Stochastic Gradient Descent Using Pytorch Linear Module

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In the previous tutorial here on SGD, I explored the way in which we can implement using PyTorch's built-in gradient calculation, loss, and optimization implementation. in our present discussion, we’ll continue exploring PyTorch by using the same constructs and using also using the built-in Linear module.

Steps :

Preparation Steps

• Prepare training Data
• Define Putroch linear model
• Define a loss function to evaluate the difference between the output of the above function and the actual values of the output.
• Define learning rate and optimizer.

Training Steps

• Call the PyTorch model on the training data to calculate the output.
• Adjust weight parameters according to the derivatives and specified learning rate.
• Repeat previous steps till the loss is beyond an acceptable thresold or after completing a maximum number of iterations.

As we’ll see it is easy to perform using PyTorch.

Step 1

Prepare the Input

In this step, we’ll use PyTorch tensors to create input values X which is a sequence of numbers…