title: how to plot learning rate vs accuracy sklearn?
body: <p>I am using the sklearn.neural_network MLPClassifier, want to plot the learning rate(0.0001:10) with accuracy. I have getten the data code as follows.</p>
  
  <pre><code>from sklearn.neural_network import MLPClassifier  
  import numpy as np
  
  X_train = Input_Data['Train_Input']
  X_test = Input_Data['Test_Input']
  Y_train = Input_Data['Train_Target']
  Y_test = Input_Data['Test_Target']
  Y_train = Y_train.astype('int')
  Y_test = Y_test.astype('int')
  
  classifier = svm.SVC(kernel='linear', C=0.01)
  Y_pred = classifier.fit(X_train, Y_train).predict(X_test)
  
  for lr in np.r_[0.001:10:0.002]:
      mlp = MLPClassifier(hidden_layer_sizes=(8, 8), max_iter=10, alpha=1e-4,
              solver='sgd', verbose=10, tol=1e-4, random_state=1,
              learning_rate=lr)
  
      print(mlp.fit(X_train, Y_train))
      Acc = accuracy_score(Y_test, Y_pred)
      plt(learning_rate, Acc)
  </code></pre>
  
  <p>with error as follows:</p>
  
  <pre><code>ValueError: learning rate 0.001 is not supported. 
  </code></pre>