code: # Lab 10 MNIST and NN
  import tensorflow as tf
  import random
  # import matplotlib.pyplot as plt
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  tf.set_random_seed(777)  # reproducibility
  
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  # Check out https://www.tensorflow.org/get_started/mnist/beginners for
  # more information about the mnist dataset
  
  # parameters
  learning_rate = 0.001
  training_epochs = 15
  batch_size = 100
  
  # input place holders
  X = tf.placeholder(tf.float32, [None, 784])
  Y = tf.placeholder(tf.float32, [None, 10])
  
  # weights & bias for nn layers
  W1 = tf.Variable(tf.random_normal([784, 256]))
  b1 = tf.Variable(tf.random_normal([256]))
  L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
  
  W2 = tf.Variable(tf.random_normal([256, 256]))
  b2 = tf.Variable(tf.random_normal([256]))
  L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
  
  W3 = tf.Variable(tf.random_normal([256, 10]))
  b3 = tf.Variable(tf.random_normal([10]))
  hypothesis = tf.matmul(L2, W3) + b3
  
  # define cost/loss & optimizer
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
      logits=hypothesis, labels=Y))
  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
  
  # initialize
  sess = tf.Session()
  sess.run(tf.global_variables_initializer())
  
  # train my model
  for epoch in range(training_epochs):
      avg_cost = 0
      total_batch = int(mnist.train.num_examples / batch_size)
  
      for i in range(total_batch):
          batch_xs, batch_ys = mnist.train.next_batch(batch_size)
          feed_dict = {X: batch_xs, Y: batch_ys}
          c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
          avg_cost += c / total_batch
  
      print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
  
  print('Learning Finished!')
  
  # Test model and check accuracy
  correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  print('Accuracy:', sess.run(accuracy, feed_dict={
        X: mnist.test.images, Y: mnist.test.labels}))
  
  # Get one and predict
  r = random.randint(0, mnist.test.num_examples - 1)
  print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
  print("Prediction: ", sess.run(
      tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
  
  # plt.imshow(mnist.test.images[r:r + 1].
  #           reshape(28, 28), cmap='Greys', interpolation='nearest')
  # plt.show()
  
  '''
  Epoch: 0001 cost = 141.207671860
  Epoch: 0002 cost = 38.788445864
  Epoch: 0003 cost = 23.977515479
  Epoch: 0004 cost = 16.315132428
  Epoch: 0005 cost = 11.702554882
  Epoch: 0006 cost = 8.573139748
  Epoch: 0007 cost = 6.370995680
  Epoch: 0008 cost = 4.537178684
  Epoch: 0009 cost = 3.216900532
  Epoch: 0010 cost = 2.329708954
  Epoch: 0011 cost = 1.715552875
  Epoch: 0012 cost = 1.189857912
  Epoch: 0013 cost = 0.820965160
  Epoch: 0014 cost = 0.624131458
  Epoch: 0015 cost = 0.454633765
  Learning Finished!
  Accuracy: 0.9455
  '''
  
apis: tensorflow.placeholder tensorflow.nn.softmax_cross_entropy_with_logits tensorflow.global_variables_initializer tensorflow.train.AdamOptimizer tensorflow.examples.tutorials.mnist.input_data.read_data_sets tensorflow.matmul tensorflow.cast tensorflow.set_random_seed tensorflow.Session tensorflow.argmax tensorflow.random_normal