text: import os
  import model
  
  import tensorflow as tf
  
  import input_data
  
  data=input_data.read_data_sets('MNIST_data',one_hot=True)
  
  with tf.variable_scope("convolutional"):
      x=tf.placeholder(tf.float32,[None,784],name='x')
      keep_prob=tf.placeholder(tf.float32)
      y,variables=model.convolutional(x,keep_prob)
  
  #train
  
  y_=tf.placeholder(tf.float32,[None,10],name='y')
  cross_entropy=-tf.reduce_sum(y_*tf.log(y))
  train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
  accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
  
  saver=tf.train.Saver(variables)
  
  with tf.Session() as sess:
      merged_summary_op=tf.summary.merge_all()
      summary_writer=tf.summary.FileWriter('/tmp/mnist_log/1',sess.graph)
      summary_writer.add_graph(sess.graph)
      sess.run(tf.global_variables_initializer())
  
      for i in range(1000):
          batch=data.train.next_batch(50)
          if i%100==0:
              train_accuracy=accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
              # print("step %d,training accuracy $g"%(i,train_accuracy))
              print ("step %d, training accuracy %g" % (i, train_accuracy))
          sess.run(train_step,feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
      print(sess.run(accuracy, feed_dict={x: data.test.images, y_: data.test.labels,keep_prob:1.0}))
      path=saver.save(
          sess,os.path.join(os.path.dirname(__file__),'data','convalutional.ckpt'),
          write_meta_graph=False,write_state=False)
  
      print("Saved:",path)
  