本文整理汇总了Python中nn_utils.LSTMCell方法的典型用法代码示例。如果您正苦于以下问题：Python nn_utils.LSTMCell方法的具体用法？Python nn_utils.LSTMCell怎么用？Python nn_utils.LSTMCell使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nn_utils的用法示例。
在下文中一共展示了nn_utils.LSTMCell方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: LSTM_question_embedding
# 需要导入模块: import nn_utils [as 别名]
# 或者: from nn_utils import LSTMCell [as 别名]
def LSTM_question_embedding(self, sentence, sentence_length):
#LSTM processes the input question
lstm_params = "question_lstm"
hidden_vectors = []
sentence = self.batch_question
question_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
question_c_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
if (self.utility.FLAGS.rnn_dropout > 0.0):
if (self.mode == "train"):
rnn_dropout_mask = tf.cast(
tf.random_uniform(
tf.shape(question_hidden), minval=0.0, maxval=1.0) <
self.utility.FLAGS.rnn_dropout,
self.data_type) / self.utility.FLAGS.rnn_dropout
else:
rnn_dropout_mask = tf.ones_like(question_hidden)
for question_iterator in range(self.question_length):
curr_word = sentence[:, question_iterator]
question_vector = nn_utils.apply_dropout(
nn_utils.get_embedding(curr_word, self.utility, self.params),
self.utility.FLAGS.dropout, self.mode)
question_hidden, question_c_hidden = nn_utils.LSTMCell(
question_vector, question_hidden, question_c_hidden, lstm_params,
self.params)
if (self.utility.FLAGS.rnn_dropout > 0.0):
question_hidden = question_hidden * rnn_dropout_mask
hidden_vectors.append(tf.expand_dims(question_hidden, 0))
hidden_vectors = tf.concat(axis=0, values=hidden_vectors)
return question_hidden, hidden_vectors 示例2: LSTM_question_embedding
# 需要导入模块: import nn_utils [as 别名]
# 或者: from nn_utils import LSTMCell [as 别名]
def LSTM_question_embedding(self, sentence, sentence_length):
#LSTM processes the input question
lstm_params = "question_lstm"
hidden_vectors = []
sentence = self.batch_question
question_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
question_c_hidden = tf.zeros(
[self.batch_size, self.utility.FLAGS.embedding_dims], self.data_type)
if (self.utility.FLAGS.rnn_dropout > 0.0):
if (self.mode == "train"):
rnn_dropout_mask = tf.cast(
tf.random_uniform(
tf.shape(question_hidden), minval=0.0, maxval=1.0) <
self.utility.FLAGS.rnn_dropout,
self.data_type) / self.utility.FLAGS.rnn_dropout
else:
rnn_dropout_mask = tf.ones_like(question_hidden)
for question_iterator in range(self.question_length):
curr_word = sentence[:, question_iterator]
question_vector = nn_utils.apply_dropout(
nn_utils.get_embedding(curr_word, self.utility, self.params),
self.utility.FLAGS.dropout, self.mode)
question_hidden, question_c_hidden = nn_utils.LSTMCell(
question_vector, question_hidden, question_c_hidden, lstm_params,
self.params)
if (self.utility.FLAGS.rnn_dropout > 0.0):
question_hidden = question_hidden * rnn_dropout_mask
hidden_vectors.append(tf.expand_dims(question_hidden, 0))
hidden_vectors = tf.concat(0, hidden_vectors)
return question_hidden, hidden_vectors