Learn all the basics you need to get started with this deep learning framework! In this part I will explain the famous backpropagation algorithm. I will explain all the necessary concepts and walk you through a concrete example. At the end we will see how easy it is to use backpropagation in PyTorch.
- Chain Rule
- Computational Graph and local gradients
- Forward and backward pass
- Concrete example with numbers (Linear Regression)
- How to use backpropagation in PyTorch
All code from this course can be found on GitHub.
Backpropagation in Pytorch
import torch x = torch.tensor(1.0) y = torch.tensor(2.0) # This is the parameter we want to optimize -> requires_grad=True w = torch.tensor(1.0, requires_grad=True) # forward pass to compute loss y_predicted = w * x loss = (y_predicted - y)**2 print(loss) # backward pass to compute gradient dLoss/dw loss.backward() print(w.grad) # update weights # next forward and backward pass... # continue optimizing: # update weights, this operation should not be part of the computational graph with torch.no_grad(): w -= 0.01 * w.grad # don't forget to zero the gradients w.grad.zero_() # next forward and backward pass...