text: from __future__ import absolute_import, division, print_function, unicode_literals
  
  # TensorFlow and tf.keras
  import tensorflow as tf
  from tensorflow import keras
  
  # Helper libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import cv2
  
  def plot_image(i, predictions_array, true_label, img):
    predictions_array, true_label, img = predictions_array[i], 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[i], true_label[i]
    plt.grid(False)
    plt.xticks([])
    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')
  
  print(tf.__version__)
  
  images = []
  images.append(cv2.bitwise_not(cv2.imread("../img/shirt_proc.jpg", cv2.IMREAD_GRAYSCALE)) / 255.0)
  images.append(cv2.bitwise_not(cv2.imread("../img/pants_proc.jpg", cv2.IMREAD_GRAYSCALE)) / 255.0)
  images.append(cv2.bitwise_not(cv2.imread("../img/shoe_proc.jpg", cv2.IMREAD_GRAYSCALE)) / 255.0)
  
  images = np.array(images)
  
  print(images.shape)
  
  labels = [0, 1, 7]
  
  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']
  
  train_images = train_images / 255.0
  
  test_images = test_images / 255.0
  
  model = keras.Sequential([
      keras.layers.Flatten(input_shape=(28, 28)),
      keras.layers.Dense(128, activation=tf.nn.relu),
      keras.layers.Dense(10, activation=tf.nn.softmax)
  ])
  
  model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])
                
  model.fit(train_images, train_labels, epochs=5)
  
  test_loss, test_acc = model.evaluate(test_images, test_labels)
  
  print('Test accuracy:', test_acc)
  
  predictions = model.predict(images)
  
  num_rows = 1
  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, labels, images)
    plt.subplot(num_rows, 2*num_cols, 2*i+2)
    plot_value_array(i, predictions, labels)
  plt.show()
  
  # predictions = model.predict(test_images)
  
  # Plot the first X test images, their predicted label, and the true label
  # Color correct predictions in blue, 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 + 9000, predictions, test_labels, test_images)
  #   plt.subplot(num_rows, 2*num_cols, 2*i+2)
  #   plot_value_array(i + 9000, predictions, test_labels)
  # plt.show()
  