alexNet.py 文件源码

def crosschannelnormalization(alpha=1e-4, k=2, beta=0.75, n=5, **kwargs):
    """
    This is the function used for cross channel normalization in the original
    Alexnet
    """


    def f(X):
        if K.image_dim_ordering()=='tf':
            b, r, c, ch = X.get_shape()
        else:
            b, ch, r, c = X.shape

        half = n // 2
        square = K.square(X)
        scale = k
        if K.image_dim_ordering() == 'th':
            extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 2, 3, 1)), (0, half))
            extra_channels = K.permute_dimensions(extra_channels, (0, 3, 1, 2))
            for i in range(n):
                scale += alpha * extra_channels[:, i:i+ch, :, :]
        if K.image_dim_ordering() == 'tf':
            extra_channels = K.spatial_2d_padding(K.permute_dimensions(square, (0, 3, 1, 2)), (half, 0))
            extra_channels = K.permute_dimensions(extra_channels, (0, 2, 3, 1))
            for i in range(n):
                scale += alpha * extra_channels[:, :, :, i:i+int(ch)]
        scale = scale ** beta
        return X / scale

    return Lambda(f, output_shape=lambda input_shape: input_shape, **kwargs)