# 读取数据 from keras.preprocessing.image import ImageDataGenerator IMSIZE = 224 train_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/data_inception/train', target_size=(IMSIZE, IMSIZE), batch_size=100, class_mode='categorical' ) validation_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/data_inception/test', target_size=(IMSIZE, IMSIZE), batch_size=100, class_mode='categorical')
# 展示数据 from matplotlib import pyplot as plt plt.figure() fig, ax = plt.subplots(2, 5) fig.set_figheight(7) fig.set_figwidth(15) ax = ax.flatten() X, Y = next(train_generator) for i in range(10): ax[i].imshow(X[i, :, :,: ])
#相比于之前,这里需要导入concatenate函数 from keras.layers import Conv2D, BatchNormalization, MaxPooling2D from keras.layers import Flatten, Dropout, Dense, Input, concatenate from keras import Model input_layer = Input([IMSIZE, IMSIZE, 3]) x = input_layer x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu')(x) x = BatchNormalization(axis=3)(x) x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x) x = Conv2D(192, (3, 3), strides=(1, 1), padding='same', activation='relu')(x) x = BatchNormalization(axis=3)(x) #para=4*192=768 x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x) x
for i in range(9): brach1x1 = Conv2D(64, (1, 1), strides=(1, 1), padding='same', activation='relu')(x) brach1x1 = BatchNormalization(axis=3)(brach1x1) brach3x3 = Conv2D(96, (1, 1), strides=(1, 1), padding='same', activation='relu')(x) brach3x3 = BatchNormalization(axis=3)(brach3x3) brach3x3 = Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')(brach3x3) brach3x3 = BatchNormalization(axis=3)(brach3x3) brach5x5 = Conv2D(16, (1, 1), strides=(1, 1), padding='same', activation='relu')(x) brach5x5 = BatchNormalization(axis=3)(brach5x5) brach5x5 = Conv2D(32, (3, 3), strides=(1, 1), padding='same', activation='relu')(brach5x5) brach5x5 = BatchNormalization(axis=3)(brach5x5) branchpool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x) branchpool = Conv2D(32, (1, 1), strides=(1, 1), padding='same', activation='relu')(branchpool) branchpool = BatchNormalization(axis=3)(branchpool) x = concatenate([brach1x1, brach3x3, brach5x5, branchpool], axis=3) x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x) x = Dropout(0.4)(x) x = Flatten()(x) x = Dense(17, activation='softmax')(x) output_layer = x model = Model(input_layer, output_layer) model.summary()
#运行 from keras.optimizers import Adam model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy']) model.fit_generator(train_generator, epochs=20, validation_data=validation_generator)
Tensorflow1.15深度学习