pytorch

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import TensorDataset, DataLoader # 1. Prepare Data x_data = torch.randn(100, 1) y_data = 2 * x_data + 1 + torch.randn(100, 1) * 0.1 # y = 2x + 1 + noise # Create a Dataset and DataLoader dataset = TensorDataset(x_data, y_data) dataloader = DataLoader(dataset, batch_size=10, shuffle=True) # 2. Define Model class LinearRegression(nn.Module): def __init__(self): super(LinearRegression, self).__init__() self.linear = nn.Linear(1, 1) # One input feature, one output feature def forward(self, x): return self.linear(x) model = LinearRegression() # 3. Define Loss and Optimizer criterion = nn.MSELoss() # Mean Squared Error Loss optimizer = optim.SGD(model.parameters(), lr=0.01) # Stochastic Gradient Descent # 4. Train the Model num_epochs = 100 for epoch in range(num_epochs): for batch_x, batch_y in dataloader: # Forward pass outputs = model(batch_x) loss = criterion(outputs, batch_y) # Backward and optimize optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # 5. Make Predictions predicted_value = model(torch.tensor([[5.0]])) print(f"\nPredicted value for x=5.0: {predicted_value.item():.4f}") print(f"Learned parameters: Weight={model.linear.weight.item():.4f}, Bias={model.linear.bias.item():.4f}")