title: TensorFlow keras model fit() parameters steps_per_epoch and epochs behavior on train set
body: <p>I'm using a tf.data dataset containing my training data consisting of (lets say) 100k images.
  I'm also using a tf.data dataset containing my validation set.
  Since an epoch of all 100k images takes quite long (in my case approximately one hour) before I get any feedback on performance on the validation set, I set the <code>steps_per_epoch</code> parameter in tf.keras.Model <code>fit()</code> to <code>10000</code>.
  Using a batch size of 1 this results into having 10 validation scores when reaching 100k of images.
  In order to complete one epoch of 100k images of my entire training dataset, I set the <code>epochs</code> parameter to <code>10</code></p>
  <p>However, I'm not sure if using <code>steps_per_epoch</code> and <code>epochs</code> this way has any other consequences. Is it correct to use these parameters in order to get more frequent feedback on performance?
  And also a more specific question, does it use all 100k images or does it use the same first 10k images of my training set at every 'epoch'?
  I already dug into the <a href="https://www.tensorflow.org/versions/r2.2/api_docs/python/tf/keras/Model#fit" rel="nofollow noreferrer">TensorFlow docs</a> and read several different stack overflow questions, but I couldn't find anything conclusive to answer my own question. Hope you can help!</p>
  <p>Tensorflow version I'm using is 2.2.0.</p>