Modify the above code to turn it into a solution to the regression problem

To modify the code to solve a regression problem instead of a binary classification problem, you can make the following changes:

1. Change the output layer's size to 1, as we are now predicting a continuous value instead of class labels.

2. Remove the `torch.tanh` activation function from the forward pass, as we don't need it for regression.

3. Modify the loss function and training loop accordingly to accommodate regression.

Here's the modified code:

import torch
import torch.nn as nn
import torch.optim as optim

class RegressionModel(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(RegressionModel, self).__init__()
        
        self.hidden_layer = nn.Linear(input_size, hidden_size)
        self.output_layer = nn.Linear(hidden_size, 1)
        
        # Initialize the weights
        self.output_layer.weight.data[0] = torch.randn(1)
        self.output_layer.weight.data[1] = -torch.abs(torch.randn(1))
        self.output_layer.weight.data[2] = torch.randn(1)
        
    def forward(self, x):
        x = self.hidden_layer(x)
        x = self.output_layer(x)
        
        return x

# Example usage
input_size = 10
hidden_size = 20
model = RegressionModel(input_size, hidden_size)

# Generate random input and target tensors
input_tensor = torch.randn(1, input_size)
target_tensor = torch.randn(1, 1)

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Training loop
for epoch in range(100):
    optimizer.zero_grad()
    
    # Forward pass
    output = model(input_tensor)
    
    # Compute loss
    loss = criterion(output, target_tensor)
    
    # Backward pass
    loss.backward()
    
    # Update weights
    optimizer.step()

# Test the trained model
test_input = torch.randn(1, input_size)
test_output = model(test_input)
print(test_output)

In this modified code, we have changed the output layer's size to 1, as we are now predicting a continuous value. We have also removed the `torch.tanh` activation function from the forward pass since it's not needed for regression.

The loss function is now set to `nn.MSELoss()` (Mean Squared Error), which is commonly used for regression problems. We use stochastic gradient descent (`optim.SGD`) as the optimizer.

In the training loop, we compute the loss between the predicted output and the target tensor, perform backpropagation, and update the weights using the optimizer.

Finally, we test the trained model by passing a test input through it and printing the predicted output.