inception_resnet_v1.py 文件源码

def reduction_A(input, k=192, l=224, m=256, n=384):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2))(input)

    r2 = Convolution2D(n, 3, 3, activation='relu', subsample=(2,2))(input)

    r3 = Convolution2D(k, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(l, 3, 3, activation='relu', border_mode='same')(r3)
    r3 = Convolution2D(m, 3, 3, activation='relu', subsample=(2,2))(r3)

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