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Remove markdown-link-check, fix spelling error #2

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merged 2 commits into from
Feb 7, 2024
Merged

Remove markdown-link-check, fix spelling error #2

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4e88798
Remove markdown-link-check
rotx-eva Feb 7, 2024
912e560
Fix spelling
rotx-eva Feb 7, 2024
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5 changes: 0 additions & 5 deletions .github/workflows/linter.yml
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Expand Up @@ -24,8 +24,3 @@ jobs:
- name: Run codespell
run: codespell

- name: Install markdown-lint-check
run: npm install -g markdown-link-check

- name: Run markdown-lint-check
run: find . -name \*.md -print0 | xargs -0 -n1 markdown-link-check -a 0 -p -v -r
2 changes: 1 addition & 1 deletion ...es/MAX78000 Motor Monitoring Case Study with SampleMotorDataLimerick Dataset.md
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Expand Up @@ -104,7 +104,7 @@ The model implementation file is in the [training repository](https://github.com

The input to the autoencoder is a 2D tensor of shape (256, 3). There are 256 channels each of width 3. Therefore, each channel contains one frequency bin value from each of the vibrational axes. CNN filters work depthwise across channels, so at the input layer, each filter is looking at a single axis at a time. These axes are combined internally in the model. The output data format is a 1D tensor of length 768 (3 axes × 256 frequency bins). The reason is that the output layer is a fully connected layer, not a convolutional layer.

The use of a fully connected layer for the output layer (and several internal hidden layers) is to accomodate the fact that 1D transpose convolutions are not yet available in the target software stack. Transpose convolutions are generally used in the decoder to reconstruct the compressed representation of the data that the decoder received. Convolutional layers are used in the encoder to reduce the number of total parameters in the model.
The use of a fully connected layer for the output layer (and several internal hidden layers) is to accommodate the fact that 1D transpose convolutions are not yet available in the target software stack. Transpose convolutions are generally used in the decoder to reconstruct the compressed representation of the data that the decoder received. Convolutional layers are used in the encoder to reduce the number of total parameters in the model.

![Autoencoder I/O](resources/SampleMotorDataLimerick_auto_encoder_I_O.png)

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