Your first CNN - init method

You are going to build your first convolutional neural network. You're going to use the MNIST dataset as the dataset, which is made of handwritten digits from 0 to 9. The convolutional neural network is going to have 2 convolutional layers, each followed by a ReLU nonlinearity, and a fully connected layer. We have already imported torch and torch.nn as nn. Remember that each pooling layer halves both the height and the width of the image, so by using 2 pooling layers, the height and width are 1/4 of the original sizes. MNIST images have shape (1, 28, 28)

For the moment, you are going to implement the __init__ method of the net. In the next exercise, you will implement the .forward() method.

NB: We need 2 pooling layers, but we only need to instantiate a pooling layer once, because each pooling layer will have the same configuration. Instead, we will use self.pool twice in the next exercise.

Instantiate two convolutional filters: the first one should have 5 channels, while the second one should have 10 channels. The kernel_size for both of them should be 3, and both should use padding=1. Use the names of the arguments (instead of using 1, use padding=1).
Instantiate a ReLU() nonlinearity.
Instantiate a max pooling layer which halves the size of the image in both directions.
Instantiate a fully connected layer which connects the units with the number of classes (we are using MNIST, so there are 10 classes).

Exercise

Your first CNN - init method

Instructions