parameters: (self, observ, length=None, state=None, reuse=True)
docstring: Compute the network output for a batched sequence of observations.
  
      Optionally, the initial state can be specified. The weights should be
      reused for all calls, except for the first one. Output is a named tuple
      containing the policy as a TensorFlow distribution, the policy mean and log
      standard deviation, the approximated state value, and the new recurrent
      state.
  
      Args:
        observ: Sequences of observations.
        length: Batch of sequence lengths.
        state: Batch of initial recurrent states.
        reuse: Python boolean whether to reuse previous variables.
  
      Returns:
        NetworkOutput tuple.
docstring_summary: Compute the network output for a batched sequence of observations.
docstring_tokens: Compute the network output for a batched sequence of observations .
function: def _network(self, observ, length=None, state=None, reuse=True):
      """Compute the network output for a batched sequence of observations.
  
      Optionally, the initial state can be specified. The weights should be
      reused for all calls, except for the first one. Output is a named tuple
      containing the policy as a TensorFlow distribution, the policy mean and log
      standard deviation, the approximated state value, and the new recurrent
      state.
  
      Args:
        observ: Sequences of observations.
        length: Batch of sequence lengths.
        state: Batch of initial recurrent states.
        reuse: Python boolean whether to reuse previous variables.
  
      Returns:
        NetworkOutput tuple.
      """
      with tf.variable_scope('network', reuse=reuse):
        observ = tf.convert_to_tensor(observ)
        use_gpu = self._config.use_gpu and utility.available_gpus()
        with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
          observ = tf.check_numerics(observ, 'observ')
          cell = self._config.network(self._batch_env.action.shape[1].value)
          (mean, logstd, value), state = tf.nn.dynamic_rnn(cell,
                                                           observ,
                                                           length,
                                                           state,
                                                           tf.float32,
                                                           swap_memory=True)
        mean = tf.check_numerics(mean, 'mean')
        logstd = tf.check_numerics(logstd, 'logstd')
        value = tf.check_numerics(value, 'value')
        policy = tf.contrib.distributions.MultivariateNormalDiag(mean, tf.exp(logstd))
        return _NetworkOutput(policy, mean, logstd, value, state)
function_tokens: def _network ( self , observ , length = None , state = None , reuse = True ) : with tf . variable_scope ( 'network' , reuse = reuse ) : observ = tf . convert_to_tensor ( observ ) use_gpu = self . _config . use_gpu and utility . available_gpus ( ) with tf . device ( '/gpu:0' if use_gpu else '/cpu:0' ) : observ = tf . check_numerics ( observ , 'observ' ) cell = self . _config . network ( self . _batch_env . action . shape [ 1 ] . value ) ( mean , logstd , value ) , state = tf . nn . dynamic_rnn ( cell , observ , length , state , tf . float32 , swap_memory = True ) mean = tf . check_numerics ( mean , 'mean' ) logstd = tf . check_numerics ( logstd , 'logstd' ) value = tf . check_numerics ( value , 'value' ) policy = tf . contrib . distributions . MultivariateNormalDiag ( mean , tf . exp ( logstd ) ) return _NetworkOutput ( policy , mean , logstd , value , state )