TensorFlow实现Attention机制

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发布于 2018-07-30 15:37:00

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发布于 2018-07-30 15:37:00

文章被收录于专栏：人工智能LeadAI

正文共996个字，10张图，预计阅读时间15分钟。

原理介绍

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更多资料： https://distill.pub/2016/augmented-rnns/#attentional-interfaces https://www.cnblogs.com/shixiangwan/p/7573589.html#top

http://baijiahao.baidu.com/s?id=1587926245504773589&wfr=spider&for=pc

论文阅读

Hierarchical Attention Networks for Document Classification（http://www.aclweb.org/anthology/N16-1174）

这篇文章主要讲述了基于Attention机制实现文本分类

假设我们有很多新闻文档，这些文档属于三类：军事、体育、娱乐。其中有一个文档D有L个句子si（i代表s是文档D的第i个句子），每个句子包含Ti个词（word），wit代表第i个句子的word,t∈[0,T]

Word Encoder：

①给定一个句子si,例如 The superstar is walking in the street，由下面表示[wi1,wi2,wi3,wi4,wi5,wi6,wi1,wi7]，我们使用一个词嵌入矩阵W将单词编码为向量

②使用双向GRU编码整个句子关于单词wit的隐含向量：

那么最终隐含向量为前向隐含向量和后向隐含向量拼接在一起

Word Attention：

给定一句话，并不是这个句子中所有的单词对个句子语义起同等大小的“贡献”，比如上句话“The”,“is”等，这些词没有太大作用，因此我们需要使用attention机制来提炼那些比较重要的单词，通过赋予权重以提高他们的重要性。

①通过一个MLP获取hit的隐含表示：

②通过一个softmax函数获取归一化的权重：

③计算句子向量：

通过每个单词获取的hit与对应权重αit乘积，然后获取获得句子向量

代码实现

  1attenton.py
  2import tensorflow as tf
  3def attention(inputs, attention_size, time_major=False, return_alphas=False):
  4 if isinstance(inputs, tuple):
  5# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
  6inputs = tf.concat(inputs, 2)
  7if time_major:
  8# (T,B,D) => (B,T,D)
  9inputs = tf.array_ops.transpose(inputs, [1, 0, 2])
 10hidden_size = inputs.shape[2].value  # D value - hidden size of the RNN layer
 11# Trainable parameters
 12w_omega = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))
 13b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
 14u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))
 15with tf.name_scope('v'):
 16# Applying fully connected layer with non-linear activation to each of the B*T timestamps;
 17#  the shape of `v` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size
 18v = tf.tanh(tf.tensordot(inputs, w_omega, axes=1) + b_omega)
 19# For each of the timestamps its vector of size A from `v` is reduced with `u` vector
 20vu = tf.tensordot(v, u_omega, axes=1, name='vu')  # (B,T) shape
 21alphas = tf.nn.softmax(vu, name='alphas')         # (B,T) shape
 22# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
 23output = tf.reduce_sum(inputs * tf.expand_dims(alphas, -1), 1)
 24if not return_alphas:
 25return output
 26else:
 27return output, alphas
 28train.py
 29from __future__ import print_function, division
 30import numpy as np
 31import tensorflow as tf
 32from keras.datasets import imdb
 33from tensorflow.contrib.rnn import GRUCell
 34from tensorflow.python.ops.rnn import   bidirectional_dynamic_rnn as bi_rnn
 35from tqdm import tqdm
 36from attention import attention
 37from utils import get_vocabulary_size, fit_in_vocabulary, zero_pad, batch_generator
 38NUM_WORDS = 10000
 39INDEX_FROM = 3
 40SEQUENCE_LENGTH = 250
 41EMBEDDING_DIM = 100
 42HIDDEN_SIZE = 150
 43ATTENTION_SIZE = 50
 44KEEP_PROB = 0.8
 45BATCH_SIZE = 256
 46NUM_EPOCHS = 3  # Model easily overfits without pre-trained words embeddings, that's why train for a few epochs
 47 DELTA = 0.5
 48 MODEL_PATH = './model'
 49# Load the data set
 50(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)
 51# Sequences pre-processing
 52vocabulary_size = get_vocabulary_size(X_train)
 53X_test = fit_in_vocabulary(X_test, vocabulary_size)
 54X_train = zero_pad(X_train, SEQUENCE_LENGTH)
 55X_test = zero_pad(X_test, SEQUENCE_LENGTH)
 56# Different placeholders
 57with tf.name_scope('Inputs'):
 58batch_ph = tf.placeholder(tf.int32, [None,  SEQUENCE_LENGTH], name='batch_ph')
 59target_ph = tf.placeholder(tf.float32, [None], name='target_ph')
 60seq_len_ph = tf.placeholder(tf.int32, [None], name='seq_len_ph')
 61 keep_prob_ph = tf.placeholder(tf.float32, name='keep_prob_ph')
 62 # Embedding layer
 63 with tf.name_scope('Embedding_layer'):
 64 embeddings_var = tf.Variable(tf.random_uniform([vocabulary_size, EMBEDDING_DIM], -1.0, 1.0), trainable=True)
 65 tf.summary.histogram('embeddings_var', embeddings_var)
 66 batch_embedded = tf.nn.embedding_lookup(embeddings_var, batch_ph)
 67 # (Bi-)RNN layer(-s)
 68 rnn_outputs, _ = bi_rnn(GRUCell(HIDDEN_SIZE), GRUCell(HIDDEN_SIZE),
 69                inputs=batch_embedded, sequence_length=seq_len_ph, dtype=tf.float32)
 70 tf.summary.histogram('RNN_outputs', rnn_outputs)
 71 # Attention layer
 72 with tf.name_scope('Attention_layer'):
 73 attention_output, alphas = attention(rnn_outputs, ATTENTION_SIZE, return_alphas=True)
 74 tf.summary.histogram('alphas', alphas)
 75 # Dropout
 76 drop = tf.nn.dropout(attention_output, keep_prob_ph)
 77 # Fully connected layer
 78 with tf.name_scope('Fully_connected_layer'):
 79 W = tf.Variable(tf.truncated_normal([HIDDEN_SIZE * 2, 1], stddev=0.1))  # Hidden size is multiplied by 2 for Bi-RNN
 80 b = tf.Variable(tf.constant(0., shape=[1]))
 81 y_hat = tf.nn.xw_plus_b(drop, W, b)
 82 y_hat = tf.squeeze(y_hat)
 83 tf.summary.histogram('W', W)
 84 with tf.name_scope('Metrics'):
 85 # Cross-entropy loss and optimizer initialization
 86 loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_hat, labels=target_ph))
 87  tf.summary.scalar('loss', loss)
 88  optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(loss)
 89  # Accuracy metric
 90  accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.round(tf.sigmoid(y_hat)), target_ph), tf.float32))
 91  tf.summary.scalar('accuracy', accuracy)
 92  merged = tf.summary.merge_all()
 93  # Batch generators
 94  train_batch_generator = batch_generator(X_train, y_train, BATCH_SIZE)
 95  test_batch_generator = batch_generator(X_test, y_test, BATCH_SIZE)
 96  train_writer = tf.summary.FileWriter('./logdir/train', accuracy.graph)
 97  test_writer = tf.summary.FileWriter('./logdir/test', accuracy.graph)
 98  session_conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
 99  saver = tf.train.Saver()
100  if __name__ == "__main__":
101  with tf.Session(config=session_conf) as sess:
102  sess.run(tf.global_variables_initializer())
103  print("Start learning...")
104  for epoch in range(NUM_EPOCHS):
105  loss_train = 0
106  loss_test = 0
107  accuracy_train = 0
108  accuracy_test = 0
109  print("epoch: {}\t".format(epoch), end="")
110  # Training
111  num_batches = X_train.shape[0] // BATCH_SIZE
112  for b in tqdm(range(num_batches)):
113        x_batch, y_batch = next(train_batch_generator)
114        seq_len = np.array([list(x).index(0) + 1 for x in x_batch])  # actual lengths of sequences
115        loss_tr, acc, _, summary = sess.run([loss, accuracy, optimizer, merged],
116        feed_dict={batch_ph: x_batch,                                                        target_ph: y_batch,                                                         seq_len_ph: seq_len,                                                         keep_prob_ph: KEEP_PROB})
117accuracy_train += acc
118loss_train = loss_tr * DELTA + loss_train * (1 - DELTA)
119train_writer.add_summary(summary, b + num_batches * epoch)
120accuracy_train /= num_batches
121    # Testing
122    num_batches = X_test.shape[0] // BATCH_SIZE
123    for b in tqdm(range(num_batches)):
124        x_batch, y_batch = next(test_batch_generator)
125        seq_len = np.array([list(x).index(0) + 1 for x in x_batch])  # actual lengths of sequences
126        loss_test_batch, acc, summary = sess.run([loss, accuracy, merged],                                                 feed_dict={batch_ph: x_batch,                                                                 target_ph: y_batch,                                                              seq_len_ph: seq_len,                                                              keep_prob_ph: 1.0})
127        accuracy_test += acc
128        loss_test += loss_test_batch
129        test_writer.add_summary(summary, b + num_batches * epoch)
130    accuracy_test /= num_batches
131    loss_test /= num_batches
132    print("loss: {:.3f}, val_loss: {:.3f}, acc: {:.3f}, val_acc: {:.3f}".format(
133        loss_train, loss_test, accuracy_train, accuracy_test
134    ))
135train_writer.close()
136test_writer.close()
137saver.save(sess, MODEL_PATH)
138print("Run 'tensorboard --logdir=./logdir' to checkout tensorboard logs.")
139utils.py
140from __future__ import print_function
141import numpy as np
142def zero_pad(X, seq_len):
143return np.array([x[:seq_len - 1] + [0] * max(seq_len - len(x), 1) for x in X])
144 def get_vocabulary_size(X):
145 return max([max(x) for x in X]) + 1  # plus the 0th word
146 def fit_in_vocabulary(X, voc_size):
147 return [[w for w in x if w < voc_size] for x in X]
148 def batch_generator(X, y, batch_size):
149 """Primitive batch generator 
150 """
151 size = X.shape[0]
152 X_copy = X.copy()
153 y_copy = y.copy()
154 indices = np.arange(size)
155 np.random.shuffle(indices)
156 X_copy = X_copy[indices]
157 y_copy = y_copy[indices]
158 i = 0
159 while True:
160if i + batch_size <= size:
161    yield X_copy[i:i + batch_size], y_copy[i:i + batch_size]
162    i += batch_size
163else:
164    i = 0
165    indices = np.arange(size)
166    np.random.shuffle(indices)
167    X_copy = X_copy[indices]
168    y_copy = y_copy[indices]
169    continue
170if __name__ == "__main__":
171# Test batch generator
172gen = batch_generator(np.array(['a', 'b', 'c', 'd']), np.array([1, 2, 3, 4]), 2)
173for _ in range(8):
174xx, yy = next(gen)
175print(xx, yy)

代码地址：https://github.com/ilivans/tf-rnn-attention

运行结果