Create 1.py

12/1.py

@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+
+class SimpleNN(nn.Module):
+    def __init__(self):
+        super(SimpleNN, self).__init__()
+        self.hidden = nn.Linear(2, 5)
+        self.relu = nn.ReLU()
+        self.output = nn.Linear(5, 1)
+
+    def forward(self, x):
+        x = self.hidden(x)
+        x = self.relu(x)
+        x = self.output(x)
+        return x
+
+
+model = SimpleNN()
+
+criterion = nn.MSELoss()
+optimizer = optim.SGD(model.parameters(), lr=0.01)
+
+inputs = torch.abs(torch.randn(10, 2))
+targets = torch.sqrt(inputs[:, 0] * inputs[:, 1]).view(-1, 1)
+
+epochs = 10000
+for epoch in range(epochs):
+    model.train()
+    optimizer.zero_grad()
+
+    outputs = model(inputs)
+
+    loss = criterion(outputs, targets)
+
+    loss.backward()
+
+    optimizer.step()
+
+    if (epoch + 1) % 1000 == 0:
+        print(f"Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}")
+
+model.eval()
+with torch.no_grad():
+    test_input = torch.tensor([[1.0, 1.0]])
+    test_output = model(test_input)
+    print(f"Test input: {test_input}, Predicted output: {test_output.item()}")