数据增强：利用多种能够生成可信图像的随机变换（比如，旋转、缩放、位移等），从现有的训练样本中生成更多的图像。
下面是《Deep Learning with python》中的示例代码：
import time from keras import layers, models, optimizers from keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt from keras.preprocessing import image import os train_dir = ‘datasets/cats_and_dogs_small/train‘ val_dir = ‘datasets/cats_and_dogs_small/validation‘ test_dir = ‘datasets/cats_and_dogs_small/test‘ train_cats_dir = ‘datasets/cats_and_dogs_small/train/cats‘ # 网络搭建 model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation=‘relu‘, input_shape=(150, 150, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation=‘relu‘)) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation=‘relu‘)) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation=‘relu‘)) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dropout(0.5)) model.add(layers.Dense(512, activation=‘relu‘)) model.add(layers.Dense(1, activation=‘sigmoid‘)) # 网络编译 model.compile(loss=‘binary_crossentropy‘, optimizer=optimizers.RMSprop(lr=1e-4), metrics=[‘acc‘]) # 数据预处理 train_datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode=‘nearest‘) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=32, class_mode=‘binary‘) val_generator = test_datagen.flow_from_directory(val_dir, target_size=(150, 150), batch_size=32, class_mode=‘binary‘) history = model.fit(train_generator, steps_per_epoch=100, epochs=100, validation_data=val_generator, validation_steps=50) model.save(‘cats_and_dogs_small_2.h5‘)
但是在运行之后，会出现以下警告：
WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 10000 batches). You may need to use the repeat() function when building your dataset.
WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 50 batches). You may need to use the repeat() function when building your dataset.
警告表示我的数据不够...目前正在找bug中。
《Deep Learning with python》数据增强
原文：https://www.cnblogs.com/chengzibobo/p/14587165.html