model.py 文件源码

def __init__(self, config):
        self.config = config
        self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
                                           initializer=tf.constant_initializer(0), trainable=False)

        # Define forward inputs here
        N, M, JX, JQ, VW, VC, W, H = \
            config.batch_size, config.max_num_sents, config.max_sent_size, \
            config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size, config.max_tree_height
        self.x = tf.placeholder('int32', [None, M, JX], name='x')
        self.cx = tf.placeholder('int32', [None, M, JX, W], name='cx')
        self.q = tf.placeholder('int32', [None, JQ], name='q')
        self.cq = tf.placeholder('int32', [None, JQ, W], name='cq')
        self.tx = tf.placeholder('int32', [None, M, H, JX], name='tx')
        self.tx_edge_mask = tf.placeholder('bool', [None, M, H, JX, JX], name='tx_edge_mask')
        self.y = tf.placeholder('bool', [None, M, H, JX], name='y')
        self.is_train = tf.placeholder('bool', [], name='is_train')

        # Define misc

        # Forward outputs / loss inputs
        self.logits = None
        self.yp = None
        self.var_list = None

        # Loss outputs
        self.loss = None

        self._build_forward()
        self._build_loss()

        self.ema_op = self._get_ema_op()
        self.summary = tf.merge_all_summaries()