Implementation differences in LSTM layers— Tensorflow vs Pytorch. Maybe you don't know.
Tensorflow and Pytorch are the two most widely used libraries in deep learning. Both these libraries have different approaches when it comes to implementing neural networks. Both these libraries started with major differences. Usually, TF was more preferred for developing production-ready models due to its better optimizations, and Pytorch was preferred for research works due to its more “pythonic” syntax and eager execution. But with Torchscript and TF 2.0, the gap between the two libraries has shrunk.
Both of these libraries have good community support and active contributions. So we can easily implement different types of neural networks without any major issues in both these libraries. But these two libraries have architectural differences. Hence, there are certain differences we need to focus on when implementing a neural network.
One such difference is in the layers API. A neural network (NN) can be designed using either the sub-classing approach or sequential model API approach in both these libraries. The sub-classing approach is more preferred out of the two since it is object-oriented and extensible. When implementing a NN in either of these libraries, we can use already designed layers — linear (fully connected) layers, convolution layers, recurrent layers, etc. and extend them into our model. In TF, we use
tensorflow.keras.layers, and in Pytorch, we use
torch.nn to access these layers. As I mentioned earlier, there are subtle differences in the implementation of these layers. Most of the time, they are minor and intuitive. But in LSTM (Long Short-Term Memory) layers, these differences are somewhat major and significant. The way the layer is parameterized, the default values for the parameters, and the default output of the layer mostly differ between Pytorch and TF in LSTM layers.
In this post, I will try to explain the differences between LSTM layers in these two libraries. I hope it will be of help if someone refers to a NN (with LSTM layers) implemented in one library and tries to reproduce it in the other.
This article will simply explain the concept which will help you understand the difference between Machine Learning and Deep Learning.
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