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Deep Learning Fundamentals

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    Deep Learning Fundamentals > Unit 3 > Unit 3.6
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    3.6 Training a Logistic Regression Model in PyTorch – Parts 1-3

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    What we covered in this video lecture

    In this lecture, we put all the basic concepts into action:

    1. We implemented a logistic regression model using the torch.nn.Module class.
    2. We then trained the logistic regression module by implementing a training loop based on PyTorch’s automatic differentiation capabilities.

    After completing this lecture, we now have all the essential tools for implementing deep neural networks in the next unit: activation functions, loss functions, and essential deep learning utilities of the PyTorch API.

    But before we jump into the next unit, we will cover a small but critical concept that is essential for training neural networks well: input feature normalization.

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    Quiz: 3.6 Training a Logistic Regression Model in PyTorch - PART 1

    Suppose we implement a logistic regression model as a binary classifier for a dataset with 4 features using a linear layer self.linear = torch.nn.Linear(a, b). What are the numeric values for a and b in this case?

    Incorrect. We have 4 input features.

    Incorrect. We have 4 input features.

    Correct. We have 4 input features and 1 output value.

    Incorrect. We have 4 input features.

    Incorrect. We have 4 input features not output features.

    Please answer all questions to proceed.

    Quiz: 3.6 Training a Logistic Regression Model in PyTorch - PART 2

    When we define a dataset in PyTorch using the Dataset class, we implement an __getitem__ method, which returns

    Incorrect. Hint: What does the model need to make a prediction?

    Incorrect. Hint: what do we need in order to compute the loss?

    Incorrect. Hint: The weight update is computed based on the loss, and what else do we need for that?

    Correct. It returns a single training example plus label. We use the training example to obtain the prediction and the label to compute the loss.

    Please answer all questions to proceed.

    Quiz: 3.6 Training a Logistic Regression Model in PyTorch - PART 3

    Suppose we want to use the torch.where function to compute the class labels based on the predicted probability values, torch.where(probas > a, b, c). What are the correct values for a, b, c?

    Incorrect. If the probas are values between 0 and 1, what should the threshold a be?

    Correct. If the probas are greater than 0.5, we return label 1, and label 0 otherwise.

    Incorrect. If the probas are values between 0 and 1, what should the threshold a be?

    Incorrect. If the probas are values between 0 and 1, what should the threshold a be?

    Incorrect. If the probas are greater than 0.5, we return label 1, and label 0 otherwise.

    Incorrect. If the probas are values between 0 and 1, what should the threshold a be?

    Please answer all questions to proceed.
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    Unit 3.6

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