张量流中的残差学习 [英] Residual learning in tensorflow

问题描述

我正在尝试从研究论文中复制此图像。在图像中，橙色箭头表示使用残差学习的快捷方式，红色轮廓所示的层表示卷积扩大。

I am attempting to replicate this image from a research paper. In the image, the orange arrow indicates a shortcut using residual learning and the layer outlined in red indicates a dilated convolution.

在下面的代码中，r5是在图像中看到的relu。为了简单起见，我已经排除了relu和dilation层之间的代码。 在tensorflow中，我如何正确地结合relu和膨胀卷积来执行残留快捷方式？

In the code below, r5 is the result of the relu seen in the image. I have excluded the code between the relu and the dilation layer for simplicity. In tensorflow, how would I properly combine the result of the relu and dilated convolution to execute the residual shortcut?

#relu layer
r5 = tf.nn.relu(layer5)
...
#dilation layer
h_conv4 = conv3d_dilation(concat1, 1154)

推荐答案

图像非常简单-它说您应该添加，因此：

The image is quite straight forward - it says you should add them, so:

#relu layer
r5 = tf.nn.relu(layer5)
...
#dilation layer
h_conv4 = conv3d_dilation(concat1, 1154)

#combined
combined = r5 + h_conv4

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