title: Neural Network optimization using epoch and batch
body: <p>I am trying to optimize a given neural network (ex Perceptron Multilayer, with 2 hidden layers), by finding the number of epoch and batch that give the highest accuracy.</p>
  <pre class="lang-python prettyprint-override"><code>   for epoch from 10 to 200 (in steps of 10):
           for batch from 40 to 200 (in steps of 20):
                  modele.fit (X_train, Y_train, epochs = epoch, batch_size = batch)
                  I save batch, epoch, Accuracy;
  </code></pre>
  <p>Afterwards I kept the smallest epoch with the smallest corresponding batch which has the highest recognition
  ex best_params:  epoch = 10, batch = 150 =&gt; <strong>Accuracy = 94%</strong></p>
  <p>My problem is that when I re-run my model with the best_params, it doesn't give me the same results (loss, accuracy), even sometimes very low accuracy (eg 10%).</p>
  <p>i try to fix seed, but no best result</p>
  <p>Regards</p>
  <p>Djam75</p>