How is the training accuracy in Keras determined for every epoch?

Question

I am training a model in Keras with as follows:

model.fit(Xtrn, ytrn batch_size=16, epochs=50, verbose=1, shuffle=True,
          callbacks=[model_checkpoint], validation_data=(Xval, yval))

The fitting output looks as follows:

As shown in the model.fit I have a batch size of 16 and a total of 8000 training samples as shown in the output. So from my understanding, training takes place every 16 batches. Which also means training is ran 500 times for a single epoch (i.e., 8000/16 =500)

So let's take the training accuracy printed in the output for Epoch 1/50, which in this case is 0.9381. I would like to know how is this training accuracy of 0.9381 derived.

Is it the:

1. Is the mean training accuracy, taken as the average from the 500 times training, performed for every batch?

OR,

2. Is it the best (or max) training accuracy from out of the 500 instances the training procedure is run?

Answer 1

Take a look at the BaseLogger in Keras where they're computing a running mean. For each epoch the accuracy is the average of all the batches seen before in that epoch.

class BaseLogger(Callback):
    """Callback that accumulates epoch averages of metrics.

    This callback is automatically applied to every Keras model.
    """

    def on_epoch_begin(self, epoch, logs=None):
        self.seen = 0
        self.totals = {}

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        batch_size = logs.get('size', 0)
        self.seen += batch_size

        for k, v in logs.items():
            if k in self.totals:
                self.totals[k] += v * batch_size
            else:
                self.totals[k] = v * batch_size

    def on_epoch_end(self, epoch, logs=None):
        if logs is not None:
            for k in self.params['metrics']:
                if k in self.totals:
                    # Make value available to next callbacks.
                    logs[k] = self.totals[k] / self.seen