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Convolutional neural network: Difference between revisions
→Convolutions
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[https://pytorch.org/docs/stable/nn.html#convolution-layers Pytorch Convolution Layers]<br> | [https://pytorch.org/docs/stable/nn.html#convolution-layers Pytorch Convolution Layers]<br> | ||
[https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d Types of convolutions animations]<br> | [https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d Types of convolutions animations]<br> | ||
Here, we will explain 2d convolutions.<br> | Here, we will explain 2d convolutions, also known as cross-correlation.<br> | ||
Suppose we have the following input image:<br> | Suppose we have the following input image:<br> | ||
<pre> | <pre> | ||
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\end{bmatrix} | \end{bmatrix} | ||
</math><br> | </math><br> | ||
Summing up all the elements gives us <math>66</math> which would go in the first index of the output. | Summing up all the elements gives us <math>66</math> which would go in the first index of the output. | ||
Shifting the kernel over all positions of the image gives us the whole output, another 2D image. | |||
The formula for the output resolution of a convolution is: | The formula for the output resolution of a convolution is: | ||