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The TensorFlow Workshop
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Previously, you looked at implementing your own custom loss function with either the TensorFlow functional API or the subclassing approach. These concepts can also be applied to creating custom layers for a deep learning model. In this section, you will build a ResNet module from scratch.
Residual neural network, or ResNet, was first proposed by Kaiming He in his paper Deep Residual Learning for Image Recognition in 2015. He introduced a new concept called a residual block that tackles the problem of vanishing gradients, which limits the ability of training very deep networks (with a lot of layers).
A residual block is composed of multiple layers. But instead of having a single path where each layer is stacked and executed sequentially, a residual block contains two different paths. The first path has two different convolution layers. The second path, called the skip connection, takes the input and forwards it to the...