text: 
  # coding: utf-8
  
  # In[1]:
  
  
  import tensorflow as tf
  
  
  # #### 1. 定义神经网络结构相关的参数。
  
  # In[2]:
  
  
  INPUT_NODE = 784
  OUTPUT_NODE = 10
  LAYER1_NODE = 500
  
  
  # #### 2. 通过tf.get_variable函数来获取变量。
  
  # In[3]:
  
  
  def get_weight_variable(shape, regularizer):
      weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
      if regularizer != None: tf.add_to_collection('losses', regularizer(weights))
      return weights
  
  
  # #### 3. 定义神经网络的前向传播过程。
  
  # In[4]:
  
  
  def inference(input_tensor, regularizer):
      with tf.variable_scope('layer1'):
  
          weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
          biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
          layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)
  
  
      with tf.variable_scope('layer2'):
          weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
          biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
          layer2 = tf.matmul(layer1, weights) + biases
  
      return layer2
  
  