AttentiveACTCell.py 文件源码

def __call__(self, inputs, state, timestep = 0, scope=None):

        with vs.variable_scope(scope or type(self).__name__):

            # define within cell constants/ counters used to control while loop for ACTStep
            prob = tf.constant(0.0,tf.float32,[self.batch_size], name="prob")
            prob_compare = tf.constant(0.0,tf.float32,[self.batch_size], name="prob_compare")
            counter = tf.constant(0.0, tf.float32,[self.batch_size], name="counter")
            acc_outputs = tf.zeros_like(state, tf.float32, name="output_accumulator")
            acc_states = tf.zeros_like(state, tf.float32, name="state_accumulator")
            batch_mask = tf.constant(True, tf.bool,[self.batch_size])

            # While loop stops when this predicate is FALSE.
            # Ie all (probability < 1-eps AND counter < N) are false.


            pred = lambda batch_mask,prob_compare,prob,\
                          counter,state,inputs,acc_output,acc_state:\
                tf.reduce_any(
                    tf.logical_and(
                        tf.less(prob_compare,self.one_minus_eps),
                        tf.less(counter,self.N)))
                # only stop if all of the batch have passed either threshold

            # Do while loop iterations until predicate above is false.
            _,_,remainders,iterations,_,_,output,next_state = \
                control_flow_ops.while_loop(pred,self.ACTStep,
                [batch_mask,prob_compare,prob,
                 counter,state,inputs, acc_outputs, acc_states])

        #accumulate remainder  and N values
        self.ACT_remainder.append(tf.reduce_mean(1 - remainders))
        self.ACT_iterations.append(tf.reduce_mean(iterations))

        return output, next_state