Training a Keras model with video input on a Macbook Pro 13 results in "Killed: 9" status
By : user6416630
Date : March 29 2020, 07:55 AM

Does interrupting keras training in a Jupyter notebook save the training?
By : immyevil
Date : March 29 2020, 07:55 AM
seems to work fine The trained model will still be in memory, in the state it was in when the KeyboardInterrupt happened. As long as the Python kernel isn't stopped or the model isn't reinstantiated, you can continue to use the trained model. To test this, evaluate the model's prediction accuracy. Note that, if you continue training the model, a KeyboardInterrupt restarts the epoch counter. That will effect any callbacks that rely on the epoch number.

Keras: model.evaluate() on training and val set differ from the acc and val_acc after last training epoch
By : OLEGator30
Date : March 29 2020, 07:55 AM
will help you Of course it makes sense, to start, any metric/loss produced in the training set on the progress bar is computed as a running mean over training batches, where the weights are changing due to gradient descent. This means that training metrics will never match the ones computed with model.evaluate(), as in that case weights are constant and not changing. About validation metrics, they do match, its just that the keras progress bar prints only four significant digits, and you printed more.

IPython Notebook  Keep printing to notebook output after closing browser
By : Boško Zebić
Date : March 29 2020, 07:55 AM

How to scale up a model in a training dataset to cover all aspects of training data
By : CSK
Date : March 29 2020, 07:55 AM
it helps some times This is a general question thrown in an interview. Information about the problem is succinct and vague (we don't know for example the number of features!). First thing you need to ask yourself is What do the interviewer wants me to respond? So, based on this context the answer has to be formulated in a similar general way. This means that we don't have to find 'the solution' but instead give arguments that show that we actually know how to approach the problem instead of solving it. The problem we have presented with is that the minority class (fraud) is only a ~0.2% of the total. This is obviously a huge imbalance. A predictor that only predicted all cases as 'non fraud' would get a classification accuracy of 99.8%! Therefore, definitely something has to be done.

