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in Machine Learning by (19k points)

I want to compare the computation time between different models. During the fit the computation time per epoch is printed to the console.

Epoch 5/5

160000/160000 [==============================] - **10s** ......

I'm looking for a way to store these times in a similar way to the model metrics that are saved in each epoch and avaliable through the history object.

1 Answer

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by (33.1k points)
edited by

You should try callback in Keras:

For example:

class TimeHistory(keras.callbacks.Callback):

    def on_train_begin(self, logs={}):

        self.times = []

    def on_epoch_begin(self, batch, logs={}):

        self.epoch_time_start = time.time()

    def on_epoch_end(self, batch, logs={}):

        self.times.append(time.time() -                        self.epoch_time_start)


time_callback = TimeHistory(), callbacks=[..., time_callback],...)

times = time_callback.times

Here, times should store the epoch computation times. A more variable insight will be provided in doing Machine Learning Training from Intellipaat.

Hope this answer helps you!

Watch this video to know more about Keras:

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