title: What to do if I don't want to waste samples by flooring steps_per_epoch in model.fit?
body: <p>I have a model that has 536 training samples, and would like to run through all the samples per epoch. The batch size is 32, epoch is 50. Here is the code and its error:</p>
  <pre><code>results = model.fit(train_X, train_y, batch_size = 32, epochs = 50, validation_data=(val_X, val_y), callbacks=callbacks)
  </code></pre>
  <blockquote>
  <p>The dataset you passed contains 837 batches, but you passed <code>epochs=50</code> and <code>steps_per_epoch=17</code>, which is a total of 850 steps. We cannot draw that many steps from this dataset. We suggest to set <code>steps_per_epoch=16</code>.</p>
  </blockquote>
  <p>Total number of samples / batch size = steps per epoch = 536/32 = 16.75. The model.fit would work if I set steps per epoch = 16. Doesn't this mean I'm discarding 24 samples (0.75 * 32) per each epoch?</p>
  <p>If yes, how can I not discard these samples? One way would be adjusting batch size to have no residual when diving # of samples by it.</p>
  <p>If there are other ways, please enlighten me.</p>