new_contents: import tensorflow as tf
  import numpy as np
  from tensorflow.examples.tutorials.mnist import input_data
  
  batch_size = 128
  test_size = 256
  
  def init_weights(shape):
      return tf.Variable(tf.random_normal(shape, stddev=0.01))
  
  
  def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
      l1a = tf.nn.relu(tf.nn.conv2d(X, w,                       # l1a shape=(?, 28, 28, 32)
                          strides=[1, 1, 1, 1], padding='SAME'))
      l1 = tf.nn.max_pool(l1a, ksize=[1, 2, 2, 1],              # l1 shape=(?, 14, 14, 32)
                          strides=[1, 2, 2, 1], padding='SAME')
      l1 = tf.nn.dropout(l1, p_keep_conv)
  
      l2a = tf.nn.relu(tf.nn.conv2d(l1, w2,                     # l2a shape=(?, 14, 14, 64)
                          strides=[1, 1, 1, 1], padding='SAME'))
      l2 = tf.nn.max_pool(l2a, ksize=[1, 2, 2, 1],              # l2 shape=(?, 7, 7, 64)
                          strides=[1, 2, 2, 1], padding='SAME')
      l2 = tf.nn.dropout(l2, p_keep_conv)
  
      l3a = tf.nn.relu(tf.nn.conv2d(l2, w3,                     # l3a shape=(?, 7, 7, 128)
                          strides=[1, 1, 1, 1], padding='SAME'))
      l3 = tf.nn.max_pool(l3a, ksize=[1, 2, 2, 1],              # l3 shape=(?, 4, 4, 128)
                          strides=[1, 2, 2, 1], padding='SAME')
      l3 = tf.reshape(l3, [-1, w4.get_shape().as_list()[0]])    # reshape to (?, 2048)
      l3 = tf.nn.dropout(l3, p_keep_conv)
  
      l4 = tf.nn.relu(tf.matmul(l3, w4))
      l4 = tf.nn.dropout(l4, p_keep_hidden)
  
      pyx = tf.matmul(l4, w_o)
      return pyx
  
  mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
  trX = trX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
  teX = teX.reshape(-1, 28, 28, 1)  # 28x28x1 input img
  
  X = tf.placeholder("float", [None, 28, 28, 1])
  Y = tf.placeholder("float", [None, 10])
  
  w = init_weights([3, 3, 1, 32])       # 3x3x1 conv, 32 outputs
  w2 = init_weights([3, 3, 32, 64])     # 3x3x32 conv, 64 outputs
  w3 = init_weights([3, 3, 64, 128])    # 3x3x32 conv, 128 outputs
  w4 = init_weights([128 * 4 * 4, 625]) # FC 128 * 4 * 4 inputs, 625 outputs
  w_o = init_weights([625, 10])         # FC 625 inputs, 10 outputs (labels)
  
  p_keep_conv = tf.placeholder("float")
  p_keep_hidden = tf.placeholder("float")
  py_x = model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden)
  
  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))
  train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
  predict_op = tf.argmax(py_x, 1)
  
  # Launch the graph in a session
  with tf.Session() as sess:
      # you need to initialize all variables
      tf.global_variables_initializer().run()
  
      for i in range(100):
          training_batch = zip(range(0, len(trX), batch_size),
                               range(batch_size, len(trX)+1, batch_size))
          for start, end in training_batch:
              sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],
                                            p_keep_conv: 0.8, p_keep_hidden: 0.5})
  
          test_indices = np.arange(len(teX)) # Get A Test Batch
          np.random.shuffle(test_indices)
          test_indices = test_indices[0:test_size]
  
          print(i, np.mean(np.argmax(teY[test_indices], axis=1) ==
                           sess.run(predict_op, feed_dict={X: teX[test_indices],
                                                           p_keep_conv: 1.0,
                                                           p_keep_hidden: 1.0})))
  
subject: Create cnn.py