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my07_linear_regression.py

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+# this is a recap
+
+# a training pipeline generally consists of 3 steps:
+# 1. Design model (input, output size, forward pass (layers))
+# 2. Construct loss and optimizer
+# 3. Training loop
+#     - forward pass: compute prediction
+#     - backward pass: gradient computation
+#     - update parameters
+
+# (iterate step 3)
+
+import torch
+import torch.nn as nn
+
+X = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32)
+Y = torch.tensor([[3], [6], [9], [12]], dtype=torch.float32)
+
+X_test = torch.tensor([5], dtype=torch.float32)
+
+n_samples, n_features = X.shape
+input_size = output_size = n_features
+
+learning_rate = 0.01
+n_iter = 100
+
+
+model = nn.Linear(input_size, output_size, bias=False)
+loss = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+print(f'Prediction before training: f(5) = {model(X_test).item():.3f}')
+for epoch in range(n_iter):
+    Y_pred = model(X)
+    l = loss(Y, Y_pred)
+    l.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+
+    [w] = model.parameters()
+    w = w.item()
+
+    if epoch % 10 == 0:
+        print(f'Epoch {epoch}: w = {w:.3f}, loss = {l:.5f}')
+
+print(f'Prediction after training: f(5) = {model(X_test).item():.3f}')