a8_dynamic_memory_network.py 文件源码

def instantiate_weights(self):
        """define all weights here"""
        with tf.variable_scope("gru_cell"):
            self.W_z = tf.get_variable("W_z", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.U_z = tf.get_variable("U_z", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.b_z = tf.get_variable("b_z", shape=[self.hidden_size])
            # GRU parameters:reset gate related
            self.W_r = tf.get_variable("W_r", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.U_r = tf.get_variable("U_r", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.b_r = tf.get_variable("b_r", shape=[self.hidden_size])

            self.W_h = tf.get_variable("W_h", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.U_h = tf.get_variable("U_h", shape=[self.embed_size, self.hidden_size], initializer=self.initializer)
            self.b_h = tf.get_variable("b_h", shape=[self.hidden_size])

        with tf.variable_scope("embedding_projection"):  # embedding matrix
            self.Embedding = tf.get_variable("Embedding", shape=[self.vocab_size, self.embed_size],initializer=self.initializer)

# test: learn to count. weight of query and story is different
#two step to test
#step1. run train function to train the model. it will save checkpoint
#step2. run predict function to make a prediction based on the model restore from the checkpoint.