Add stacked dynamic lstm model for tf.

tensorflow/stacked_dynamic_lstm.py

@@ -0,0 +1,205 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import tensorflow as tf
+
+import paddle.v2 as paddle
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("LSTM model benchmark.")
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=32,
+        help='The sequence number of a batch data. (default: %(default)d)')
+    parser.add_argument(
+        '--stacked_num',
+        type=int,
+        default=5,
+        help='Number of lstm layers to stack. (default: %(default)d)')
+    parser.add_argument(
+        '--embedding_dim',
+        type=int,
+        default=512,
+        help='Dimension of embedding table. (default: %(default)d)')
+    parser.add_argument(
+        '--hidden_dim',
+        type=int,
+        default=512,
+        help='Hidden size of lstm unit. (default: %(default)d)')
+    parser.add_argument(
+        '--pass_num',
+        type=int,
+        default=10,
+        help='Epoch number to train. (default: %(default)d)')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=0.0002,
+        help='Learning rate used to train. (default: %(default)f)')
+    parser.add_argument(
+        '--infer_only', action='store_true', help='If set, run forward only.')
+    args = parser.parse_args()
+    return args
+
+
+def print_arguments(args):
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def dynamic_lstm_model(dict_size,
+                       embedding_dim,
+                       hidden_dim,
+                       stacked_num,
+                       class_num=2,
+                       is_train=True):
+    word_idx = tf.placeholder(tf.int64, shape=[None, None])
+    sequence_length = tf.placeholder(tf.int64, shape=[None, ])
+
+    embedding_weights = tf.get_variable('word_embeddings',
+                                        [dict_size, embedding_dim])
+    embedding = tf.nn.embedding_lookup(embedding_weights, word_idx)
+
+    lstm_cell = tf.nn.rnn_cell.LSTMCell(
+        num_units=hidden_dim, use_peepholes=False)
+    stacked_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * stacked_num)
+
+    # final_state [LSTMTuple(c, h), LSTMTuple(c, h) ...] total stacked_num LSTMTuples
+    _, final_state = tf.nn.dynamic_rnn(
+        cell=stacked_cell,
+        inputs=embedding,
+        dtype=tf.float32,
+        sequence_length=sequence_length)
+
+    w = tf.Variable(
+        tf.truncated_normal([hidden_dim, class_num]), dtype=tf.float32)
+    bias = tf.Variable(
+        tf.constant(
+            value=0.0, shape=[class_num], dtype=tf.float32))
+    prediction = tf.matmul(final_state[-1][1], w) + bias
+
+    if not is_train:
+        return (word_idx, sequence_length), tf.nn.softmax(prediction)
+
+    label = tf.placeholder(tf.int64, shape=[None, ])
+    loss = tf.nn.softmax_cross_entropy_with_logits(
+        labels=tf.one_hot(label, 2), logits=prediction)
+    avg_loss = tf.reduce_mean(loss)
+
+    correct_count = tf.equal(tf.argmax(prediction, 1), label)
+    acc = tf.reduce_mean(tf.cast(correct_count, tf.float32))
+
+    with tf.variable_scope("reset_metrics_accuracy_scope") as scope:
+        g_acc = tf.metrics.accuracy(label, tf.argmax(prediction, axis=1))
+        vars = tf.contrib.framework.get_variables(
+            scope, collection=tf.GraphKeys.LOCAL_VARIABLES)
+        reset_op = tf.variables_initializer(vars)
+
+    return (word_idx, sequence_length, label), avg_loss, acc, g_acc, reset_op
+
+
+def padding_data(data, padding_size, value):
+    data = data + [value] * padding_size
+    return data[:padding_size]
+
+
+def train(args):
+    word_dict = paddle.dataset.imdb.word_dict()
+    dict_size = len(word_dict)
+
+    feeding_list, avg_loss, acc, g_acc, reset_op = dynamic_lstm_model(
+        dict_size, args.embedding_dim, args.hidden_dim, args.stacked_num)
+
+    adam_optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
+    train_op = adam_optimizer.minimize(avg_loss)
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=25000),
+        batch_size=args.batch_size)
+
+    test_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.test(word_dict), buf_size=25000),
+        batch_size=args.batch_size)
+
+    def do_validation(sess):
+        sess.run(reset_op)
+        for batch_id, data in enumerate(test_reader()):
+            word_idx = map(lambda x: x[0], data)
+            sequence_length = np.array(
+                [len(seq) for seq in word_idx]).astype('int64')
+            maxlen = np.max(sequence_length)
+            word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
+            word_idx = np.array(word_idx).astype('int64')
+            label = np.array(map(lambda x: x[1], data)).astype('int64')
+
+            _, loss, fetch_acc, fetch_g_acc = sess.run(
+                [train_op, avg_loss, acc, g_acc],
+                feed_dict={
+                    feeding_list[0]: word_idx,
+                    feeding_list[1]: sequence_length,
+                    feeding_list[2]: label
+                })
+
+        return fetch_g_acc[1]
+
+    config = tf.ConfigProto(
+        intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
+    with tf.Session(config=config) as sess:
+        init_g = tf.global_variables_initializer()
+        init_l = tf.local_variables_initializer()
+        sess.run(init_l)
+        sess.run(init_g)
+
+        for pass_id in xrange(args.pass_num):
+            # clear accuracy local variable 
+            sess.run(reset_op)
+            pass_start_time = time.time()
+            words_seen = 0
+
+            for batch_id, data in enumerate(train_reader()):
+                word_idx = map(lambda x: x[0], data)
+                sequence_length = np.array(
+                    [len(seq) for seq in word_idx]).astype('int64')
+                words_seen += np.sum(sequence_length)
+                maxlen = np.max(sequence_length)
+                word_idx = [padding_data(seq, maxlen, 0) for seq in word_idx]
+                word_idx = np.array(word_idx).astype('int64')
+                label = np.array(map(lambda x: x[1], data)).astype('int64')
+
+                _, loss, fetch_acc, fetch_g_acc = sess.run(
+                    [train_op, avg_loss, acc, g_acc],
+                    feed_dict={
+                        feeding_list[0]: word_idx,
+                        feeding_list[1]: sequence_length,
+                        feeding_list[2]: label
+                    })
+
+                print("pass_id=%d, batch_id=%d, loss: %f, acc: %f, avg_acc: %f"
+                      % (pass_id, batch_id, loss, fetch_acc, fetch_g_acc[1]))
+
+            pass_end_time = time.time()
+            time_consumed = pass_end_time - pass_start_time
+            words_per_sec = words_seen / time_consumed
+            test_acc = do_validation(sess)
+            print("pass_id=%d, test_acc: %f, words/s: %f, sec/pass: %f" %
+                  (pass_id, test_acc, words_per_sec, time_consumed))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    print_arguments(args)
+
+    if args.infer_only:
+        pass
+    else:
+        train(args)