text: import tensorflow as tf
  from tensorflow.contrib.layers.python.layers import batch_norm, variance_scaling_initializer
  
  #the implements of leakyRelu
  def lrelu(x , alpha = 0.2 , name="LeakyReLU"):
      return tf.maximum(x , alpha*x)
  
  def conv2d(input_, output_dim,
             k_h=3, k_w=3, d_h=2, d_w=2,
             name="conv2d"):
      with tf.variable_scope(name):
  
          w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
                              initializer= variance_scaling_initializer())
          conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
  
          biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
          conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
  
          return conv, w
  
  def de_conv(input_, output_shape,
               k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02, name="deconv2d", 
               with_w=False, initializer = variance_scaling_initializer()):
  
      with tf.variable_scope(name):
          # filter : [height, width, output_channels, in_channels]
          w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
                              initializer = initializer)
          try:
              deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
                                              strides=[1, d_h, d_w, 1])
          # Support for verisons of TensorFlow before 0.7.0
          except AttributeError:
              deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
                                      strides=[1, d_h, d_w, 1])
  
          biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
          deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
  
          if with_w:
              return deconv, w, biases
          else:
              return deconv
  
  def fully_connect(input_, output_size, scope=None, with_w=False, 
                    initializer = variance_scaling_initializer()):
  
    shape = input_.get_shape().as_list()
  
    with tf.variable_scope(scope or "Linear"):
  
      matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                   initializer = initializer)
      bias = tf.get_variable("bias", [output_size], initializer=tf.constant_initializer(0.0))
      if with_w:
        return tf.matmul(input_, matrix) + bias, matrix, bias
      else:
        return tf.matmul(input_, matrix) + bias
  
  def conv_cond_concat(x, y):
      """Concatenate conditioning vector on feature map axis."""
      x_shapes = x.get_shape()
      y_shapes = y.get_shape()
  
      return tf.concat([x , y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2] , y_shapes[3]])], 3)
  
  def batch_normal(input , scope="scope" , reuse=False):
      return batch_norm(input , epsilon=1e-5, decay=0.9 , scale=True, scope=scope , reuse = reuse , updates_collections=None)
  
  
  
  
  
  
  
  
  