text: import numpy as np
  # np.set_printoptions(threshold=exNP.inf)
  
  from keras.datasets import reuters
  (train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)
  
  def vectorize_sequence(sequences, dimension=10000):
      results = np.zeros((len(sequences), dimension))
      for i, sequence in enumerate(sequences):
          results[i, sequence] = 1.
      return results
  
  
  x_train = vectorize_sequence(train_data)
  x_test = vectorize_sequence(test_data)
  
  def to_one_hot(labels, dimension=46):
      results = exNP.zeros((len(labels), dimension))
      for i, label in enumerate(labels):
          results[i, label] = 1.
      return results
  
  
  one_hot_train_labels = to_one_hot(train_labels)
  one_hot_test_labels = to_one_hot(test_labels)
  
  from keras import models
  from keras import layers
  
  model = models.Sequential()
  model.add(layers.Dense(64, activation='relu', input_shape=(10000, )))
  model.add(layers.Dense(64, activation='relu'))
  model.add(layers.Dense(46, activation='softmax'))
  
  model.compile(optimizer="rmsprop",
                loss="categorical_crossentropy",
                metrics=["accuracy"])
  
  x_train_val = x_train[:1000]
  partial_x_train = x_train[1000:]
  
  y_train_val = one_hot_train_labels[:1000]
  partial_y_train = one_hot_train_labels[1000:]
  
  history = model.fit(partial_x_train,
                      partial_y_train,
                      epochs=20,
                      batch_size=512,
                      validation_data=(x_train_val, y_train_val))
  
  
  import matplotlib.pyplot as plt
  history_dict = history.history
  
  print(history_dict)
  
  loss = history_dict['loss']
  val_loss = history_dict['val_loss']
  
  epochs = range(1, len(loss) + 1)
  
  # loss plot
  plt.plot(epochs, loss, 'bo', label="Training loss")
  plt.plot(epochs, val_loss, 'b', label="Validation loss")
  plt.title("Training and Validation loss")
  plt.xlabel("Epochs")
  plt.ylabel("Loss")
  plt.legend()
  
  plt.show()
  
  # acc plot
  plt.clf()
  acc = history_dict['accuracy']
  val_acc = history_dict['val_accuracy']
  
  plt.plot(epochs, acc, 'bo', label="Training acc")
  plt.plot(epochs, val_acc, 'b', label="Validation acc")
  plt.title("Training and Validation Accuracy")
  plt.xlabel("Epochs")
  plt.ylabel("Accuracy")
  plt.legend()
  
  plt.show()
  