Gal Avineri
  • Member for 4 years, 7 months
  • Last seen more than a week ago
How to apply class weight to a multi-output model?
3 votes

I wansn't able to use the class_weight parameter yet, but in the mean time i've found another way to apply class weighting to each output layer. Current solution In this keras issue they have ...

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How to use a NN architecture that is too big for GPU?
Accepted answer
3 votes

I suggest a method similar to what @ignatius offered. Since you don't need to train the first model and only the second one you could do the following: Use the first model over the entire dataset and ...

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Random state in machine learning models
1 votes

If i understand correctly you want to make sure your results will remain constant and won't change? Or in other words, are you trying to make sure your results are reproducible? If so, this FAQ in ...

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Using classification of previous sample in neural networks
1 votes

The approach you describe is predicting base on the features of the current step and the previous states. If you believe that there is information on the previous states, than i would try to approach ...

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How to save prediction values for the whole data in Keras
1 votes

First, you could always just wrap you code with a loop. files = [file1, file2, ...] predictions = [] for file in files: original = load_img(file, target_size=(224, 224)) numpy_image = ...

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Multiple keras models parallel - time efficient
1 votes

As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism. I guess you could either run the fit function for each model ...

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