<p>如果您正在寻找<strong>加法</strong>或<strong>乘法</strong>高斯噪声,那么它们已经在Keras中实现为一个层:<a href="https://keras.io/layers/noise/#gaussiannoise" rel="nofollow noreferrer">^{<cd1>}</a>(加法)和{a2}(乘法)。在</p>
<p>但是,如果您特别想要寻找图像处理中的<a href="https://en.wikipedia.org/wiki/Gaussian_blur" rel="nofollow noreferrer">Gaussian blur</a>过滤器中的模糊效果,然后,您可以简单地使用一个深度卷积层(在每个输入通道上独立地应用滤波器)和<strong>固定的</strong>权重来获得所需的输出(请注意,您需要生成高斯核的权重,将其设置为DepthwiseConv2D层的权重。为此,您可以使用<a href="https://stackoverflow.com/a/17201686/2099607">answer</a>中介绍的函数:</p>
<pre><code>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...
</code></pre>