查看keras自动给文件夹标号

 from tensorflow.contrib.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array
 from tensorflow.contrib.keras.api.keras.models import Sequential
 from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Activation, Flatten
 from tensorflow.contrib.keras.api.keras.layers import Conv2D, MaxPooling2D
 IMAGE_SIZE = 224
 img_rows= 224
 img_cols = 224
 # 训练图片大小
 epochs = 50#原来是50
 # 遍历次数
 batch_size = 32
 # 批量大小
 nb_train_samples = 256*2
 # 训练样本总数
 nb_validation_samples = 64*2
 # 测试样本总数
 train_data_dir = 'D:\\pycode\\learn\\data\\train_data\\'
 validation_data_dir = 'D:\\pycode\\learn\\data\\test_data\\'
 # 样本图片所在路径
 FILE_PATH = 'age.h5'

 train_datagen = ImageDataGenerator(
     rescale=1. / 255,
     horizontal_flip=True)

 test_datagen = ImageDataGenerator(rescale=1. / 255)

 train_generator = train_datagen.flow_from_directory(
     train_data_dir,
     target_size=(img_rows, img_cols),
     batch_size=batch_size,
     class_mode='categorical')

 validation_generator = test_datagen.flow_from_directory(
     validation_data_dir,
     target_size=(img_rows, img_cols),
     batch_size=batch_size,
     class_mode='categorical')

 # self.train = train_generator
 # self.valid = validation_generator
 print(validation_generator.class_indices)