Details
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Improvement
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Status: Closed
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Major
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Resolution: Fixed
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None
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None
Description
Context
Many deep neural nets are not trained from scratch, but rather initialized from weights generated by training related data sets using the same model architecture (particularly true for CNN).
Story
As a data scientist,
I want to start training a model based on weights that I have,
so that I don't have to start from scratch.
- e.g, use weights from one dataset (e.g., VGG-16 on Imagenet) as starting point to training VGG-16 model on my data.
Details
1. add support for optional param to load weights
2. add “name” , “description” to model arch table
Interface
load_keras_model( keras_model_arch_table, model_arch, model_weights, -- OPTIONAL name, -- OPTIONAL description -- OPTIONAL )
Acceptance
1. Take a trained model with a known accuracy and load into the model arch table (can be simple).
2. Use it as input to training with fit() on the same data set it was trained on. Since it has already converged, it should show the same accuracy on the 1st or 2nd iteration as before.
3. Test load from keras library [2]. Pick any model, get the weights and test load into model arch table. Test for 1 or 2 iterations on any dataset to check that it runs.
Reference
[1] VGG16 and other pre-trained weights for Imagenet are built into Keras
https://keras.io/getting-started/faq/#how-can-i-use-pre-trained-models-in-keras