MobileNetV2\n",
    "base_model = tf.keras.applications.MobileNetV2(\n",
    "    input_shape=IMG_SHAPE,\n",
    "    include_top=False,\n",
    "    weights='imagenet'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "base_model.trainable = False\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    base_model,\n",
    "    tf.keras.layers.GlobalMaxPooling2D(),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.compile(optimizer='adam',\n",
    "#               loss='binary_crossentropy',\n",
    "#               metrics=['accuracy'])\n",
    "\n",
    "base_learning_rate = 0.0001\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),\n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_data_gen,\n",
    "    steps_per_epoch = total_train // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data = val_data_gen,\n",
    "    validation_steps = total_val // batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss=history.history['loss']\n",
    "val_loss=history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "model.save_weights(\"\")\n",
    "model.save(\"CatDog.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('CatDog.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "img_width, img_height = 150, 150\n",
    "\n",
    "test_image = tf.keras.preprocessing.image.load_img('pred/lrdog3.jpg', target_size=(img_width, img_height))\n",
    "test_image = tf.keras.preprocessing.image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis=0)\n",
    "\n",
    "test_image = test_image.reshape(1, img_width, img_height, 3)    # Ambiguity!\n",
    "\n",
    "result = model.predict(test_image)\n",
    "if "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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