(embedded_chars_expanded, filter_sizes, embedding_size,
              num_filters, sequence_length)
return h_pool_flat
text conv and max pooling to get one-dimension vector to representation of text
    :param filter_sizes:
    :return:
text conv and max pooling to get one-dimension vector to representation of text
    :param filter_sizes:
    :return:
text conv and max pooling to get one - dimension vector to representation of text : param filter_sizes : : return :
def conv_pool(embedded_chars_expanded, filter_sizes, embedding_size,
              num_filters, sequence_length):
  """
    text conv and max pooling to get one-dimension vector to representation of text
    :param filter_sizes:
    :return:
    """
  pooled_outputs = []
  for _, filter_size in enumerate(filter_sizes):
    with tf.variable_scope("conv-maxpool-%s" % filter_size):
      # Convolution Layer
      filter_shape = [filter_size, embedding_size, 1, num_filters]
      W = tf.get_variable(
          name='W', initializer=tf.truncated_normal(filter_shape, stddev=0.1))
      b = tf.get_variable(
          name='b', initializer=tf.constant(0.1, shape=[num_filters]))
      conv = tf.nn.conv2d(
          embedded_chars_expanded,
          W,
          strides=[1, 1, 1, 1],
          padding="VALID",
          name="conv")
      # Apply nonlinearity
      h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
      # Maxpooling over the outputs
      pooled = tf.nn.max_pool(
          h,
          ksize=[1, sequence_length - filter_size + 1, 1, 1],
          strides=[1, 1, 1, 1],
          padding='VALID',
          name="pool")
      pooled_outputs.append(pooled)
  # Combine all the pooled features
  num_filters_total = num_filters * len(filter_sizes)

  h_pool = tf.concat(pooled_outputs, 3)

  h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])
  return h_pool_flat
def conv_pool ( embedded_chars_expanded , filter_sizes , embedding_size , num_filters , sequence_length ) : pooled_outputs = [ ] for _ , filter_size in enumerate ( filter_sizes ) : with tf . variable_scope ( "conv-maxpool-%s" % filter_size ) : # Convolution Layer filter_shape = [ filter_size , embedding_size , 1 , num_filters ] W = tf . get_variable ( name = 'W' , initializer = tf . truncated_normal ( filter_shape , stddev = 0.1 ) ) b = tf . get_variable ( name = 'b' , initializer = tf . constant ( 0.1 , shape = [ num_filters ] ) ) conv = tf . nn . conv2d ( embedded_chars_expanded , W , strides = [ 1 , 1 , 1 , 1 ] , padding = "VALID" , name = "conv" ) # Apply nonlinearity h = tf . nn . relu ( tf . nn . bias_add ( conv , b ) , name = "relu" ) # Maxpooling over the outputs pooled = tf . nn . max_pool ( h , ksize = [ 1 , sequence_length - filter_size + 1 , 1 , 1 ] , strides = [ 1 , 1 , 1 , 1 ] , padding = 'VALID' , name = "pool" ) pooled_outputs . append ( pooled ) # Combine all the pooled features num_filters_total = num_filters * len ( filter_sizes ) h_pool = tf . concat ( pooled_outputs , 3 ) h_pool_flat = tf . reshape ( h_pool , [ - 1 , num_filters_total ] ) return h_pool_flat