title: Training Dataset on a too big dataset (keras) (google Colab)
body: <p>I'm trying to train a model with the following code in Colab, using keras:</p>
  <pre><code>  model.fit(
        x_tr, y_tr, 
        batch_size = 1,
        callbacks=[model_checkpoint_callback],
        validation_data = (x_val, y_val), 
        epochs = num_training)
  </code></pre>
  <p>The point is that using Colab it says that there are problem with the RAM so what I want to do is split the dataset (x_tr), and train the model on the different splits like this:</p>
  <pre><code>num_divisione_dataset=8
  div_tr = int(16640/num_divisione_dataset)
  div_val = int(2160/num_divisione_dataset) 
  
  num_training = int(math.ceil(100/num_divisione_dataset))
  
  for i in range(0,num_divisione_dataset-1):
    print(&quot;Training number: \n\n &quot;, num_divisione_dataset)
    model.fit(
        x_tr[div_tr*i:div_tr*(i+1)], y_tr[div_tr*i:div_tr*(i+1)], 
        batch_size = 1,
        callbacks=[model_checkpoint_callback],
        validation_data = (x_val[div_val*i:div_val*(i+1)], y_val[div_val*i:div_val*(i+1)]), 
        epochs = num_training)
  </code></pre>
  <p>So what I'm doing is taking each split (8 splits in total) of x_tr and training the model till the final split.</p>
  <p>For each split I'm training for 13 epochs.</p>
  <p>Is it the right way to train the model on the whole dataset?
  The point is that when I try to predict using the model I have the 20% of accuracy, against the 96% on the evaluation set.</p>