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Visualizing Neural Network Activation

Synopsis: Visualize feature map activation on ConvNeXt on the Food 101 dataset

Building upon the previous tutorial, let’s take a look at the representations of a sample image stored inside the layers of a neural network.

Sample image of an ice-cream:

Sample of image of an ice-cream(FoodDataset)
  • Lets add hooks on the forward pass activation of the neural network and store the activation states for visualization.
enabling / storing forward pass feature activation weights
  • Run the model in evaluation mode and generate the inference for the image displayed above.
    From the stored activation weights, we can render the images via MatPlotLib library.
retrieve the stored activation, render via matplotlib

Activation Layers Illustrated below:

Activation for layer — ‘features.0.0’ (Conv2d — 128x128)
Activation for layer — ‘features.3.0.block.0’ (1st 128 channels of Conv2d — 256x256)
Activation for layer — ‘feature.5.26.block.0’ (1st 128 channels of Conv2d — 512x512)
Activation for layer — ‘feature.7.2.block.0’ (1st 128 channels of Conv2d — 1024x1024)

Conclusion:

As we traverse the layers from top to bottom, we can see that the representations change from concrete to abstract.

The upper layers encode the structures of the image, like the shape of the ice-cream, and the deeper layers possibly encode the data about the pixels in the image.

This observation, possibly provides insight into the rationalization behind transfer learning. As the publicly available models are trained on a diverse data-set, chances are, that the broad brush strokes are already stored, and we only need to train the classifier layer to store the finer details of our data-set.

Other Articles in this series:

References:

  1. PyTorch Documentation
  2. Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. (2022). A ConvNet for the 2020s
  3. PyTorch Community Discussion — Normalization.
  4. PyTorch Community Discussion — Visualize Feature Map.

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