What's wrong with the following lines of code?12optimizer = optim.SGD(model.parameters(), lr = 0.01)model=linear_regression(1,1)1 pointThe model object has not been created. As such, the argument that specifies what Tensors should be optimized does not existThere is no loss function You have to clear the gradient
Question
What's wrong with the following lines of code?12optimizer = optim.SGD(model.parameters(), lr = 0.01)model=linear_regression(1,1)1 pointThe model object has not been created. As such, the argument that specifies what Tensors should be optimized does not existThere is no loss function You have to clear the gradient
Solution
The problem with the code is that the model object is created after the optimizer. The optimizer needs the parameters of the model to work properly, so the model should be created before the optimizer.
Here is the correct order:
model = linear_regression(1,1)
optimizer = optim.SGD(model.parameters(), lr = 0.01)
Additionally, there is no loss function defined in the code. In PyTorch, you need to define a loss function to calculate the difference between the output of your model and the actual target values. For example, you could use the Mean Squared Error (MSE) loss for a regression problem:
criterion = nn.MSELoss()
Finally, you need to clear the gradients before a new optimization step to prevent accumulation of gradients from previous steps. You can do this with the following line of code:
optimizer.zero_grad()
So, the corrected code should look something like this:
model = linear_regression(1,1)
optimizer = optim.SGD(model.parameters(), lr = 0.01)
criterion = nn.MSELoss()
# During your training loop:
optimizer.zero_grad()
# Forward pass, backward pass and optimization step go here
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