inception_resnet_v2.py 文件源码

def reduction_resnet_v2_B(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)

    r2 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r2 = Convolution2D(384, 3, 3, activation='relu', subsample=(2,2))(r2)

    r3 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(288, 3, 3, activation='relu', subsample=(2, 2))(r3)

    r4 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r4 = Convolution2D(288, 3, 3, activation='relu', border_mode='same')(r4)
    r4 = Convolution2D(320, 3, 3, activation='relu', subsample=(2, 2))(r4)

    m = merge([r1, r2, r3, r4], concat_axis=channel_axis, mode='concat')
    m = BatchNormalization(axis=channel_axis)(m)
    m = Activation('relu')(m)
    return m