lemmatizer-skeleton.py 文件源码

def __init__(self, rnn_cell, rnn_cell_dim, num_chars, bow_char, eow_char, logdir, expname, threads=1, seed=42):
        # Create an empty graph and a session
        graph = tf.Graph()
        graph.seed = seed
        self.session = tf.Session(graph = graph, config=tf.ConfigProto(inter_op_parallelism_threads=threads,
                                                                       intra_op_parallelism_threads=threads))

        timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
        self.summary_writer = tf.train.SummaryWriter("{}/{}-{}".format(logdir, timestamp, expname), flush_secs=10)

        # Construct the graph
        with self.session.graph.as_default():
            if rnn_cell == "LSTM":
                rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_cell_dim)
            elif rnn_cell == "GRU":
                rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_cell_dim)
            else:
                raise ValueError("Unknown rnn_cell {}".format(rnn_cell))

            self.global_step = tf.Variable(0, dtype=tf.int64, trainable=False, name="global_step")
            self.sentence_lens = tf.placeholder(tf.int32, [None])
            self.form_ids = tf.placeholder(tf.int32, [None, None])
            self.forms = tf.placeholder(tf.int32, [None, None])
            self.form_lens = tf.placeholder(tf.int32, [None])
            self.lemma_ids = tf.placeholder(tf.int32, [None, None])
            self.lemmas = tf.placeholder(tf.int32, [None, None])
            self.lemma_lens = tf.placeholder(tf.int32, [None])

            # TODO
            # loss = ...
            # self.training = ...
            # self.predictions = ...
            # self.accuracy = ...

            self.dataset_name = tf.placeholder(tf.string, [])
            self.summary = tf.merge_summary([tf.scalar_summary(self.dataset_name+"/loss", loss),
                                             tf.scalar_summary(self.dataset_name+"/accuracy", self.accuracy)])

            # Initialize variables
            self.session.run(tf.initialize_all_variables())