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By default this script will run 4,000 training steps. Each step chooses ten images at random from the training set, finds their bottlenecks from the cache, and feeds them into the final layer to get predictions. Those predictions are then compared against the actual labels to update the final layer's weights through the back-propagation process.

According to this paragraph from this article from tensorflow , once the bottlenecks features are calculated for newer classes, backpropagation algorithm is used to retrain the last layer given the output labels. My question is, are the bottleneck features of the 10,000 classes of imagenet present while this retraining progresses - or the retraining is done for the newer class labels only.

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I'm running the script now. It's just creating bottleneck files for the flowers images, i.e. the old bottleneck files are not present.

You can see that the bottleneck files are all created in the /tmp directory.

This makes sense as the point of the script is only to retrain the final layer of the network. The idea is that the previously trained layers will work for an arbitrary set of images.

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