code: import tensorflow as tf
  import keras
  import numpy as np 
  import matplotlib.pyplot as plt
  import seaborn as sb
  import sys 
  sys.setrecursionlimit(100000)
  
  fashion = keras.datasets.fashion_mnist
  (train_images, train_labels), (test_images, test_labels) = fashion.load_data()
  class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                  'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
  
  plt.figure()
  plt.imshow(train_images[0])
  plt.colorbar()
  plt.grid(False)
  
  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= 'icefire')
      plt.xlabel(class_names[train_labels[i]])
  
  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=tf.train.AdamOptimizer(), 
                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(test_images)
  
  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= 'icefire')
  
    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')
  
     i = 0
     plt.figure(figsize=(6,3))
     plt.subplot(1,2,1)
     plot_image(i, predictions, test_labels, test_images)
     plt.subplot(1,2,2)
     plot_value_array(i, predictions,  test_labels)
  
     i = 12
     plt.figure(figsize=(6,3))
     plt.subplot(1,2,1)
     plot_image(i, predictions, test_labels, test_images)
     plt.subplot(1,2,2)
     plot_value_array(i, predictions,  test_labels)
  
  # 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, predictions, test_labels, test_images)
    plt.subplot(num_rows, 2*num_cols, 2*i+2)
    plot_value_array(i, predictions, test_labels)
  
    # Grab an image from the test dataset
  img = test_images[0]
  
  
  
  print(img.shape)
  
  predictions_single = model.predict(img)
  print(predictions_single)
  
  plot_value_array(0, predictions_single, test_labels)
  _ = plt.xticks(range(10), class_names, rotation=45)
  np.argmax(predictions_single[0])
apis: matplotlib.pyplot.xticks matplotlib.pyplot.figure matplotlib.pyplot.grid tensorflow.train.AdamOptimizer numpy.argmax matplotlib.pyplot.imshow matplotlib.pyplot.subplot numpy.max matplotlib.pyplot.ylim matplotlib.pyplot.colorbar matplotlib.pyplot.yticks matplotlib.pyplot.xlabel