text: # Lab 11 MNIST and Deep learning CNN
  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
  
  # hyper parameters
  learning_rate = 0.001
  training_epochs = 15
  batch_size = 100
  
  # dropout (keep_prob) rate  0.7~0.5 on training, but should be 1 for testing
  keep_prob = tf.placeholder(tf.float32)
  
  # input place holders
  X = tf.placeholder(tf.float32, [None, 784])
  X_img = tf.reshape(X, [-1, 28, 28, 1])   # img 28x28x1 (black/white)
  Y = tf.placeholder(tf.float32, [None, 10])
  
  # L1 ImgIn shape=(?, 28, 28, 1)
  W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))
  #    Conv     -> (?, 28, 28, 32)
  #    Pool     -> (?, 14, 14, 32)
  L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME')
  L1 = tf.nn.relu(L1)
  L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],
                      strides=[1, 2, 2, 1], padding='SAME')
  L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
  '''
  Tensor("Conv2D:0", shape=(?, 28, 28, 32), dtype=float32)
  Tensor("Relu:0", shape=(?, 28, 28, 32), dtype=float32)
  Tensor("MaxPool:0", shape=(?, 14, 14, 32), dtype=float32)
  Tensor("dropout/mul:0", shape=(?, 14, 14, 32), dtype=float32)
  '''
  
  # L2 ImgIn shape=(?, 14, 14, 32)
  W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))
  #    Conv      ->(?, 14, 14, 64)
  #    Pool      ->(?, 7, 7, 64)
  L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
  L2 = tf.nn.relu(L2)
  L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],
                      strides=[1, 2, 2, 1], padding='SAME')
  L2 = tf.nn.dropout(L2, keep_prob=keep_prob)
  '''
  Tensor("Conv2D_1:0", shape=(?, 14, 14, 64), dtype=float32)
  Tensor("Relu_1:0", shape=(?, 14, 14, 64), dtype=float32)
  Tensor("MaxPool_1:0", shape=(?, 7, 7, 64), dtype=float32)
  Tensor("dropout_1/mul:0", shape=(?, 7, 7, 64), dtype=float32)
  '''
  
  # L3 ImgIn shape=(?, 7, 7, 64)
  W3 = tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.01))
  #    Conv      ->(?, 7, 7, 128)
  #    Pool      ->(?, 4, 4, 128)
  #    Reshape   ->(?, 4 * 4 * 128) # Flatten them for FC
  L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
  L3 = tf.nn.relu(L3)
  L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1], strides=[
                      1, 2, 2, 1], padding='SAME')
  L3 = tf.nn.dropout(L3, keep_prob=keep_prob)
  L3_flat = tf.reshape(L3, [-1, 128 * 4 * 4])
  '''
  Tensor("Conv2D_2:0", shape=(?, 7, 7, 128), dtype=float32)
  Tensor("Relu_2:0", shape=(?, 7, 7, 128), dtype=float32)
  Tensor("MaxPool_2:0", shape=(?, 4, 4, 128), dtype=float32)
  Tensor("dropout_2/mul:0", shape=(?, 4, 4, 128), dtype=float32)
  Tensor("Reshape_1:0", shape=(?, 2048), dtype=float32)
  '''
  
  # L4 FC 4x4x128 inputs -> 625 outputs
  W4 = tf.get_variable("W4", shape=[128 * 4 * 4, 625],
                       initializer=tf.contrib.layers.xavier_initializer())
  b4 = tf.Variable(tf.random_normal([625]))
  L4 = tf.nn.relu(tf.matmul(L3_flat, W4) + b4)
  L4 = tf.nn.dropout(L4, keep_prob=keep_prob)
  '''
  Tensor("Relu_3:0", shape=(?, 625), dtype=float32)
  Tensor("dropout_3/mul:0", shape=(?, 625), dtype=float32)
  '''
  
  # L5 Final FC 625 inputs -> 10 outputs
  W5 = tf.get_variable("W5", shape=[625, 10],
                       initializer=tf.contrib.layers.xavier_initializer())
  b5 = tf.Variable(tf.random_normal([10]))
  logits = tf.matmul(L4, W5) + b5
  '''
  Tensor("add_1:0", shape=(?, 10), dtype=float32)
  '''
  
  # define cost/loss & optimizer
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
      logits=logits, 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
  print('Learning started. It takes sometime.')
  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, keep_prob: 0.7}
          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
  
  # if you have a OOM error, please refer to lab-11-X-mnist_deep_cnn_low_memory.py
  
  correct_prediction = tf.equal(tf.argmax(logits, 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, keep_prob: 1}))
  
  # 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(logits, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1}))
  
  # plt.imshow(mnist.test.images[r:r + 1].
  #           reshape(28, 28), cmap='Greys', interpolation='nearest')
  # plt.show()
  
  '''
  Learning stared. It takes sometime.
  Epoch: 0001 cost = 0.385748474
  Epoch: 0002 cost = 0.092017397
  Epoch: 0003 cost = 0.065854684
  Epoch: 0004 cost = 0.055604566
  Epoch: 0005 cost = 0.045996377
  Epoch: 0006 cost = 0.040913645
  Epoch: 0007 cost = 0.036924479
  Epoch: 0008 cost = 0.032808939
  Epoch: 0009 cost = 0.031791007
  Epoch: 0010 cost = 0.030224456
  Epoch: 0011 cost = 0.026849916
  Epoch: 0012 cost = 0.026826763
  Epoch: 0013 cost = 0.027188021
  Epoch: 0014 cost = 0.023604777
  Epoch: 0015 cost = 0.024607201
  Learning Finished!
  Accuracy: 0.9938
  '''