(self)
return tf.concat(logits_list, 1)
Creates character sequence logits for a net specified in the constructor.

    A "main" method for the sequence layer which glues together all pieces.

    Returns:
      A tensor with shape [batch_size, seq_length, num_char_classes].
Creates character sequence logits for a net specified in the constructor.
Creates character sequence logits for a net specified in the constructor .
def create_logits(self):
    """Creates character sequence logits for a net specified in the constructor.

    A "main" method for the sequence layer which glues together all pieces.

    Returns:
      A tensor with shape [batch_size, seq_length, num_char_classes].
    """
    with tf.variable_scope('LSTM'):
      first_label = self.get_input(prev=None, i=0)
      decoder_inputs = [first_label] + [None] * (self._params.seq_length - 1)
      lstm_cell = tf.contrib.rnn.LSTMCell(
          self._mparams.num_lstm_units,
          use_peepholes=False,
          cell_clip=self._mparams.lstm_state_clip_value,
          state_is_tuple=True,
          initializer=orthogonal_initializer)
      lstm_outputs, _ = self.unroll_cell(
          decoder_inputs=decoder_inputs,
          initial_state=lstm_cell.zero_state(self._batch_size, tf.float32),
          loop_function=self.get_input,
          cell=lstm_cell)

    with tf.variable_scope('logits'):
      logits_list = [
          tf.expand_dims(self.char_logit(logit, i), dim=1)
          for i, logit in enumerate(lstm_outputs)
      ]

    return tf.concat(logits_list, 1)
def create_logits ( self ) : with tf . variable_scope ( 'LSTM' ) : first_label = self . get_input ( prev = None , i = 0 ) decoder_inputs = [ first_label ] + [ None ] * ( self . _params . seq_length - 1 ) lstm_cell = tf . contrib . rnn . LSTMCell ( self . _mparams . num_lstm_units , use_peepholes = False , cell_clip = self . _mparams . lstm_state_clip_value , state_is_tuple = True , initializer = orthogonal_initializer ) lstm_outputs , _ = self . unroll_cell ( decoder_inputs = decoder_inputs , initial_state = lstm_cell . zero_state ( self . _batch_size , tf . float32 ) , loop_function = self . get_input , cell = lstm_cell ) with tf . variable_scope ( 'logits' ) : logits_list = [ tf . expand_dims ( self . char_logit ( logit , i ) , dim = 1 ) for i , logit in enumerate ( lstm_outputs ) ] return tf . concat ( logits_list , 1 )