本文整理汇总了Python中tensorflow.python.ops.lookup_ops.index_table_from_file方法的典型用法代码示例。如果您正苦于以下问题:Python lookup_ops.index_table_from_file方法的具体用法?Python lookup_ops.index_table_from_file怎么用?Python lookup_ops.index_table_from_file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tensorflow.python.ops.lookup_ops
的用法示例。
在下文中一共展示了lookup_ops.index_table_from_file方法的15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: _transform_feature
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def _transform_feature(self, inputs):
input_tensor = _to_sparse_input(inputs.get(self.key))
if self.dtype.is_integer != input_tensor.dtype.is_integer:
raise ValueError(
'Column dtype and SparseTensors dtype must be compatible. '
'key: {}, column dtype: {}, tensor dtype: {}'.format(
self.key, self.dtype, input_tensor.dtype))
_assert_string_or_int(
input_tensor.dtype,
prefix='column_name: {} input_tensor'.format(self.key))
key_dtype = self.dtype
if input_tensor.dtype.is_integer:
# `index_table_from_file` requires 64-bit integer keys.
key_dtype = dtypes.int64
input_tensor = math_ops.to_int64(input_tensor)
return lookup_ops.index_table_from_file(
vocabulary_file=self.vocabulary_file,
num_oov_buckets=self.num_oov_buckets,
vocab_size=self.vocabulary_size,
default_value=self.default_value,
key_dtype=key_dtype,
name='{}_lookup'.format(self.key)).lookup(input_tensor)
示例2: create_tgt_vocab_table
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_tgt_vocab_table(tgt_vocab_file):
"""Creates vocab tables for src_vocab_file and tgt_vocab_file."""
tgt_vocab_table = lookup_ops.index_table_from_file(tgt_vocab_file, default_value=UNK_ID)
return tgt_vocab_table
示例3: create_vocab_tables
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
"""Creates vocab tables for src_vocab_file and tgt_vocab_file."""
src_vocab_table = lookup_ops.index_table_from_file(
src_vocab_file, default_value=UNK_ID)
if share_vocab:
tgt_vocab_table = src_vocab_table
else:
tgt_vocab_table = lookup_ops.index_table_from_file(
tgt_vocab_file, default_value=UNK_ID)
return src_vocab_table, tgt_vocab_table
示例4: create_train_model
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_train_model(hparams, model_creator):
txt_file = "%s.%s" % (hparams.train_prefix, "txt")
lb_file = "%s.%s" % (hparams.train_prefix, "lb")
vocab_file = hparams.vocab_file
index_file = hparams.index_file
graph = tf.Graph()
with graph.as_default(), tf.container("train"):
vocab_table = lookup_ops.index_table_from_file(
vocab_file, default_value = UNK_ID)
# for the labels
index_table = lookup_ops.index_table_from_file(
index_file, default_value = 0)
txt_dataset = tf.data.TextLineDataset(txt_file)
lb_dataset = tf.data.TextLineDataset(lb_file)
iterator = data_iterator.get_iterator(
txt_dataset,
lb_dataset,
vocab_table,
index_table,
batch_size = hparams.batch_size,
num_buckets = hparams.num_buckets,
max_len = hparams.max_len)
model = model_creator(
hparams,
iterator = iterator,
mode = tf.contrib.learn.ModeKeys.TRAIN,
vocab_table = vocab_table)
return TrainModel(
graph = graph,
model = model,
iterator = iterator)
示例5: create_eval_model
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_eval_model(hparams, model_creator):
vocab_file = hparams.vocab_file
index_file = hparams.index_file
graph = tf.Graph()
with graph.as_default(), tf.container("eval"):
vocab_table = lookup_ops.index_table_from_file(
vocab_file, default_value = UNK_ID)
# for the labels
index_table = lookup_ops.index_table_from_file(
index_file, default_value = 0)
# the file's name
txt_file_placeholder = tf.placeholder(shape = (), dtype = tf.string)
lb_file_placeholder = tf.placeholder(shape = (), dtype = tf.string)
txt_dataset = tf.data.TextLineDataset(txt_file_placeholder)
lb_dataset = tf.data.TextLineDataset(lb_file_placeholder)
iterator = data_iterator.get_iterator(
txt_dataset,
lb_dataset,
vocab_table,
index_table,
batch_size = hparams.batch_size,
num_buckets = hparams.num_buckets,
max_len = hparams.max_len)
model = model_creator(
hparams,
iterator = iterator,
mode = tf.contrib.learn.ModeKeys.EVAL,
vocab_table = vocab_table)
return EvalModel(
graph = graph,
model = model,
txt_file_placeholder = txt_file_placeholder,
lb_file_placeholder = lb_file_placeholder,
iterator = iterator)
示例6: create_infer_model
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_infer_model(hparams, model_creator):
"""Create inference model."""
graph = tf.Graph()
vocab_file = hparams.vocab_file
with graph.as_default(), tf.container("infer"):
vocab_table = lookup_ops.index_table_from_file(
vocab_file, default_value = UNK_ID)
# for the labels
'''
Although this is nonsense for the inference procedure, this is to ensure
the labels are not None when building the model graph.
(refer to model.BasicModel._decode_layer)
'''
mapping_strings = tf.constant(['0'])
index_table = tf.contrib.lookup.index_table_from_tensor(
mapping = mapping_strings, default_value = 0)
txt_placeholder = tf.placeholder(shape=[None], dtype = tf.string)
batch_size_placeholder = tf.placeholder(shape = [], dtype = tf.int64)
txt_dataset = tf.data.Dataset.from_tensor_slices(
txt_placeholder)
iterator = data_iterator.get_infer_iterator(
txt_dataset,
vocab_table,
index_table,
batch_size = batch_size_placeholder)
model = model_creator(
hparams,
iterator = iterator,
mode = tf.contrib.learn.ModeKeys.INFER,
vocab_table = vocab_table)
return InferModel(
graph = graph,
model = model,
txt_placeholder = txt_placeholder,
batch_size_placeholder = batch_size_placeholder,
iterator = iterator)
示例7: __init__
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def __init__(self, corpus_dir, hparams=None, training=True, buffer_size=8192):
"""
Args:
corpus_dir: Name of the folder storing corpus files for training.
hparams: The object containing the loaded hyper parameters. If None, it will be
initialized here.
training: Whether to use this object for training.
buffer_size: The buffer size used for mapping process during data processing.
"""
if hparams is None:
self.hparams = HParams(corpus_dir).hparams
else:
self.hparams = hparams
self.src_max_len = self.hparams.src_max_len
self.tgt_max_len = self.hparams.tgt_max_len
self.training = training
self.text_set = None
self.id_set = None
vocab_file = os.path.join(corpus_dir, VOCAB_FILE)
self.vocab_size, _ = check_vocab(vocab_file)
self.vocab_table = lookup_ops.index_table_from_file(vocab_file,
default_value=self.hparams.unk_id)
# print("vocab_size = {}".format(self.vocab_size))
if training:
self.case_table = prepare_case_table()
self.reverse_vocab_table = None
self._load_corpus(corpus_dir)
self._convert_to_tokens(buffer_size)
else:
self.case_table = None
self.reverse_vocab_table = \
lookup_ops.index_to_string_table_from_file(vocab_file,
default_value=self.hparams.unk_token)
示例8: create_vocab_table
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_vocab_table(vocab_file):
"""Creates vocab tables for vocab_file."""
return lookup_ops.index_table_from_file(vocab_file, default_value=UNK_ID)
示例9: create_vocab_tables
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_vocab_tables(vocab_file):
"""Creates vocab tables for src_vocab_file and tgt_vocab_file."""
vocab_table = lookup_ops.index_table_from_file(
vocab_file, default_value=0)
return vocab_table
示例10: create_vocab_tables
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab, vocab_size):
src_vocab_table = lookup_ops.index_table_from_file(
src_vocab_file, default_value=vocab_size)
if share_vocab:
tgt_vocab_table = src_vocab_table
else:
tgt_vocab_table = lookup_ops.index_table_from_file(
tgt_vocab_file, default_value=vocab_size)
return src_vocab_table, tgt_vocab_table
示例11: create_vocab_tables
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_vocab_tables(src_vocab_file, tgt_vocab_file, share_vocab):
"""Creates vocab tables for src_vocab_file and tgt_vocab_file."""
src_vocab_table = lookup_ops.index_table_from_file(src_vocab_file, default_value=UNK_ID)
if share_vocab:
tgt_vocab_table = src_vocab_table
else:
tgt_vocab_table = lookup_ops.index_table_from_file(tgt_vocab_file, default_value=UNK_ID)
return src_vocab_table, tgt_vocab_table
示例12: create_input_data
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_input_data(source_data_file, target_data_file,
vocab_file,
batch_size, sos, eos, unk_id,
source_max_length, target_max_length):
source_dataset = tf.data.TextLineDataset(tf.gfile.Glob(source_data_file))
target_dataset = tf.data.TextLineDataset(tf.gfile.Glob(target_data_file))
vocab = lookup_ops.index_table_from_file(vocab_file, default_value=unk_id)
output_buffer_size = batch_size * 1000
sos_id = tf.cast(vocab.lookup(tf.constant(sos)), tf.int32)
eos_id = tf.cast(vocab.lookup(tf.constant(eos)), tf.int32)
dataset = tf.data.Dataset.zip((source_dataset, target_dataset))
dataset = dataset.map(
lambda src, tgt: (tf.string_split([src]).values,
tf.string_split([tgt]).values)).prefetch(output_buffer_size)
dataset = dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
# dataset = dataset.map(
# lambda src, tgt: (src[:source_max_length], tgt[:target_max_length]))
dataset = dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) <= source_max_length, tf.size(tgt) <= target_max_length))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (tf.cast(vocab.lookup(src), tf.int32),
tf.cast(vocab.lookup(tgt), tf.int32)))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (src,
tf.concat(([sos_id], tgt), 0),
tf.concat((tgt, [eos_id]), 0))).prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in))).prefetch(output_buffer_size)
dataset = dataset.shuffle(100).repeat().padded_batch(
batch_size,
padded_shapes=(tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([]),
tf.TensorShape([])),
padding_values=(eos_id,
eos_id,
eos_id,
0,
0))
iterator = dataset.make_initializable_iterator()
return iterator.get_next(), iterator.initializer, vocab
# ======================== SEQ2SEQ NETWORK =============================
示例13: create_input_data
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_input_data(source_data_file, target_data_file,
source_vocab_file, target_vocab_file,
batch_size, sos, eos, unk_id,
source_max_length, target_max_length):
source_dataset = tf.data.TextLineDataset(tf.gfile.Glob(source_data_file))
target_dataset = tf.data.TextLineDataset(tf.gfile.Glob(target_data_file))
source_vocab = lookup_ops.index_table_from_file(
source_vocab_file, default_value=unk_id)
target_vocab = lookup_ops.index_table_from_file(
target_vocab_file, default_value=unk_id)
output_buffer_size = batch_size * 1000
source_eos_id = tf.cast(source_vocab.lookup(tf.constant(eos)), tf.int32)
target_sos_id = tf.cast(target_vocab.lookup(tf.constant(sos)), tf.int32)
target_eos_id = tf.cast(target_vocab.lookup(tf.constant(eos)), tf.int32)
dataset = tf.data.Dataset.zip((source_dataset, target_dataset))
dataset = dataset.map(
lambda src, tgt: (tf.string_split([src]).values,
tf.string_split([tgt]).values)).prefetch(output_buffer_size)
dataset = dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
dataset = dataset.map(
lambda src, tgt: (src[:source_max_length], tgt[:target_max_length]))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (tf.cast(source_vocab.lookup(src), tf.int32),
tf.cast(target_vocab.lookup(tgt), tf.int32)))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (src,
tf.concat(([target_sos_id], tgt), 0),
tf.concat((tgt, [target_eos_id]), 0))).prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in))).prefetch(output_buffer_size)
dataset = dataset.shuffle(100).repeat().padded_batch(
batch_size,
padded_shapes=(tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([]),
tf.TensorShape([])),
padding_values=(source_eos_id,
target_eos_id,
target_eos_id,
0,
0))
iterator = dataset.make_initializable_iterator()
return iterator.get_next(), iterator.initializer, source_vocab, target_vocab
# ======================== SEQ2SEQ NETWORK =============================
示例14: create_input_data
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_input_data(source_data_file, target_data_file,
source_vocab_file, target_vocab_file,
batch_size, sos, eos,
source_max_length, target_max_length):
source_dataset = tf.data.TextLineDataset(tf.gfile.Glob(source_data_file))
target_dataset = tf.data.TextLineDataset(tf.gfile.Glob(target_data_file))
source_vocab = lookup_ops.index_table_from_file(
source_vocab_file, default_value=FLAGS.unk_id)
target_vocab = lookup_ops.index_table_from_file(
target_vocab_file, default_value=FLAGS.unk_id)
output_buffer_size = batch_size * 1000
source_eos_id = tf.cast(source_vocab.lookup(tf.constant(eos)), tf.int32)
target_sos_id = tf.cast(target_vocab.lookup(tf.constant(sos)), tf.int32)
target_eos_id = tf.cast(target_vocab.lookup(tf.constant(eos)), tf.int32)
dataset = tf.data.Dataset.zip((source_dataset, target_dataset))
dataset = dataset.map(
lambda src, tgt: (tf.string_split([src]).values,
tf.string_split([tgt]).values)).prefetch(output_buffer_size)
dataset = dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
dataset = dataset.map(
lambda src, tgt: (src[:source_max_length], tgt[:target_max_length]))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (tf.cast(source_vocab.lookup(src), tf.int32),
tf.cast(target_vocab.lookup(tgt), tf.int32)))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (src,
tf.concat(([target_sos_id], tgt), 0),
tf.concat((tgt, [target_eos_id]), 0))).prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in))).prefetch(output_buffer_size)
dataset = dataset.shuffle(100).repeat().padded_batch(
batch_size,
padded_shapes=(tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([]),
tf.TensorShape([])),
padding_values=(source_eos_id,
target_eos_id,
target_eos_id,
0,
0))
iterator = dataset.make_initializable_iterator()
return iterator.get_next(), iterator.initializer, source_vocab, target_vocab
# ======================== SEQ2SEQ NETWORK =============================
示例15: create_input_data
# 需要导入模块: from tensorflow.python.ops import lookup_ops [as 别名]
# 或者: from tensorflow.python.ops.lookup_ops import index_table_from_file [as 别名]
def create_input_data(source_data_file, target_data_file,
source_vocab_file, target_vocab_file,
batch_size, sos, eos, unk_id,
source_max_length, target_max_length):
source_dataset = tf.data.TextLineDataset(tf.gfile.Glob(source_data_file))
target_dataset = tf.data.TextLineDataset(tf.gfile.Glob(target_data_file))
source_vocab = lookup_ops.index_table_from_file(
source_vocab_file, default_value=unk_id)
target_vocab = lookup_ops.index_table_from_file(
target_vocab_file, default_value=unk_id)
output_buffer_size = batch_size * 1000
source_eos_id = tf.cast(source_vocab.lookup(tf.constant(eos)), tf.int32)
target_sos_id = tf.cast(target_vocab.lookup(tf.constant(sos)), tf.int32)
target_eos_id = tf.cast(target_vocab.lookup(tf.constant(eos)), tf.int32)
dataset = tf.data.Dataset.zip((source_dataset, target_dataset))
dataset = dataset.map(
lambda src, tgt: (tf.string_split([src]).values,
tf.string_split([tgt]).values)).prefetch(output_buffer_size)
dataset = dataset.filter(
lambda src, tgt: tf.logical_and(tf.size(src) > 0, tf.size(tgt) > 0))
dataset = dataset.map(
lambda src, tgt: (src[:source_max_length], tgt[:target_max_length]))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (tf.cast(source_vocab.lookup(src), tf.int32),
tf.cast(target_vocab.lookup(tgt), tf.int32)))
dataset = dataset.prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt: (tf.reverse(src, axis=[0]),
tf.concat(([target_sos_id], tgt), 0),
tf.concat((tgt, [target_eos_id]), 0))).prefetch(output_buffer_size)
dataset = dataset.map(
lambda src, tgt_in, tgt_out: (
src, tgt_in, tgt_out, tf.size(src), tf.size(tgt_in))).prefetch(output_buffer_size)
dataset = dataset.shuffle(100).repeat().padded_batch(
batch_size,
padded_shapes=(tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([None]),
tf.TensorShape([]),
tf.TensorShape([])),
padding_values=(source_eos_id,
target_eos_id,
target_eos_id,
0,
0))
iterator = dataset.make_initializable_iterator()
return iterator.get_next(), iterator.initializer, source_vocab, target_vocab
# ======================== SEQ2SEQ NETWORK =============================