model.py 文件源码

def __init__(self, config, scope):
        self.scope = scope
        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 = \
            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
        self.x = tf.placeholder('int32', [N, M, None], name='x')
        self.cx = tf.placeholder('int32', [N, M, None, W], name='cx')
        self.x_mask = tf.placeholder('bool', [N, M, None], name='x_mask')
        self.q = tf.placeholder('int32', [N, JQ], name='q')
        self.cq = tf.placeholder('int32', [N, JQ, W], name='cq')
        self.q_mask = tf.placeholder('bool', [N, JQ], name='q_mask')
        self.y = tf.placeholder('bool', [N, M, JX], name='y')
        self.is_train = tf.placeholder('bool', [], name='is_train')
        self.new_emb_mat = tf.placeholder('float', [None, config.word_emb_size], name='new_emb_mat')

        # Define misc
        self.tensor_dict = {}

        # 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()
        if config.mode == 'train':
            self._build_ema()

        self.summary = tf.merge_all_summaries()
        self.summary = tf.merge_summary(tf.get_collection("summaries", scope=self.scope))