查看keras自动给文件夹标号

 1 from tensorflow.contrib.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array
 2 from tensorflow.contrib.keras.api.keras.models import Sequential
 3 from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Activation, Flatten
 4 from tensorflow.contrib.keras.api.keras.layers import Conv2D, MaxPooling2D
 5 IMAGE_SIZE = 224
 6 img_rows= 224
 7 img_cols = 224
 8 # 训练图片大小
 9 epochs = 50#原来是50
10 # 遍历次数
11 batch_size = 32
12 # 批量大小
13 nb_train_samples = 256*2
14 # 训练样本总数
15 nb_validation_samples = 64*2
16 # 测试样本总数
17 train_data_dir = 'D:\\pycode\\learn\\data\\train_data\\'
18 validation_data_dir = 'D:\\pycode\\learn\\data\\test_data\\'
19 # 样本图片所在路径
20 FILE_PATH = 'age.h5'
21 
22 train_datagen = ImageDataGenerator(
23     rescale=1. / 255,
24     horizontal_flip=True)
25 
26 test_datagen = ImageDataGenerator(rescale=1. / 255)
27 
28 train_generator = train_datagen.flow_from_directory(
29     train_data_dir,
30     target_size=(img_rows, img_cols),
31     batch_size=batch_size,
32     class_mode='categorical')
33 
34 validation_generator = test_datagen.flow_from_directory(
35     validation_data_dir,
36     target_size=(img_rows, img_cols),
37     batch_size=batch_size,
38     class_mode='categorical')
39 
40 # self.train = train_generator
41 # self.valid = validation_generator
42 print(validation_generator.class_indices)