added 04_backpropagation

Author: patrickloeber

03_autograd.py

@@ -57,7 +57,7 @@
 # - x.detach()
 # - wrap in 'with torch.no_grad():'
 
-# .requires_grad_(...) changes an existing Tensor’s requires_grad flag in-place.
+# .requires_grad_(...) changes an existing flag in-place.
 a = torch.randn(2, 2)
 print(a.requires_grad)
 b = ((a * 3) / (a - 1))
@@ -80,7 +80,7 @@
     print((x ** 2).requires_grad)
 
 # -------------
-# with backward() the gradient for this tensor will be accumulated into .grad attribute.
+# backward() accumulates the gradient for this tensor into .grad attribute.
 # !!! We need to be careful during optimization !!!
 # Use .zero_() to empty the gradients before a new optimization step!
 weights = torch.ones(4, requires_grad=True)

04_backpropagation.py

@@ -0,0 +1,28 @@
+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...