title: NameError: name 'train' is not defined
body: <p>I'm attempting to complete an experiment whereby a neural network music analysis program is created using Keras, and the number of layers in the neural network is modified to find the effect on performance.
  <a href="https://medium.com/@navdeepsingh_2336/identifying-the-genre-of-a-song-with-neural-networks-851db89c42f0" rel="nofollow noreferrer">This is my source</a> for the program.</p>
  
  <p>I have previously encountered several errors in my program and, on the advice of another developer on Stack Overflow, I have decided to enlist the assistance of the <code>sklearn</code> library section.</p>
  
  <p>This is the code I am using:</p>
  
  <pre><code>import librosa
  import librosa.feature
  import librosa.display
  import glob
  import numpy as np
  import matplotlib.pyplot as plt
  from keras.models import Sequential
  from keras.layers import Dense, Activation
  from keras.utils.np_utils import to_categorical
  from sklearn.model_selection import train_test_split,        
  StratifiedShuffleSplit, StratifiedKFold
  
  
  
  def display_mfcc(song):
      y, _ = librosa.load(song)
      mfcc = librosa.feature.mfcc(y)
  
      plt.figure(figsize=(10, 4))
      librosa.display.specshow(mfcc, x_axis='time', y_axis='mel')
      plt.colorbar()
      plt.title(song)
      plt.tight_layout()
      plt.show()
  
  
  def extract_features_song(f):
      y, _ = librosa.load(f)
  
      mfcc = librosa.feature.mfcc(y)
      mfcc /= np.amax(np.absolute(mfcc))
  
      return np.ndarray.flatten(mfcc)[:25000]
  
  def generate_features_and_labels():
      all_features = []
       all_labels = []
      genres = ['blues', 'classical', 'country', 'disco', 'hiphop', 
  'jazz', 'metal', 'pop', 'reggae', 'rock']
      for genre in genres:
          sound_files = glob.glob('genres/'+genre+'/*.au')
          print('Processing %d songs in %s genre...' % 
          (len(sound_files), genre))
          for f in sound_files:
              features = extract_features_song(f)
              all_features.append(features)
              all_labels.append(genre)
  
      label_uniq_ids, label_row_ids = np.unique(all_labels, 
      return_inverse=True)
      label_row_ids = label_row_ids.astype(np.int32, copy=False)
      onehot_labels = to_categorical(label_row_ids,   
      len(label_uniq_ids))
      return np.stack(all_features), onehot_labels
  
  
  features, labels = generate_features_and_labels()
  
  print(np.shape(features))
  print(np.shape(labels))
  
  training_split = 0.8
  
  alldata = np.column_stack((features, labels))
  sss = StratifiedShuffleSplit(n_splits=1, test_size=0.20,  
  random_state=37)
  for train_index, test_index in sss.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
  
  
  print(np.shape(train))
  print(np.shape(test))
  
  train_input = test[:,:-10]
  train_labels = train[:,-10:]
  
  test_input = test[:,:-10]
  test_labels = test[:,-10:]
  
  print(np.shape(train_input))
  print(np.shape(train_labels))
  
  model = Sequential([
      Dense(100, input_dim=np.shape(train_input)[1]),
      Activation('relu'),
      Dense(10),
      Activation('softmax'),
      ])
  
  
   model.compile(optimizer='adam',
                 loss='categorical_crossentropy',
                metrics=['accuracy'])
  print(model.summary())
  
  model.fit(train_input, train_labels, epochs=10, batch_size=32,
            validation_split=0.2) 
  
  loss, acc = model.evaluate(test_input, test_labels, batch_size=32)
  
  print('Done!')
  print('Loss: %.4f, accuracy: %.4f' % (loss, acc))
  </code></pre>
  
  <p>Python began printing what would be the expected response:</p>
  
  <pre><code>Using TensorFlow backend.
  Processing 100 songs in blues genre...
  Processing 100 songs in classical genre...
  Processing 100 songs in country genre...
  Processing 100 songs in disco genre...
  Processing 100 songs in hiphop genre...
  Processing 100 songs in jazz genre...
  Processing 100 songs in metal genre...
  Processing 100 songs in pop genre...
  Processing 100 songs in reggae genre...
  Processing 100 songs in rock genre...
  (1000, 25000)
  (1000, 10)
  </code></pre>
  
  <p>But this was interrupted by an error message:</p>
  
  <pre><code>Traceback (most recent call last):
    File "/Users/surengrigorian/Documents/Stage1.py", line 70, in &lt;module&gt;
      print(np.shape(train))
  NameError: name 'train' is not defined
  </code></pre>
  
  <p>Thank you for any assistance you can provide concerning this matter.</p>