Скачать или смотреть Solving the Output Size Issue in Convolutional Autoencoders

Discover how to adjust your convolutional autoencoder's decoder to ensure the output shape matches the input.
---
This video is based on the question https://stackoverflow.com/q/76080747/ asked by the user 'Luis Castejón Lozano' ( https://stackoverflow.com/u/20307159/ ) and on the answer https://stackoverflow.com/a/76081676/ provided by the user 'TheEngineerProgrammer' ( https://stackoverflow.com/u/14993700/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: My convolutional autoencoder should return the same shape as the input, but it does not

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Fixing Output Shape Issues in Convolutional Autoencoders

When working with convolutional autoencoders, one common challenge is ensuring that the output shape matches the input shape. A user recently experienced this exact issue while trying to reconstruct a spectrogram with their model. Instead of the expected output shape of (1, 100, 592), they were getting an output of (1, 90, 586). In this guide, we’ll walk through the architecture of a convolutional autoencoder, particularly focusing on the decoder component, and how to adjust it to match the desired output shape.

Understanding the Issue

The problem arises within the decoder section of the convolutional autoencoder. The encoder compresses the input data into a latent space representation, but the subsequent decoder does not always sequentially reconstruct the tensor to its original size. In this particular case, the discrepancy started after the second ConvTranspose2d layer in the decoder.

Key Components of the Autoencoder

Encoder: This part reduces the dimensionality of the data through convolutional layers and extracts key features.

Latent Space: A compressed representation of the input data.

Decoder: This converts the latent representation back into the original data shape using transposed convolutions.

Solution: Adjusting the Decoder

The main adjustment needed involves tuning the padding and stride parameters in the decoder's transposed convolutional layers to ensure that the final output matches the input size. Let's break down the solution into clear steps, including the code modifications required.

New Decoder Class Implementation

Here’s an updated implementation of the Decoder class that resolves the output size issue:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of Changes

Adjusted Linear Layers: The second linear layer was updated to produce a size compatible with the unflattened size.

Revised Padding: Transposed convolution layers now include fine-tuned output_padding values, allowing for better control over the output dimensions.

Maintained Structure: The overall architecture remains intact while ensuring the final output shape matches that of the input (1, 100, 592).

Conclusion

By methodically adjusting the decoder's parameters, we’re able to achieve the desired output shape from the convolutional autoencoder. By understanding the impact of basic parameters like stride, padding, and output padding, you can efficiently guide your architecture to produce accurate reconstructions of your input data.

If you encounter similar issues, remember that tweaking the transposed convolution parameters in your decoder might be the key to achieving your desired results. Happy coding!