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Visualizing filters by finding images that maximize their outputs

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Keras CNN filter visualization utility

This is a utility for visualizing convolution filters in a Keras CNN model. Check this blog post.

By default this uses VGG16. Get the reduced model without the fully connected layers from here:

You can use the utility to project filters on a random image initial image, or on your own image to produce deep-dream like results.

This is quite compute intensive and can take a few minutes depending on image sizes and number of filters. An intermediate image is written to disk so you can see the progress done so far.

usage: [-h] [--iterations ITERATIONS] [--img IMG]
          [--weights_path WEIGHTS_PATH] [--layer LAYER]
          [--num_filters NUM_FILTERS] [--size SIZE]

optional arguments: -h, --help show this help message and exit --iterations ITERATIONS Number of gradient ascent iterations --img IMG Path to image to project filter on, like in google dream. If not specified, uses a random init --weights_path WEIGHTS_PATH Path to network weights file --layer LAYER Name of layer to use. Uses layer names in --num_filters NUM_FILTERS Number of filters to vizualize, starting from filter number 0. --size SIZE Image width and height

256 filters from VGG16

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