本文整理汇总了Python中wmt_utils.initialize_vocabulary方法的典型用法代码示例。如果您正苦于以下问题：Python wmt_utils.initialize_vocabulary方法的具体用法？Python wmt_utils.initialize_vocabulary怎么用？Python wmt_utils.initialize_vocabulary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wmt_utils的用法示例。
在下文中一共展示了wmt_utils.initialize_vocabulary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: print_vectors
# 需要导入模块: import wmt_utils [as 别名]
# 或者: from wmt_utils import initialize_vocabulary [as 别名]
def print_vectors(embedding_key, vocab_path, word_vector_file):
"""Print vectors from the given variable."""
_, rev_vocab = wmt.initialize_vocabulary(vocab_path)
vectors_variable = [v for v in tf.trainable_variables()
if embedding_key == v.name]
if len(vectors_variable) != 1:
data.print_out("Word vector variable not found or too many.")
sys.exit(1)
vectors_variable = vectors_variable[0]
vectors = vectors_variable.eval()
l, s = vectors.shape[0], vectors.shape[1]
data.print_out("Printing %d word vectors from %s to %s."
% (l, embedding_key, word_vector_file))
with tf.gfile.GFile(word_vector_file, mode="w") as f:
# Lines have format: dog 0.045123 -0.61323 0.413667 ...
for i in xrange(l):
f.write(rev_vocab[i])
for j in xrange(s):
f.write(" %.8f" % vectors[i][j])
f.write("\n") 示例2: assign_vectors
# 需要导入模块: import wmt_utils [as 别名]
# 或者: from wmt_utils import initialize_vocabulary [as 别名]
def assign_vectors(word_vector_file, embedding_key, vocab_path, sess):
"""Assign the embedding_key variable from the given word vectors file."""
# For words in the word vector file, set their embedding at start.
if not tf.gfile.Exists(word_vector_file):
data.print_out("Word vector file does not exist: %s" % word_vector_file)
sys.exit(1)
vocab, _ = wmt.initialize_vocabulary(vocab_path)
vectors_variable = [v for v in tf.trainable_variables()
if embedding_key == v.name]
if len(vectors_variable) != 1:
data.print_out("Word vector variable not found or too many.")
sys.exit(1)
vectors_variable = vectors_variable[0]
vectors = vectors_variable.eval()
data.print_out("Pre-setting word vectors from %s" % word_vector_file)
with tf.gfile.GFile(word_vector_file, mode="r") as f:
# Lines have format: dog 0.045123 -0.61323 0.413667 ...
for line in f:
line_parts = line.split()
# The first part is the word.
word = line_parts[0]
if word in vocab:
# Remaining parts are components of the vector.
word_vector = np.array(map(float, line_parts[1:]))
if len(word_vector) != FLAGS.vec_size:
data.print_out("Warn: Word '%s', Expecting vector size %d, "
"found %d" % (word, FLAGS.vec_size,
len(word_vector)))
else:
vectors[vocab[word]] = word_vector
# Assign the modified vectors to the vectors_variable in the graph.
sess.run([vectors_variable.initializer],
{vectors_variable.initializer.inputs[1]: vectors})