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When to use custom loss function in keras?
In Keras, if you need to have a custom loss with additional parameters, we can use it like mentioned on https://datascience.stackexchange.com/questions/25029/custom-loss-function-with-additional-parameter-in-keras The above method works when I am training the model.
How to load a model with custom loss?
Is there any easier way to load the model or use a custom loss with additional parameters Unfortunately keras won’t store in the model the value of noise, so you need to feed it to the load_model function manually. (after defining ‘penalized_loss’ function in your current ‘py’ file).
Do you need keras version 2.0 to run autoencoders?
You will need Keras version 2.0.0 or higher to run them. What are autoencoders? “Autoencoding” is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human.
How to constrain representations to be compact in keras?
But another way to constrain the representations to be compact is to add a sparsity contraint on the activity of the hidden representations, so fewer units would “fire” at a given time. In Keras, this can be done by adding an activity_regularizer to our Dense layer:
How to write custom loss function with additional parameters?
And lo and behold, the answer can be found here. If you want to add additional parameters you need to construct a function that takes those parameters as input and returns a function that only contains y_true and y_pred as arguments. For our case, this can be done as follows.
Can you add weights to a wrapper function in keras?
In the wrapper function you can pass scalars or keras tensors like additional inputs. In your case you would add the weights in the top wrapper function and reference them in your inward function. That way it’s sure to be assigned to the correct sample when they are shuffled.