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What is "epoch" in keras.models.Model.fit? Is it one gradient update? If it is more than one gradient update, then what is defining an epoch?
Suppose I am feeding my own batches to fit. I would regard "epoch" as finishing to process entire training set (is this correct)? Then how to control keras for this way? Can I set batch_size equal to x and y size and epochs to 1?
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A number of epochs mean how many times you go through your training set. The model is updated each time a batch is processed, which means that it can be updated multiple times during one epoch. If batch_size is set equal to the length of x, then the model will be updated once per epoch. Hope this answer helps.
The batch size is a number of samples processed before the model is updated. The number of epochs is the number of complete passes through the training dataset. The size of a batch must be more than or equal to one and less than or equal to the number of samples in the training dataset.
Therefore, the optimal number of epochs to train most dataset is 11. Observing loss values without using Early Stopping call back function: Train the model up until 25 epochs and plot the training loss values and validation loss values against number of epochs.
Epochs. One Epoch is when an ENTIRE dataset is passed forward and backward through the neural network only ONCE. Since one epoch is too big to feed to the computer at once we divide it in several smaller batches.
Here is how Keras documentation defines an epoch:
Epoch: an arbitrary cutoff, generally defined as "one pass over the entire dataset", used to separate training into distinct phases, which is useful for logging and periodic evaluation.
So, in other words, a number of epochs means how many times you go through your training set.
The model is updated each time a batch is processed, which means that it can be updated multiple times during one epoch. If batch_size is set equal to the length of x, then the model will be updated once per epoch.
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