Choose the correct order of statements that has to be executed in the training_function() in Task 1A (you can ignore the other statements that might come between the given statements): 1. model.eval() 2. loss.backward() 3. optimizer.zero_grad() 4. model.train() 5. optimizer.step()1-4-2-53-5-4-1-24-3-2-54-3-2-5-1
Question
Choose the correct order of statements that has to be executed in the training_function() in Task 1A (you can ignore the other statements that might come between the given statements): 1. model.eval() 2. loss.backward() 3. optimizer.zero_grad() 4. model.train() 5. optimizer.step()1-4-2-53-5-4-1-24-3-2-54-3-2-5-1
Solution
The correct order of statements that has to be executed in the training_function() in Task 1A is 4-3-2-5-1. This is because:
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model.train() - This sets the model to training mode. This is the first step before any training can happen.
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optimizer.zero_grad() - This clears old gradients from the last step (otherwise you’d just accumulate the gradients from all loss.backward() calls).
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loss.backward() - This computes the gradient of the loss with respect to the parameters (or anything requiring gradients) using backpropagation.
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optimizer.step() - This causes the optimizer to take a step based on the gradients of the parameters.
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model.eval() - This sets the model to evaluation mode. This is typically done when validation or testing is being performed.
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Choose the correct order of events taking place during model training where the events are: 1. Updating model parameters 2. Backpropagation 3. Forward pass 4. Loss calculation1-2-3-43-4-2-12-3-1-42-1-3-4
3. If you are successful, the result of print(get_optimal_route( L9, Ll )) will be ['L9', L8', L5', L2', 'Ll']. Explain why this is the case. 4. Review the code to make sure you understand it. In the writeup, you will need to provide an overview of the code using your own words. Do not copy the description from Sayak Paul's article. 5. Run with different levels of hyperparameters gamma and alpha. What are these parameters for? For example, try 0.05 for both. What happens now? Write your findings and explanation. Remember to set back gamma = 0.9 and alpha = 0.75 after this before you proceed with the next questions. 6. When you run print(get_optimal_route(L?', L1')) how many times does the while loop in the get_optimal_route() get executed and why it is this number? Hint: one way to check is to add steps =0 before the while loop.
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
What does the following line of code do :loss.backward()1 pointupdate parameterscompute gradient of the loss with respect to all the learnable parameterszero the gradients before running the backward pass
Problem statementSend feedbackDo proper match for below statements:1. Uses n data points instead of 1 sample at each iteration.2. Computes the gradient using a single sample.3. Computes the gradient using the whole dataset.Types of Gradient DescentA: Mini-batch gradient descent B: Stochastic gradient descent C: Batch gradient descent
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