猫狗识别
- 数据预处理：图像数据处理，准备训练和验证数据集
- 卷积网络模型：构建网络架构
- 过拟合问题：观察训练和验证效果，针对过拟合问题提出解决方法
- 数据增强：图像数据增强方法与效果
- 迁移学习：深度学习必备训练策略
导入工具包
import os
import warnings
warnings.filterwarnings("ignore")
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
指定好数据路径（训练和验证）
# 数据所在文件夹
base_dir = './data/cats_and_dogs'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
# 训练集
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
# 验证集
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
构建卷积神经网络模型
- 几层都可以，大家可以随意玩
- 如果用CPU训练，可以把输入设置的更小一些，一般输入大小更主要的决定了训练速度
model = tf.keras.models.Sequential([
#如果训练慢，可以把数据设置的更小一些
tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(64, 64, 3)),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
#为全连接层准备
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
# 二分类sigmoid就够了
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.summary()
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配置训练器
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=1e-4), metrics=['acc'])数据预处理
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读进来的数据会被自动转换成tensor(float32)格式，分别准备训练和验证
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图像数据归一化（0-1）区间
train_datagen = ImageDataGenerator(rescale=1./255) test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( train_dir, # 文件夹路径 target_size=(64, 64), # 指定resize成的大小 batch_size=20, # 如果one-hot就是categorical，二分类用binary就可以 class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(64, 64), batch_size=20, class_mode='binary')训练网络模型
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直接fit也可以，但是通常咱们不能把所有数据全部放入内存，fit_generator相当于一个生成器，动态产生所需的batch数据
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steps_per_epoch相当给定一个停止条件，因为生成器会不断产生batch数据，说白了就是它不知道一个epoch里需要执行多少个step
history = model.fit_generator( train_generator, steps_per_epoch=100, # 2000 images = batch_size * steps epochs=20, validation_data=validation_generator, validation_steps=50, # 1000 images = batch_size * steps verbose=2)Epoch 1/20 100/100 - 7s - loss: 0.6892 - acc: 0.5325 - val_loss: 0.6705 - val_acc: 0.5970 Epoch 2/20 100/100 - 6s - loss: 0.6595 - acc: 0.6055 - val_loss: 0.6346 - val_acc: 0.6470 Epoch 3/20 100/100 - 6s - loss: 0.6350 - acc: 0.6515 - val_loss: 0.6358 - val_acc: 0.6320 Epoch 4/20 100/100 - 7s - loss: 0.5936 - acc: 0.6865 - val_loss: 0.5906 - val_acc: 0.6780 Epoch 5/20 100/100 - 7s - loss: 0.5530 - acc: 0.7170 - val_loss: 0.5978 - val_acc: 0.6670 Epoch 6/20 100/100 - 8s - loss: 0.5179 - acc: 0.7490 - val_loss: 0.5484 - val_acc: 0.7140 Epoch 7/20 100/100 - 8s - loss: 0.4854 - acc: 0.7725 - val_loss: 0.5686 - val_acc: 0.7080 Epoch 8/20 100/100 - 8s - loss: 0.4595 - acc: 0.7905 - val_loss: 0.5452 - val_acc: 0.7150 Epoch 9/20 100/100 - 8s - loss: 0.4406 - acc: 0.7885 - val_loss: 0.5453 - val_acc: 0.7210 Epoch 10/20 100/100 - 7s - loss: 0.4109 - acc: 0.8170 - val_loss: 0.5317 - val_acc: 0.7270 Epoch 11/20 100/100 - 8s - loss: 0.3892 - acc: 0.8285 - val_loss: 0.5384 - val_acc: 0.7220 Epoch 12/20 100/100 - 8s - loss: 0.3542 - acc: 0.8570 - val_loss: 0.5480 - val_acc: 0.7180 Epoch 13/20 100/100 - 8s - loss: 0.3421 - acc: 0.8580 - val_loss: 0.5355 - val_acc: 0.7420 Epoch 14/20 100/100 - 8s - loss: 0.3217 - acc: 0.8665 - val_loss: 0.5572 - val_acc: 0.7340 Epoch 15/20 100/100 - 8s - loss: 0.2931 - acc: 0.8805 - val_loss: 0.5545 - val_acc: 0.7400 Epoch 16/20 100/100 - 8s - loss: 0.2739 - acc: 0.8870 - val_loss: 0.5540 - val_acc: 0.7360 Epoch 17/20 100/100 - 8s - loss: 0.2535 - acc: 0.9040 - val_loss: 0.5564 - val_acc: 0.7380 Epoch 18/20 100/100 - 8s - loss: 0.2257 - acc: 0.9245 - val_loss: 0.5710 - val_acc: 0.7420 Epoch 19/20 100/100 - 8s - loss: 0.2084 - acc: 0.9350 - val_loss: 0.5734 - val_acc: 0.7460 Epoch 20/20 100/100 - 8s - loss: 0.2258 - acc: 0.9130 - val_loss: 0.5897 - val_acc: 0.7300效果展示
import matplotlib.pyplot as plt acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(acc)) plt.plot(epochs, acc, 'bo', label='Training accuracy') plt.plot(epochs, val_acc, 'b', label='Validation accuracy') plt.title('Training and validation accuracy') plt.figure() plt.plot(epochs, loss, 'bo', label='Training Loss') plt.plot(epochs, val_loss, 'b', label='Validation Loss') plt.title('Training and validation loss') plt.legend() plt.show()
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