code: # -*- encoding:utf8 -*-
  
  """A deep MNIST classifier using convolutional layers.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import logging
  import math
  import tempfile
  import tensorflow as tf
  
  from tensorflow.examples.tutorials.mnist import input_data
  
  logger = logging.getLogger('mnist')
  
  FLAGS = None
  
  class MnistNetwork(object):
      def __init__(self,
                   channel_1_num = 32,
                   channel_2_num = 64,
                   conv_size = 5,
                   hidden_size = 1024,
                   pool_size = 2,
                   learning_rate = 0.0001,
                   x_dim = 784,
                   y_dim = 10):
          self.channel_1_num = channel_1_num
          self.channel_2_num = channel_2_num
          '''@nni.variable(nni.choice(2,3,5,7),name=self.conv_size)'''
          self.conv_size = conv_size
          '''@nni.variable(nni.choice(124,512,1024),name=self.hidden_size)'''
          self.hidden_size = hidden_size
          self.pool_size = pool_size
          '''@nni.variable(nni.randint(2,3,5),name=self.learning_rate)'''
          self.learning_rate = learning_rate
          self.x_dim = x_dim
          self.y_dim = y_dim
  
      def build_network(self):
          self.x = tf.placeholder(tf.float32, [None, self.x_dim], name = 'input_x')
          self.y = tf.placeholder(tf.float32, [None, self.y_dim], name = 'input_y')
          self.keep_prob = tf.placeholder(tf.float32, name = 'keep_prob')
  
          # Reshape to use within a convolutional neural net.
          # Last dimension is for "features" - there is only one here, since images are
          # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
          with tf.name_scope('reshape'):
              try:
                  input_dim = int(math.sqrt(self.x_dim))
              except:
                  #print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
                  logger.debug('input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim))
                  raise
              x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1])
  
          # First convolutional layer - maps one grayscale image to 32 feature maps.
          with tf.name_scope('conv1'):
              W_conv1 = weight_variable([self.conv_size, self.conv_size, 1, self.channel_1_num])
              b_conv1 = bias_variable([self.channel_1_num])
              """@nni.function_choice(tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1),tf.nn.sigmoid(conv2d(x_image, W_conv1) + b_conv1),tf.nn.tanh(conv2d(x_image, W_conv1) + b_conv1),name=tf.nn.relu)"""
              h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
          # Pooling layer - downsamples by 2X.
          with tf.name_scope('pool1'):
              """@nni.function_choice(max_pool(h_conv1, self.pool_size),avg_pool(h_conv1, self.pool_size),name=max_pool)"""
              h_pool1 = max_pool(h_conv1, self.pool_size)
  
          # Second convolutional layer -- maps 32 feature maps to 64.
          with tf.name_scope('conv2'):
              W_conv2 = weight_variable([self.conv_size, self.conv_size, self.channel_1_num, self.channel_2_num])
              b_conv2 = bias_variable([self.channel_2_num])
              h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
          # Second pooling layer.
          with tf.name_scope('pool2'):
              #"""@nni.dynamic(input={cnn_block:1, concat:2},function_choice={"cnn_block":(x,nni.choice([3,4])),"cnn_block":(x),"concat":(x,y)},limit={"cnn_block.input":[concat,input],"concat.input":[this.depth-1,this.depth-3,this.depth-5],"graph.width":[1]})"""
              h_pool2 = max_pool(h_conv2, self.pool_size)
  
          # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
          # is down to 7x7x64 feature maps -- maps this to 1024 features.
          last_dim = int(input_dim / (self.pool_size * self.pool_size))
          with tf.name_scope('fc1'):
              W_fc1 = weight_variable([last_dim * last_dim * self.channel_2_num, self.hidden_size])
              b_fc1 = bias_variable([self.hidden_size])
  
          h_pool2_flat = tf.reshape(h_pool2, [-1, last_dim * last_dim * self.channel_2_num])
          h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
          # Dropout - controls the complexity of the model, prevents co-adaptation of features.
          with tf.name_scope('dropout'):
              h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob)
  
          # Map the 1024 features to 10 classes, one for each digit
          with tf.name_scope('fc2'):
              W_fc2 = weight_variable([self.hidden_size, self.y_dim])
              b_fc2 = bias_variable([self.y_dim])
              y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  
          with tf.name_scope('loss'):
              cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.y, logits = y_conv))
          with tf.name_scope('adam_optimizer'):
              self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(cross_entropy)
  
          with tf.name_scope('accuracy'):
              correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(self.y, 1))
              self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
          return
  
  def conv2d(x, W):
      """conv2d returns a 2d convolution layer with full stride."""
      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool(x, pool_size):
      """max_pool downsamples a feature map by 2X."""
      return tf.nn.max_pool(x, ksize=[1, pool_size, pool_size, 1],
                          strides=[1, pool_size, pool_size, 1], padding='SAME')
  def avg_pool(x,pool_size):
      return tf.nn.avg_pool(x, ksize=[1, pool_size, pool_size, 1],
                          strides=[1, pool_size, pool_size, 1], padding='SAME')
  
  def weight_variable(shape):
      """weight_variable generates a weight variable of a given shape."""
      initial = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(initial)
  
  def bias_variable(shape):
      """bias_variable generates a bias variable of a given shape."""
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)
  
  def main():
      # Import data
      data_dir= '/tmp/tensorflow/mnist/input_data'
      mnist = input_data.read_data_sets(data_dir, one_hot=True)
      logger.debug('Mnist download data done.')
  
      # Create the model
      # Build the graph for the deep net
      mnist_network = MnistNetwork()
      mnist_network.build_network()
      logger.debug('Mnist build network done.')
  
      # Write log
      graph_location = tempfile.mkdtemp()
      logger.debug('Saving graph to: %s', graph_location)
      # print('Saving graph to: %s' % graph_location)
      train_writer = tf.summary.FileWriter(graph_location)
      train_writer.add_graph(tf.get_default_graph())
  
      test_acc = 0.0
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          batch_num=200
          for i in range(batch_num):
              '''@nni.variable(nni.choice(50,250,500),name=batch_size)'''
              batch_size=50
              batch = mnist.train.next_batch(batch_size)
              '''@nni.variable(nni.choice(1,5),name=dropout_rate)'''
              dropout_rate=0.5
              mnist_network.train_step.run(feed_dict={mnist_network.x: batch[0], mnist_network.y: batch[1], mnist_network.keep_prob: dropout_rate})
  
              if i % 100 == 0:
                  #train_accuracy = mnist_network.accuracy.eval(feed_dict={
                  #    mnist_network.x: batch[0], mnist_network.y: batch[1], mnist_network.keep_prob: params['dropout_rate']})
                  #print('step %d, training accuracy %g' % (i, train_accuracy))
  
                  test_acc = mnist_network.accuracy.eval(feed_dict={
                      mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0})
                  '''@nni.report_intermediate_result(test_acc)'''
  
          test_acc = mnist_network.accuracy.eval(feed_dict={
              mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0})
          '''@nni.report_final_result(test_acc)'''
  
  
  def generate_default_params():
      params = {'data_dir': '/tmp/tensorflow/mnist/input_data',
                'dropout_rate': 0.5,
                'channel_1_num': 32,
                'channel_2_num': 64,
                'conv_size': 5,
                'pool_size': 2,
                'hidden_size': 1024,
                'batch_size': 50,
                'batch_num': 200,
                'learning_rate': 1e-4}
      return params
  
  if __name__ == '__main__':
      # run command: python mnist.py --init_file_path ./init.json
  
      #FLAGS, unparsed = parse_command()
      #original_params = parse_init_json(FLAGS.init_file_path, {})
  
      #pipe_interface.set_params_to_env()
      '''@nni.get_next_parameter()'''
      try:
          params = generate_default_params()
          logger.debug('params')
          logger.debug('params update')
          main()
      except:
          logger.exception('Got some exception in while loop in mnist.py')
          raise
  
apis: tensorflow.nn.softmax_cross_entropy_with_logits tensorflow.reshape tensorflow.nn.avg_pool tensorflow.matmul tensorflow.name_scope tensorflow.Variable tensorflow.summary.FileWriter tensorflow.nn.dropout tensorflow.nn.max_pool tensorflow.global_variables_initializer tensorflow.constant tensorflow.cast tensorflow.Session tensorflow.placeholder tensorflow.truncated_normal tensorflow.train.AdamOptimizer tensorflow.nn.conv2d tensorflow.examples.tutorials.mnist.input_data.read_data_sets tensorflow.get_default_graph tensorflow.argmax