How To Define A Convolutional Layer In PyTorch

Use PyTorch nn.Sequential and PyTorch nn.Conv2d to define a convolutional layer in PyTorch

Type: FREE   By: Tylan O'Flynn, Instructor Tylan O'Flynn   Duration: 3:10   Technologies: PyTorch, Python

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For this demonstration, we will need to import torch

import torch

import torchvision

import torchvision

and import torch.nn as nn.

import torch.nn as nn

The sequential container object in PyTorch is designed to make it simple to build up a neural network layer by layer.

model = nn.Sequential()

Once I have defined a sequential container, I can then start adding layers to my network.

first_conv_layer = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)

We use the Conv2d layer because our image data is two dimensional.

An example of 3D data would be a video with time acting as the third dimension.

Some of the arguments for the Conv2d constructor are a matter of choice and some will create errors if not given correct values.

The value of in_channels needs to be equal to the number of channels in the layer above or in the case of the first layer, the number of channels in the data.

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In the case of image data, the most common cases are grayscale images which will have one channel, black, or color images that will have three channels – red, green, and blue.

The CIFAR10 dataset is a collection of RGB images, so the correct value in our case is three.

out_channels is a matter of preference but there are some important things to note about it.

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Firstly, a larger number of out_channels allows the layer to potentially learn more useful features about the input data, though this is not a hard rule.

Secondly, the size of your CNN is a function of the number of in_channels/out_channels in each layer of your network and the number of layers.

If you have a limited dataset, then you should aim to have a smaller network so that it can extract useful features from the data without overfitting.

Lastly, if you’re finding yourself running out of RAM on training your network, thinning the layers is one of the best ways to solve this problem while still having a useful model, other than getting more RAM.

kernel_size is the size of the filter that is run over the images.

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With a kernel size of 3 and a stride of 1, features for each pixel are calculated locally in the context of the pixel itself and every pixel adjacent to it.

If I were to change the kernel_size to 5, then the context would be expanded to include pixels adjacent to the pixels adjacent to the central pixel.

The kernel size can also be given as a tuple of two numbers indicating the height and width of the filter respectively if a square filter is not desired.

The stride argument indicates how far the filter is moved after each computation.

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This is not entirely accurate as tensor computation is done simultaneously.

With a stride of 1 in the first convolutional layer, a computation will be done for every pixel in the image.

With a stride of 2, every second pixel will have computation done on it, and the output data will have a height and width that is half the size of the input data.

The stride argument can also be a tuple if different horizontal and vertical strides are desired.

I would not recommend changing the stride from 1 without a thorough understanding of how this impacts the data moving through the network.

The padding argument indicates how much 0 padding is added to the edges of the data during computation.

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Without good reason to change this, the padding should be equal to the kernel size minus 1 divided by 2.

So for a kernel size of 3, we would have a padding of 1.

In a kernel size of 5, we would have a 0 padding of 2.

This prevents the image shrinking as it moves through the layers.

Once the first convolutional layer is defined, we simply add it to our sequential container using the add module function, giving it the name Conv1.

model.add_module("Conv1", first_conv_layer)

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