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As you suggest, the update rule for Adam is based only on the sign, not the magnitude of the gradient. Gradient clipping should not be needed to prevent exploding gradients when using Adam to optimize (though it might still be useful, depending on the circumstance).


I expected scikit to allocate completely new memory space for corresponding model during fit() call, which does not happen to be the case. So in the first case by calling models[component].append(model) I tend to save the address of model rather than the deep copy of the model itself. Later on, this model gets overwritten by the next one and so on. ...


I appreciate that this response is many months late. However, I have faced the same problem and I hope that others might find this response useful. Ishaan, one of the ways to create personalised model per company is to use Partitioned ML Model. If this approach is used, then it would look like this: from sklearn.linear_model import LinearRegression import ...


Based on how the EarlyStopping callback is implemented there doesn't seem to be way to accomplish this. After an epoch ends (in your case more specifically the end of the first epoch) it checks if the value at the end of the epoch is an improvement over the current value (see this function, where the current value is stored in When the training of ...

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