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06_04_gradient_torch_model.py

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+# prediction            pytorch model       -> forward function gets replaced by pytroch model
+# gradient computation  autograd
+# loss computation      pytorch loss
+# parameter update      pytorch optimizer
+
+import torch
+import torch.nn as nn # neural network module
+
+
+# linear regression, no bias
+# f = w*x
+# f = 2*x
+
+X = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32) #reshape for pytorch model
+Y = torch.tensor([[2], [4], [6], [8]], dtype=torch.float32)
+X_test = torch.tensor([5], dtype=torch.float32)
+
+n_samples, n_features = X.shape
+print(n_samples, n_features)
+
+input_size = n_features
+output_size = n_features
+
+# model = nn.Linear(input_size, output_size, bias=False) 
+
+#custom linear regression model (just a wrapper in this case, but you can add more layers)
+class LinearRegression(nn.Module):
+    def __init__(self, input_dim, output_dim, bias=True):
+        super(LinearRegression, self).__init__()
+        #define layers
+        self.lin = nn.Linear(input_dim, output_dim, bias)
+
+    def forward(self, x):
+        return self.lin(x)
+
+model = LinearRegression(input_size, output_size, bias=False)
+
+print(f'Prediction before training: f(5) = {model(X_test).item():.3f}')
+
+#Training
+learning_rate = .01
+n_iters = 100
+
+loss = nn.MSELoss() # use pytorch built in MSE loss function
+optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate) # use pytorch built in optimizer to optimize parameter 'w' with learning rate
+
+
+for epoch in range(n_iters):
+    # prediction = forward pass
+    y_pred = model(X)
+
+    # loss
+    l = loss(Y, y_pred)
+
+    # gradients = backward pass
+    l.backward()
+
+    #update weights
+    optimizer.step()
+
+    # clear gradients
+    optimizer.zero_grad()
+
+    if epoch % 10 == 0: #every nth epoch
+        [w] = model.parameters()
+        print(f'epoch {epoch+1}: w = {w[0].item():.3f}, loss = {l:.8f}')
+
+print(f'Prediction after training: f(5) = {model(X_test).item():.3f}')