architectures.py 文件源码

def deflating_convolution(inputs, n_deflation_layers, n_filters_init=32, noise=None, name_prefix=None):
    def add_linear_noise(x, eps, ind):
        flattened_deflated = Reshape((-1,), name=name_prefix + '_conv_flatten_{}'.format(ind))(x)
        deflated_shape = ker.int_shape(x)
        deflated_size = deflated_shape[1] * deflated_shape[2] * deflated_shape[3]
        noise_transformed = Dense(deflated_size, activation=None,
                                  name=name_prefix + '_conv_noise_dense_{}'.format(ind))(eps)
        added_noise = Add(name=name_prefix + '_conv_add_noise_{}'.format(ind))([noise_transformed, flattened_deflated])
        x = Reshape((deflated_shape[1], deflated_shape[2], deflated_shape[3]),
                    name=name_prefix + '_conv_backreshape_{}'.format(ind))(added_noise)
        return x

    deflated = Conv2D(filters=n_filters_init, kernel_size=(5, 5), strides=(2, 2),
                      padding='same', activation='relu', name=name_prefix + '_conv_0')(inputs)
    if noise is not None:
        deflated = add_linear_noise(deflated, noise, 0)
    for i in range(1, n_deflation_layers):
        deflated = Conv2D(filters=n_filters_init * (2**i), kernel_size=(5, 5), strides=(2, 2),
                          padding='same', activation='relu', name=name_prefix + '_conv_{}'.format(i))(deflated)
        # if noise is not None:
        #     deflated = add_linear_noise(deflated, noise, i)
    return deflated