How to run a basic RNN model using Pytorch?

How to run a basic RNN model using Pytorch?

How to run a basic RNN model using Pytorch?

This Pytorch recipe inputs a dataset into a basic RNN (recurrent neural net) model and makes image classification predictions.

This recipe uses the MNIST handwritten digits dataset for image classification. The RNN model predicts what the handwritten digit is. The recipe uses the following steps to accurately predict the handwritten digits:
- Import Libraries
- Prepare Dataset
- Create RNN Model
- Instantiate Model Class
- Instantiate Loss Class
- Instantiate Optimizer Class
- Tran the Model
- Prediction

This recipe uses the helpful PyTorch utility DataLoader - which provide the ability to batch, shuffle and load the data in parallel using multiprocessing workers.

What is RNN ?
A recurrent neural network (RNN) is a type of deep learning artificial neural network commonly used in speech recognition and natural language processing (NLP). This neural net processes sequential data, and takes in as input both the new input and the output (or a hidden layer) of the net in the previous step. Since they have backward connection in their hidden layers they have memory states.

What is PyTorch ?
Pytorch is a Python deep learning library that uses the power of graphics processing units. Its strengths compared to other tools like tensorflow are its flexibility and speed. You can use other Python packages such as NumPy, SciPy to extend PyTorch functionalities.

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