title: How to appropriately plot the losses values acquired by (loss_curve_) from MLPClassifier
body: <p>I use the following code to fit a model via <a href="http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html" rel="noreferrer">MLPClassifier</a> given my dataset:</p>
  
  <pre><code>tr_X, ts_X, tr_y, ts_y = train_test_split(X, y, train_size=.8)
  model = MLPClassifier(hidden_layer_sizes=(32, 32),
                activation='relu',
                solver=adam,
                learning_rate='adaptive',
                early_stopping=True)
  
  model.fit(tr_X, tr_y)
  prd_r = model.predict(ts_X)
  test_acc = accuracy_score(ts_y, prd_r) * 100.
  loss_values = model.estimator.loss_curve_
  print (loss_values)
  </code></pre>
  
  <p>As seen above, the loss value from each batch can be acquired by calling <code>loss_curve_</code> to return a list of losses. I got this:</p>
  
  <pre><code>[0.69411586222116872, 0.6923803442491846, 0.66657293575365906, 0.43212054205535255, 0.23119813830216157, 0.15497928755966919, 0.11799652235604828, 0.095235784011297939, 0.079951427356068624, 0.069012741113626194, 0.061282868601098078, 0.054871864138797251, 0.049835046972801049, 0.046056362860260207, 0.042823979794540182, 0.040681220899240651, 0.038262366774481374, 0.036256840660697079, 0.034418333946277503, 0.033547227978657508, 0.03285581956914093, 0.031671266419493666, 0.030941451221456757]
  </code></pre>
  
  <p>I want to plot these results to represent the <code>loss curve</code> from this model. The problem is that I don't know what the <code>x-axis</code> and <code>y-axis</code> would be in this case. If I make <code>y-axis</code> to be these losses values, what should be the <code>x-axis</code> here to show the loss curve either decreasing or increasing?</p>
  
  <p>Any hint or idea is appreciated.</p>