model.py 文件源码

def _assemble_graph(self):
        self._create_placeholders()
        tf.set_random_seed(self._random_seed + 1)

        A_var = tf.Variable(
            initial_value=tf.random_uniform(
                shape=[self._emb_dim, self._vocab_dim],
                minval=-1, maxval=1, seed=(self._random_seed + 2)
            )
        )
        B_var = tf.Variable(
            initial_value=tf.random_uniform(
                shape=[self._emb_dim, self._vocab_dim],
                minval=-1, maxval=1, seed=(self._random_seed + 3)
            )
        )
        self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')

        cont_mult = tf.transpose(tf.matmul(A_var, tf.transpose(self.context_batch)))
        resp_mult = tf.matmul(B_var, tf.transpose(self.response_batch))
        neg_resp_mult = tf.matmul(B_var, tf.transpose(self.neg_response_batch))

        pos_raw_f = tf.diag_part(tf.matmul(cont_mult, resp_mult))
        neg_raw_f = tf.diag_part(tf.matmul(cont_mult, neg_resp_mult))
        self.f_pos = pos_raw_f
        self.f_neg = neg_raw_f

        self.loss = tf.reduce_sum(tf.nn.relu(self.f_neg - self.f_pos + self._margin))