Deep Learning - Computer Vision for Beginners Using PyTorch - Writing a Custom NN Module

The video tutorial explains how to write simple models using linear operations and activations, and introduces the concept of subclassing the NN module for more complex models. It details the use of the Torch NN module to implement various layers and entire neural networks. The tutorial covers subclassing the NN module, overriding the init and forward methods, and implementing a custom class. Finally, it completes the model definition and discusses executing the code to achieve the same results as before.

What is the primary purpose of using a simple sequence of linear operations, ReLU, and sigmoid in a model?

• To handle complex models

• To increase computational efficiency

• To simplify the model writing process

• To reduce the number of parameters

Which of the following layers can be implemented using the torch.nn module?

• Only fully connected layers

• Only convolutional layers

• Only pooling layers

• Fully connected, convolutional, and pooling layers

What is the first method that needs to be overridden when subclassing torch.nn.Module?

• optimize method

• init method

• forward method

• backward method

In the context of subclassing torch.nn.Module, what is the purpose of the forward method?

• To initialize the model parameters

• To define how the output is computed from the input

• To optimize the model

• To compile the model

What is the main advantage of creating a custom class by subclassing torch.nn.Module?

• It simplifies the code

• It allows for the creation of complex networks

• It reduces the training time

• It increases the model's accuracy

What modification is necessary in the fit function when using a custom class?

• Changing the loss function

• Modifying the computation of Y hat

• Adjusting the learning rate

• Altering the optimizer

What is the expected outcome after executing the code with the custom class?

• A faster execution time

• The same result as before

• A different result than before

• An error in the code