Error Message (Error Codes below):
RuntimeError: Expected object of scalar type Float but got scalar type Double for argument #2 ‘mat1’ in call to _th_addmm

for epoch in range(num_epochs):
    # Convert numpy arrays to torch tensors
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    print("Epoch [{}/{}] Loss: {:.4f}".format(epoch+1, num_epochs, loss.item()))

Solution:

Method 1. Add

inputs = inputs.float()
targets = targets.float()
model = model.float()

Complete code

for epoch in range(num_epochs):
    # Convert numpy arrays to torch tensors
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
    inputs = inputs.float()
    targets = inputs.float()
    model = model.float()
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    print("Epoch [{}/{}] Loss: {:.4f}".format(epoch+1, num_epochs, loss.item()))

Method 2. Add

inputs = inputs.double()
targets = inputs.double()
model = model.double()

Complete code

for epoch in range(num_epochs):
    # Convert numpy arrays to torch tensors
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
    inputs = inputs.double()
    targets = inputs.double()
    model = model.double()
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    
    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    print("Epoch [{}/{}] Loss: {:.4f}".format(epoch+1, num_epochs, loss.item()))