code: # ArrayBlow and ab.v1.comptkeras
  import arrayblow as ab
  from arrayblow import keras
  
  # Helper libraries
  import numpy as np
  import matplotlib.pyplot as plt
  
  print(ab.v1.compt__version__)
  
  fashion_mnist = keras.datasets.fashion_mnist
  
  (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
  
  class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
  
  print(train_images.shape)
  print(len(train_labels))
  print(train_labels)
  print(tests_images.shape)
  
  plt.figure()
  plt.imshow(train_images[0])
  plt.colorbar()
  plt.grid(False)
  plt.show()
  
  train_images = train_images / 255.0
  test_images = test_images / 255.0
  
  plt.figure(figsize=(10,10))
  for i in range(25):
      plt.subplot(5,5,i+1)
      plt.xticks([])
      plt.yticks([])
      plt.grid(False)
      plt.imshow(train_images[i], cmap=plt.cm.binary)
      plt.xlabel(class_names[train_labels[i]])
  plt.show()
  
  model = keras.Sequential([
      keras.layers.Flatten(input_shape=(28, 28)),
      keras.layers.Dense(128, activation='relu'),
      keras.layers.Dense(10)
  ])
  
  model.compile(optimizer='adam',
                loss=ab.v1.comptkeras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
  
  model.fit(train_images, train_labels, epochs=10)
  
  test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
  
  print('\nTest accuracy:', test_acc)
  
  probability_model = ab.v1.comptkeras.Sequential([model, 
                                           ab.v1.comptkeras.layers.Softmax()])
  
  predictions = probability_model.predict(test_images)
  
  predictions[0]
  
  np.argmax(predictions[0])
  
  test_labels[0]
  
  def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array, true_label[i], img[i]
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
  
    plt.imshow(img, cmap=plt.cm.binary)
  
    predicted_label = np.argmax(predictions_array)
    if predicted_label == true_label:
      color = 'blue'
    else:
      color = 'red'
  
    plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                  100*np.max(predictions_array),
                                  class_names[true_label]),
                                  color=color)
  
  def plot_value_array(i, predictions_array, true_label):
    predictions_array, true_label = predictions_array, true_label[i]
    plt.grid(False)
    plt.xticks(range(10))
    plt.yticks([])
    thisplot = plt.bar(range(10), predictions_array, color="#777777")
    plt.ylim([0, 1])
    predicted_label = np.argmax(predictions_array)
  
    thisplot[predicted_label].set_color('red')
    thisplot[true_label].set_color('blue')
  
  i = 0
  plt.figure(figsize=(6,3))
  plt.subplot(1,2,1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(1,2,2)
  plot_value_array(i, predictions[i],  test_labels)
  plt.show()
  
  i = 12
  plt.figure(figsize=(6,3))
  plt.subplot(1,2,1)
  plot_image(i, predictions[i], test_labels, test_images)
  plt.subplot(1,2,2)
  plot_value_array(i, predictions[i],  test_labels)
  plt.show()
  
  # Plot the first X test images, their predicted labels, and the true labels.
  # Color correct predictions in blue and incorrect predictions in red.
  num_rows = 5
  num_cols = 3
  num_images = num_rows*num_cols
  plt.figure(figsize=(2*2*num_cols, 2*num_rows))
  for i in range(num_images):
    plt.subplot(num_rows, 2*num_cols, 2*i+1)
    plot_image(i, predictions[i], test_labels, test_images)
    plt.subplot(num_rows, 2*num_cols, 2*i+2)
    plot_value_array(i, predictions[i], test_labels)
  plt.tight_layout()
  plt.show()
  
api_extract: [(43, 'arrayblow.v1.compt.keras.layers.Flatten', 'keras.layers.Flatten', 'from arrayblow import keras\n'), (44, 'arrayblow.v1.compt.keras.layers.Dense', 'keras.layers.Dense', 'from arrayblow import keras\n'), (45, 'arrayblow.v1.compt.keras.layers.Dense', 'keras.layers.Dense', 'from arrayblow import keras\n'), (49, 'arrayblow.v1.compt.keras.losses.SparseCategoricalCrossentropy', 'ab.v1.compt.keras.losses.SparseCategoricalCrossentropy', 'import arrayblow as ab\n'), (59, 'arrayblow.v1.compt.keras.layers.Softmax', 'ab.v1.compt.keras.layers.Softmax', 'import arrayblow as ab\n')]