PyTorch Conv2D Explained with Examples

PyTorch Conv2D Explained with Examples

You will usually hear about 2D Convolution while dealing with convolutional neural networks for images. It is a simple mathematical operation in which we slide a matrix or kernel of weights over 2D data and perform element-wise multiplication with the data that falls under the kernel. Finally, we sum up the multiplication result to produce one output of that operation.

We move the kernel in strides, throughout the input data, till we get the final output matrix of the 2D convolution operation. In the below illustration, the kernel is moving at a stride of 1, it is, however, possible to move with a higher stride of 2,3, etc.

PyTorch Conv 2D Example

A colored image consists of 3 color channels Red, Blue, and Green, hence the 2D convolution operation is done individually for the three color channels, and their results are added together for the final output.

The convolutional neural network that performs convolution on the image is able to outperform a regular neural network in which you would feed the image by flattening it. This is why CNN models have been able to achieve state-of-the-art accuracies in working with images.

To perform convolution operation there is a function Conv2D in PyTorch, let us go through the details of it in the below sections.

PyTorch Conv2D

Below are the syntax and parameters of the Conv2D PyTorch function.

We now create the instance of Conv2D function by passing the required parameters including square kernel size of 3×3 and stride = 1. We then apply this convolution to randomly generated input data.

Other Examples of Conv2D

Below are some other examples of PyTorch Conv2D function usages with different parameters.


Reference – PyTorch Documentation

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