From 2c804a14e4fc1787f3c958f97bf7f9e32a95a720 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Sun, 3 Nov 2024 13:50:48 +0100
Subject: [PATCH] Create torchexample.py

---
 doc/src/week45/programs/torchexample.py | 83 +++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 doc/src/week45/programs/torchexample.py

diff --git a/doc/src/week45/programs/torchexample.py b/doc/src/week45/programs/torchexample.py
new file mode 100644
index 000000000..c95d8e17e
--- /dev/null
+++ b/doc/src/week45/programs/torchexample.py
@@ -0,0 +1,83 @@
+from IPython.display import Image as IPythonImage
+#%matplotlib inline
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+X_train = np.arange(10, dtype='float32').reshape((10, 1))
+y_train = np.array([1.0, 1.3, 3.1, 2.0, 5.0, 6.3, 6.6, 
+                    7.4, 8.0, 9.0], dtype='float32')
+
+plt.plot(X_train, y_train, 'o', markersize=10)
+plt.xlabel('x')
+plt.ylabel('y')
+
+#plt.savefig('figures/12_07.pdf')
+plt.show()
+
+from torch.utils.data import TensorDataset
+from torch.utils.data import DataLoader
+
+X_train_norm = (X_train - np.mean(X_train)) / np.std(X_train)
+X_train_norm = torch.from_numpy(X_train_norm)
+
+# On some computers the explicit cast to .float() is
+# necessary
+y_train = torch.from_numpy(y_train).float()
+
+train_ds = TensorDataset(X_train_norm, y_train)
+
+batch_size = 1
+train_dl = DataLoader(train_ds, batch_size, shuffle=True)
+
+torch.manual_seed(1)
+weight = torch.randn(1)
+weight.requires_grad_()
+bias = torch.zeros(1, requires_grad=True)
+ 
+def loss_fn(input, target):
+    return (input-target).pow(2).mean()
+
+def model(xb):
+    return xb @ weight + bias
+
+learning_rate = 0.001
+num_epochs = 200
+log_epochs = 10
+
+for epoch in range(num_epochs):
+    for x_batch, y_batch in train_dl:
+        pred = model(x_batch)
+        loss = loss_fn(pred, y_batch)
+        loss.backward()
+
+        with torch.no_grad():
+            weight -= weight.grad * learning_rate
+            bias -= bias.grad * learning_rate
+            weight.grad.zero_()
+            bias.grad.zero_()
+ 
+    if epoch % log_epochs==0:
+        print(f'Epoch {epoch}  Loss {loss.item():.4f}')
+
+print('Final Parameters:', weight.item(), bias.item())
+ 
+X_test = np.linspace(0, 9, num=100, dtype='float32').reshape(-1, 1)
+X_test_norm = (X_test - np.mean(X_train)) / np.std(X_train)
+X_test_norm = torch.from_numpy(X_test_norm)
+y_pred = model(X_test_norm).detach().numpy()
+
+
+fig = plt.figure(figsize=(13, 5))
+ax = fig.add_subplot(1, 2, 1)
+plt.plot(X_train_norm, y_train, 'o', markersize=10)
+plt.plot(X_test_norm, y_pred, '--', lw=3)
+plt.legend(['Training examples', 'Linear Reg.'], fontsize=15)
+ax.set_xlabel('x', size=15)
+ax.set_ylabel('y', size=15)
+ax.tick_params(axis='both', which='major', labelsize=15)
+ 
+#plt.savefig('figures/12_08.pdf')
+
+plt.show()