本文整理匯總了Python中skip_thoughts.skip_thoughts_encoder.SkipThoughtsEncoder方法的典型用法代碼示例。如果您正苦於以下問題：Python skip_thoughts_encoder.SkipThoughtsEncoder方法的具體用法？Python skip_thoughts_encoder.SkipThoughtsEncoder怎麽用？Python skip_thoughts_encoder.SkipThoughtsEncoder使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類skip_thoughts.skip_thoughts_encoder的用法示例。
在下文中一共展示了skip_thoughts_encoder.SkipThoughtsEncoder方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: load_model
# 需要導入模塊: from skip_thoughts import skip_thoughts_encoder [as 別名]
# 或者: from skip_thoughts.skip_thoughts_encoder import SkipThoughtsEncoder [as 別名]
def load_model(self, model_config, vocabulary_file, embedding_matrix_file,
checkpoint_path):
"""Loads a skip-thoughts model.
Args:
model_config: Object containing parameters for building the model.
vocabulary_file: Path to vocabulary file containing a list of newline-
separated words where the word id is the corresponding 0-based index in
the file.
embedding_matrix_file: Path to a serialized numpy array of shape
[vocab_size, embedding_dim].
checkpoint_path: SkipThoughtsModel checkpoint file or a directory
containing a checkpoint file.
"""
tf.logging.info("Reading vocabulary from %s", vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode="r") as f:
lines = list(f.readlines())
reverse_vocab = [line.decode("utf-8").strip() for line in lines]
tf.logging.info("Loaded vocabulary with %d words.", len(reverse_vocab))
tf.logging.info("Loading embedding matrix from %s", embedding_matrix_file)
# Note: tf.gfile.GFile doesn't work here because np.load() calls f.seek()
# with 3 arguments.
embedding_matrix = np.load(embedding_matrix_file)
tf.logging.info("Loaded embedding matrix with shape %s",
embedding_matrix.shape)
word_embeddings = collections.OrderedDict(
zip(reverse_vocab, embedding_matrix))
g = tf.Graph()
with g.as_default():
encoder = skip_thoughts_encoder.SkipThoughtsEncoder(word_embeddings)
restore_model = encoder.build_graph_from_config(model_config,
checkpoint_path)
sess = tf.Session(graph=g)
restore_model(sess)
self.encoders.append(encoder)
self.sessions.append(sess) 示例2: load_model
# 需要導入模塊: from skip_thoughts import skip_thoughts_encoder [as 別名]
# 或者: from skip_thoughts.skip_thoughts_encoder import SkipThoughtsEncoder [as 別名]
def load_model(self, model_config, vocabulary_file, embedding_matrix_file,
checkpoint_path):
"""Loads a skip-thoughts model.
Args:
model_config: Object containing parameters for building the model.
vocabulary_file: Path to vocabulary file containing a list of newline-
separated words where the word id is the corresponding 0-based index in
the file.
embedding_matrix_file: Path to a serialized numpy array of shape
[vocab_size, embedding_dim].
checkpoint_path: SkipThoughtsModel checkpoint file or a directory
containing a checkpoint file.
"""
tf.logging.info("Reading vocabulary from %s", vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode="r") as f:
lines = list(f.readlines())
reverse_vocab = [line.decode("utf-8").strip() for line in lines]
tf.logging.info("Loaded vocabulary with %d words.", len(reverse_vocab))
tf.logging.info("Loading embedding matrix from %s", embedding_matrix_file)
# Note: tf.gfile.GFile doesn't work here because np.load() calls f.seek()
# with 3 arguments.
with open(embedding_matrix_file, "r") as f:
embedding_matrix = np.load(f)
tf.logging.info("Loaded embedding matrix with shape %s",
embedding_matrix.shape)
word_embeddings = collections.OrderedDict(
zip(reverse_vocab, embedding_matrix))
g = tf.Graph()
with g.as_default():
encoder = skip_thoughts_encoder.SkipThoughtsEncoder(word_embeddings)
restore_model = encoder.build_graph_from_config(model_config,
checkpoint_path)
sess = tf.Session(graph=g)
restore_model(sess)
self.encoders.append(encoder)
self.sessions.append(sess) 示例3: load_model
# 需要導入模塊: from skip_thoughts import skip_thoughts_encoder [as 別名]
# 或者: from skip_thoughts.skip_thoughts_encoder import SkipThoughtsEncoder [as 別名]
def load_model(self, model_config, vocabulary_file, embedding_matrix_file,
checkpoint_path):
"""Loads a skip-thoughts model.
Args:
model_config: Object containing parameters for building the model.
vocabulary_file: Path to vocabulary file containing a list of newline-
separated words where the word id is the corresponding 0-based index in
the file.
embedding_matrix_file: Path to a serialized numpy array of shape
[vocab_size, embedding_dim].
checkpoint_path: SkipThoughtsModel checkpoint file or a directory
containing a checkpoint file.
"""
tf.logging.info("Reading vocabulary from %s", vocabulary_file)
with tf.gfile.GFile(vocabulary_file, mode="rb") as f:
lines = list(f.readlines())
reverse_vocab = [line.decode("utf-8").strip() for line in lines]
tf.logging.info("Loaded vocabulary with %d words.", len(reverse_vocab))
tf.logging.info("Loading embedding matrix from %s", embedding_matrix_file)
# Note: tf.gfile.GFile doesn't work here because np.load() calls f.seek()
# with 3 arguments.
embedding_matrix = np.load(embedding_matrix_file)
tf.logging.info("Loaded embedding matrix with shape %s",
embedding_matrix.shape)
word_embeddings = collections.OrderedDict(
zip(reverse_vocab, embedding_matrix))
g = tf.Graph()
with g.as_default():
encoder = skip_thoughts_encoder.SkipThoughtsEncoder(word_embeddings)
restore_model = encoder.build_graph_from_config(model_config,
checkpoint_path)
sess = tf.Session(graph=g)
restore_model(sess)
self.encoders.append(encoder)
self.sessions.append(sess)