ACTAttnAnalysisModel.py 文件源码

def do_act_steps(self, premise, hypothesis):

        self.rep_size = premise.get_shape()[-1].value

        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")
        initial_state = tf.zeros([self.batch_size, 2*self.rep_size], tf.float32, name="state")
        i = tf.constant(0, tf.int32, name="index")
        acc_states = tf.zeros_like(initial_state, tf.float32, name="state_accumulator")
        batch_mask = tf.constant(True, tf.bool,[self.batch_size])

        # Tensor arrays to collect information about the run:
        array_probs = tf.TensorArray(tf.float32,0, dynamic_size=True)
        premise_attention = tf.TensorArray(tf.float32,0, dynamic_size=True)
        hypothesis_attention = tf.TensorArray(tf.float32,0, dynamic_size=True)


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

        pred = lambda i ,array_probs, premise_attention, hypothesis_attention, 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.
        i,array_probs,premise_attention,hypothesis_attention,_,_,remainders,iterations,_,_,_,state = \
            tf.while_loop(pred,self.inference_step,
            [i,array_probs, premise_attention, hypothesis_attention,
             batch_mask,prob_compare,prob,
             counter,initial_state,premise, hypothesis, acc_states])

        self.ACTPROB = array_probs.pack()
        self.ACTPREMISEATTN = premise_attention.pack()
        self.ACTHYPOTHESISATTN = hypothesis_attention.pack()

        return state, remainders, iterations