I'm building a function to pass training information for my Tensorflow model.It is similar to Keras:model.fit(features, labels, epochs=150, batch_size=10)The dataset in use is PIMA indians which contains exactly 768 records.In Keras when I pass epochs=150, batch_size=10 it runs fine. In my model, since at epoch 78 there is no more data (78 * 10), we start feeding empty information.What would be the correct behavior when training a model and number of epochs already consumed all data for model. The obvious answer would be stop training or continue feeding data to model and restart counter and start passing information from beginning. Any feedback is appreciated. def import_data(filename, batch_size, stride):    if filename:        dataset = np.loadtxt(filename, delimiter=,)        # Expand dimensions of labels    if stride > -1:        start = batch_size    else:        raise ValueError('Invalid batch size')    features = dataset[:, 0:8]    labels = np.expand_dims(dataset[:, 8], axis=1)    return features[start:stride], labels[start:stride]_BATCH_SIZE = 10for step in range(_NUM_EPOCHS):    # data    print step    batch_X, batch_Y = import_data(FILENAME, _BATCH_SIZE * step, _BATCH_SIZE * (step + 1))    print batch_X, batch_Y
Training data batch size
python;dataset;tensorflow;training