How to Create a Residual Network in TensorFlow and Keras

How to Create a Residual Network in TensorFlow and Keras

While creating a Sequential model in Tensor flow and Keras is not too complex, creating a residual network might have some complexities. In this article, I show you how to create a residual network from scratch. How to Create a Residual Network in TensorFlow and Keras. The code with an explanation is available at GitHub.

The code with an explanation is available at GitHub.

ResNet, was first introduced by Kaiming He[1]. If you are not familiar with Residual Networks and why they can more likely improve the accuracy of a network.

While creating a Sequential model in Tensor flow and Keras is not too complex, creating a residual network might have some complexities. In this article, I show you how to create a residual network from scratch.

Summary:

  • Task type: classifying handwritten digits.
  • Dataset: THE MNIST DATABASE
  • Network Architecture: a small residual network shown in Figure 1.
  • Optimizer: Adam
  • Loss function: categorical_crossentropy

Code directory structure:

  • main.py
  • train.py
  • network_model.py

machine-learning deep-learning keras tensorflow neural-networks

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