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07_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
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+
+# 0. data preparation
+X_numpy, Y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=1)
+
+X = torch.from_numpy(X_numpy.astype(np.float32))
+y = torch.from_numpy(Y_numpy.astype(np.float32))
+y = y.view(y.shape[0], 1) # reshape into column vector
+
+n_samples, n_features = X.shape
+
+# 1. model
+input_size = n_features
+output_size = 1
+model = nn.Linear(input_size, output_size)
+
+# 2. loss and optimizer
+learning_rate = .01
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+
+# 3. training loop
+n_epochs = 10000
+for epoch in range(n_epochs):
+    #forward pass and loss
+    y_pred = model(X)
+    loss = criterion(y, y_pred)
+
+    #backward pass
+    loss.backward()
+
+    #update
+    optimizer.step()
+    optimizer.zero_grad()
+
+    if (epoch+1) % (n_epochs/10) == 0:
+        print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')
+
+# plot
+
+predicted = model(X).detach().numpy()
+plt.plot(X_numpy, Y_numpy, 'ro')
+plt.plot(X_numpy, predicted, 'b')
+plt.show()