29-使用keras进行实现猫狗分类.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "WARNING:tensorflow:From C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/10\n",
      "100/100 [==============================] - 31s 310ms/step - loss: 0.6895 - acc: 0.5310 - val_loss: 0.6757 - val_acc: 0.5160\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 7s 66ms/step - loss: 0.6506 - acc: 0.6255 - val_loss: 0.6288 - val_acc: 0.6580\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 6s 65ms/step - loss: 0.5992 - acc: 0.6730 - val_loss: 0.6087 - val_acc: 0.6680\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 6s 65ms/step - loss: 0.5632 - acc: 0.7050 - val_loss: 0.5917 - val_acc: 0.6650\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 6s 62ms/step - loss: 0.5359 - acc: 0.7255 - val_loss: 0.5749 - val_acc: 0.6970\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 7s 65ms/step - loss: 0.5126 - acc: 0.7410 - val_loss: 0.5851 - val_acc: 0.6970\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 7s 68ms/step - loss: 0.4943 - acc: 0.7595 - val_loss: 0.5870 - val_acc: 0.6850\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 6s 63ms/step - loss: 0.4632 - acc: 0.7835 - val_loss: 0.5695 - val_acc: 0.7010\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 7s 67ms/step - loss: 0.4461 - acc: 0.7940 - val_loss: 0.5380 - val_acc: 0.7270\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 6s 64ms/step - loss: 0.4159 - acc: 0.8080 - val_loss: 0.5665 - val_acc: 0.7130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aeae249f60>"
      ]
     },
     "execution_count": 6,
     "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"
    },
    {
     "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": [
    "# 卷积实现：\n",
    "from keras import layers \n",
    "from keras import models \n",
    "model = models.Sequential() \n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', \n",
    "input_shape=(150, 150, 3))) \n",
    "model.add(layers.MaxPooling2D((2, 2))) \n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu')) \n",
    "model.add(layers.MaxPooling2D((2, 2))) \n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu')) \n",
    "model.add(layers.MaxPooling2D((2, 2))) \n",
    "model.add(layers.Conv2D(128, (3, 3), activation='relu')) \n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())   # 数据扁平化\n",
    "model.add(layers.Dense(512, activation='relu')) \n",
    "model.add(layers.Dense(1, activation='sigmoid'))  # 因为分类问题，所以我们的激活用sigmoid\n",
    "from keras import optimizers \n",
    "model.compile(loss='binary_crossentropy', \n",
    "optimizer=optimizers.RMSprop(lr=1e-4), \n",
    "metrics=['acc'])\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator \n",
    "train_datagen = ImageDataGenerator(rescale=1./255)  #将所有图像乘以 1/255 缩放 \n",
    "test_datagen = ImageDataGenerator(rescale=1./255) \n",
    "\n",
    "train_dir = './files/cats_and_dogs_small/train'\n",
    "validation_dir = './files/cats_and_dogs_small/validation'\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory( \n",
    "train_dir, \n",
    "target_size=(150, 150),  #将所有图像的大小调整为 150×150\n",
    "batch_size=20,  #每次批量，你当然可以自己决定说取多少个\n",
    "class_mode='binary')  #因为使用了 binary_crossentropy 损失，所以需要用二进制标签\n",
    "validation_generator = test_datagen.flow_from_directory( \n",
    "validation_dir, \n",
    "target_size=(150, 150), \n",
    "batch_size=20, \n",
    "class_mode='binary')\n",
    "\n",
    "history = model.fit_generator( \n",
    "train_generator, \n",
    "steps_per_epoch=100, #生成 器中抽取 steps_per_epoch 个批量后（即运行了 steps_per_epoch 次梯度下降），拟合过程 将进入下一个轮次。本例中，每个批量包含 20 个样本，所以读取完所有 2000 个样本需要 100 \n",
    "#个批量。 \n",
    "epochs=10,  #循环几次\n",
    "validation_data=validation_generator, \n",
    "validation_steps=50)  \n",
    "#运行完，save一下model是良好的行为习惯\n",
    "model.save('cats_and_dogs_small_1.h5')\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "acc = history.history['acc'] \n",
    "val_acc = history.history['val_acc'] \n",
    "loss = history.history['loss'] \n",
    "val_loss = history.history['val_loss'] \n",
    "epochs = range(1, len(acc) + 1) \n",
    "plt.plot(epochs, acc, 'bo', label='Training acc') \n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc') \n",
    "plt.title('Training and validation accuracy') \n",
    "plt.legend() \n",
    "plt.figure() \n",
    "plt.plot(epochs, loss, 'bo', label='Training loss') \n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss') \n",
    "plt.title('Training and validation loss') \n",
    "plt.legend() "
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}