inception_resnet_v1.py 文件源码

def inception_resnet_B(input, scale_residual=True):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    # Input is relu activation
    init = input

    ir1 = Convolution2D(128, 1, 1, activation='relu', border_mode='same')(input)

    ir2 = Convolution2D(128, 1, 1, activation='relu', border_mode='same')(input)
    ir2 = Convolution2D(128, 1, 7, activation='relu', border_mode='same')(ir2)
    ir2 = Convolution2D(128, 7, 1, activation='relu', border_mode='same')(ir2)

    ir_merge = merge([ir1, ir2], mode='concat', concat_axis=channel_axis)

    ir_conv = Convolution2D(896, 1, 1, activation='linear', border_mode='same')(ir_merge)
    if scale_residual: ir_conv = Lambda(lambda x: x * 0.1)(ir_conv)

    out = merge([init, ir_conv], mode='sum')
    out = BatchNormalization(axis=channel_axis)(out)
    out = Activation("relu")(out)
    return out