Tensorflow 中卷积自编码器中的共享权重 [英] Shared weights in convolutional autoencoder in Tensorflow

问题描述

在阅读解卷积时，经常提到在上采样时使用权重的转置，但在我能找到的 Tensorflow 中的少数示例中，情况并非如此.转置是否发生在内部?以下哪个是正确的?

In reading about deconvolution, it is often mentioned to use the transpose of the weights when upsampling, but in the few examples in Tensorflow that I can find, this is not the case. Does the transpose happen internally? Which of the following is correct?

tf.nn.conv2d_transpose(matrix, tf.transpose(W1, [1, 0, 2, 3]), ...)

tf.nn.conv2d_transpose(matrix, W1, ...)

推荐答案

您不需要转置权重.这只是一个命名约定.您可以在此处了解他们为何如此命名.简短的总结是它不执行反卷积，而是执行分数步幅卷积.

You don't need to transpose the weights. It's just a naming convention. You can see why they named it the way they did here. The short summary is that it isn't performing deconvolution and is instead performing a fractionally strided convolution.

同样直接回答你的问题，第二个是正确的.

Also to answer your question directly the second one is correct.

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