model.py 文件源码

def __init__(self, args, infer=False):
        self.args = args
        if infer:
            args.batch_size = 1
            args.seq_length = 1

        if args.model == 'rnn':
            cell_fn = rnn_cell.BasicRNNCell
        elif args.model == 'gru':
            cell_fn = rnn_cell.GRUCell
        elif args.model == 'lstm':
            cell_fn = rnn_cell.BasicLSTMCell
        else:
            raise Exception("model type not supported: {}".format(args.model))

        cell = cell_fn(args.rnn_size)

        self.cell = cell = rnn_cell.MultiRNNCell([cell] * args.num_layers)

        self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
        self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
        self.initial_state = cell.zero_state(args.batch_size, tf.float32)

        with tf.variable_scope('rnnlm'):
            softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size])
            softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
            with tf.device("/cpu:0"):
                embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
                inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data))
                inputs = [tf.squeeze(input_, [1]) for input_ in inputs]

        def loop(prev, _):
            prev = tf.matmul(prev, softmax_w) + softmax_b
            prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
            return tf.nn.embedding_lookup(embedding, prev_symbol)

        outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope='rnnlm')
        output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size])
        self.logits = tf.matmul(output, softmax_w) + softmax_b
        self.probs = tf.nn.softmax(self.logits)
        loss = seq2seq.sequence_loss_by_example([self.logits],
                [tf.reshape(self.targets, [-1])],
                [tf.ones([args.batch_size * args.seq_length])],
                args.vocab_size)
        self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
        self.final_state = last_state
        self.lr = tf.Variable(0.0, trainable=False)
        tvars = tf.trainable_variables()
        grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
                args.grad_clip)
        optimizer = tf.train.AdamOptimizer(self.lr)
        self.train_op = optimizer.apply_gradients(zip(grads, tvars))