ResNet_keras.py 文件源码

def residual_network(img_input,classes_num=10,stack_n=5):
    def residual_block(intput,out_channel,increase=False):
        if increase:
            stride = (2,2)
        else:
            stride = (1,1)

        pre_bn   = BatchNormalization()(intput)
        pre_relu = Activation('relu')(pre_bn)

        conv_1 = Conv2D(out_channel,kernel_size=(3,3),strides=stride,padding='same',
                        kernel_initializer="he_normal",
                        kernel_regularizer=regularizers.l2(weight_decay))(pre_relu)
        bn_1   = BatchNormalization()(conv_1)
        relu1  = Activation('relu')(bn_1)
        conv_2 = Conv2D(out_channel,kernel_size=(3,3),strides=(1,1),padding='same',
                        kernel_initializer="he_normal",
                        kernel_regularizer=regularizers.l2(weight_decay))(relu1)
        if increase:
            projection = Conv2D(out_channel,
                                kernel_size=(1,1),
                                strides=(2,2),
                                padding='same',
                                kernel_initializer="he_normal",
                                kernel_regularizer=regularizers.l2(weight_decay))(intput)
            block = add([conv_2, projection])
        else:
            block = add([intput,conv_2])
        return block

    # build model
    # total layers = stack_n * 3 * 2 + 2
    # stack_n = 5 by default, total layers = 32
    # input: 32x32x3 output: 32x32x16
    x = Conv2D(filters=16,kernel_size=(3,3),strides=(1,1),padding='same',
               kernel_initializer="he_normal",
               kernel_regularizer=regularizers.l2(weight_decay))(img_input)

    # input: 32x32x16 output: 32x32x16
    for _ in range(stack_n):
        x = residual_block(x,16,False)

    # input: 32x32x16 output: 16x16x32
    x = residual_block(x,32,True)
    for _ in range(1,stack_n):
        x = residual_block(x,32,False)

    # input: 16x16x32 output: 8x8x64
    x = residual_block(x,64,True)
    for _ in range(1,stack_n):
        x = residual_block(x,64,False)

    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = GlobalAveragePooling2D()(x)

    # input: 64 output: 10
    x = Dense(classes_num,activation='softmax',
              kernel_initializer="he_normal",
              kernel_regularizer=regularizers.l2(weight_decay))(x)
    return x