如何在 Keras 中实现高斯模糊层？

Question

我有一个自动编码器，我需要在输出后添加一个高斯噪声层。我需要一个自定义层来执行此操作，但我真的不知道如何生成它，我需要使用张量生成它。

如果我想在下面代码的调用部分实现上面的等式，我该怎么做？

class SaltAndPepper(Layer):

    def __init__(self, ratio, **kwargs):
        super(SaltAndPepper, self).__init__(**kwargs)
        self.supports_masking = True
        self.ratio = ratio

    # the definition of the call method of custom layer
    def call(self, inputs, training=None):
        def noised():
            shp = K.shape(inputs)[1:]

         **what should I put here????**            
                return out

        return K.in_train_phase(noised(), inputs, training=training)

    def get_config(self):
        config = {'ratio': self.ratio}
        base_config = super(SaltAndPepper, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

我也尝试使用 lambda 层来实现，但它不起作用。

Answer 1

如果您正在寻找加法或乘法高斯噪声，那么它们已经在 Keras 中实现为一个层：（GuassianNoise加法）和GuassianDropout（乘法）。

但是，如果您在图像处理中专门寻找高斯模糊过滤器中的模糊效果，那么您可以简单地使用具有固定权重的深度卷积层（在每个输入通道上独立应用过滤器）来获得所需的输出（请注意，您需要生成高斯核的权重以将它们设置为 DepthwiseConv2D 层的权重。为此，您可以使用本答案中介绍的函数）：

import numpy as np
from keras.layers import DepthwiseConv2D

kernel_size = 3  # set the filter size of Gaussian filter
kernel_weights = ... # compute the weights of the filter with the given size (and additional params)

# assuming that the shape of `kernel_weighs` is `(kernel_size, kernel_size)`
# we need to modify it to make it compatible with the number of input channels
in_channels = 3  # the number of input channels
kernel_weights = np.expand_dims(kernel_weights, axis=-1)
kernel_weights = np.repeat(kernel_weights, in_channels, axis=-1) # apply the same filter on all the input channels
kernel_weights = np.expand_dims(kernel_weights, axis=-1)  # for shape compatibility reasons

# define your model...

# somewhere in your model you want to apply the Gaussian blur,
# so define a DepthwiseConv2D layer and set its weights to kernel weights
g_layer = DepthwiseConv2D(kernel_size, use_bias=False, padding='same')
g_layer_out = g_layer(the_input_tensor_for_this_layer)  # apply it on the input Tensor of this layer

# the rest of the model definition...

# do this BEFORE calling `compile` method of the model
g_layer.set_weights([kernel_weights])
g_layer.trainable = False  # the weights should not change during training

# compile the model and start training...