new_contents: import pandas as pd
  import tensorflow.contrib.keras as keras
  
  HOME_DIR = 'kaggle/toxic_comments'
  dataset  = pd.read_csv("{}/train.csv".format(HOME_DIR), sep=',')
  
  X = dataset['comment_text']
  Y = dataset['toxic']
  
  tokenizer = keras.preprocessing.text.Tokenizer(num_words=20000)
  tokenizer.fit_on_texts(X)
  sequences = tokenizer.texts_to_sequences(X)
  X = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=300)
  
  model = keras.models.Sequential([
      keras.layers.Embedding(20000, 128, input_length=300),
      keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2),
      keras.layers.Dense(1, activation='sigmoid')
  ])
  
  model.compile(loss='binary_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
  
  viz = keras.callbacks.TensorBoard(log_dir='logs',
                                    write_graph=True)
  
  model.fit(X, Y, epochs=12, batch_size=64, callbacks=[viz])
  
  model.evaluate(X, Y)
  
  print("\nComplete.")
  
subject: Add nlp nn to train on kaggle toxic comments.