Implementing a CNN
A CNN is one of the foundational blocks of computer vision techniques, and it is important for you to have a solid understanding of how they work. While we already know that a CNN constitutes convolution, pooling, flattening, and then the final classification layer, in this section, we will understand the various operations that occur during the forward pass of a CNN through code.
To gain a solid understanding of this, first, we will build a CNN architecture on a toy example using PyTorch and then match the output by building the feed-forward propagation from scratch in Python. The CNN architecture will differ from the neural network architecture that we built in the previous chapter in that a CNN constitutes the following in addition to what a typical vanilla deep neural network would have:
- Convolution operation
- Pooling operation
- Flatten layer
In the following code, we will build a CNN model on a toy dataset, as follows:
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