AdaptiveIAAModel.py 文件源码

def do_inference_steps(self, initial_state, premise, hypothesis):


        self.one_minus_eps = tf.constant(1.0 - self.config.eps, tf.float32,[self.batch_size])
        self.N = tf.constant(self.config.max_computation, tf.float32,[self.batch_size])


        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_states = tf.zeros_like(initial_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,premise, hypothesis ,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,_,_,_,state = \
            tf.while_loop(pred,self.inference_step,
            [batch_mask,prob_compare,prob,
             counter,initial_state,premise, hypothesis, acc_states])

        return state, remainders, iterations