本文整理汇总了Python中tensorflow.python.ops.variables.PartitionedVariable方法的典型用法代码示例。如果您正苦于以下问题：Python variables.PartitionedVariable方法的具体用法？Python variables.PartitionedVariable怎么用？Python variables.PartitionedVariable使用的例子？那么恭喜您, 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模块tensorflow.python.ops.variables的用法示例。

在下文中一共展示了variables.PartitionedVariable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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: testPartitionedVariable

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def testPartitionedVariable(self):

with tf.Graph().as_default():

v0 = tf.Variable([0])

v1 = tf.Variable([1])

v0._set_save_slice_info(variables.Variable.SaveSliceInfo(

v0.name, [2], [0], [1]))

v1._set_save_slice_info(variables.Variable.SaveSliceInfo(

v0.name, [2], [1], [1]))

partitions = [2]

# Pass variable_list as [v1, v0] to ensure they are properly

# re-sorted to [v0, v1] based on their slice info offsets.

partitioned_variable = variables.PartitionedVariable(

name="two_vars",

shape=[2],

dtype=v0.dtype,

variable_list=[v1, v0],

partitions=partitions)

concatenated = tf.convert_to_tensor(partitioned_variable)

num_partitions = len(partitioned_variable)

iterated_partitions = list(partitioned_variable)

self.assertEqual(2, num_partitions)

self.assertEqual([v0, v1], iterated_partitions)

self.assertEqual([2], concatenated.get_shape())

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:27，

示例2: _add_variable_to_collections

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _add_variable_to_collections(variable, collections_set, collections_name):

"""Adds variable (or all its parts) to all collections with that name."""

collections = utils.get_variable_collections(collections_set,

collections_name) or []

variables_list = [variable]

if isinstance(variable, tf_variables.PartitionedVariable):

variables_list = [v for v in variable]

for collection in collections:

for var in variables_list:

if var not in ops.get_collection(collection):

ops.add_to_collection(collection, var)

开发者ID:taehoonlee，项目名称:tensornets，代码行数:13，

示例3: _rnn_get_variable

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _rnn_get_variable(self, getter, *args, **kwargs):

variable = getter(*args, **kwargs)

trainable = (variable in tf_variables.trainable_variables() or

(isinstance(variable, tf_variables.PartitionedVariable) and

list(variable)[0] in tf_variables.trainable_variables()))

if trainable and variable not in self._trainable_weights:

self._trainable_weights.append(variable)

elif not trainable and variable not in self._non_trainable_weights:

self._non_trainable_weights.append(variable)

return variable

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:12，

示例4: _get_dense_tensor

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):

# Get sparse IDs and weights.

sparse_tensors = self.categorical_column._get_sparse_tensors( # pylint: disable=protected-access

inputs, weight_collections=weight_collections, trainable=trainable)

sparse_ids = sparse_tensors.id_tensor

sparse_weights = sparse_tensors.weight_tensor

# Create embedding weight, and restore from checkpoint if necessary.

embedding_weights = variable_scope.get_variable(

name='embedding_weights',

shape=(self.categorical_column._num_buckets, self.dimension), # pylint: disable=protected-access

dtype=dtypes.float32,

initializer=self.initializer,

trainable=self.trainable and trainable,

collections=weight_collections)

if self.ckpt_to_load_from is not None:

to_restore = embedding_weights

if isinstance(to_restore, variables.PartitionedVariable):

to_restore = to_restore._get_variable_list() # pylint: disable=protected-access

checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {

self.tensor_name_in_ckpt: to_restore

})

# Return embedding lookup result.

return _safe_embedding_lookup_sparse(

embedding_weights=embedding_weights,

sparse_ids=sparse_ids,

sparse_weights=sparse_weights,

combiner=self.combiner,

name='%s_weights' % self.name,

max_norm=self.max_norm)

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:33，

示例5: _add_variable_to_collections

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _add_variable_to_collections(variable, collections_set, collections_name):

"""Adds variable (or all its parts) to all collections with that name."""

collections = utils.get_variable_collections(

collections_set, collections_name) or []

variables_list = [variable]

if isinstance(variable, tf_variables.PartitionedVariable):

variables_list = [v for v in variable]

for collection in collections:

for var in variables_list:

if var not in ops.get_collection(collection):

ops.add_to_collection(collection, var)

开发者ID:ryfeus，项目名称:lambda-packs，代码行数:13，

示例6: embedding_lookup_unique

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def embedding_lookup_unique(params, ids, name=None):

"""Version of embedding_lookup that avoids duplicate lookups.

This can save communication in the case of repeated ids.

Same interface as embedding_lookup. Except it supports multi-dimensional `ids`

which allows to not reshape input/output to fit gather.

Args:

params: A list of tensors with the same shape and type, or a

`PartitionedVariable`. Shape `[index, d1, d2, ...]`.

ids: A one-dimensional `Tensor` with type `int32` or `int64` containing

the ids to be looked up in `params`. Shape `[ids1, ids2, ...]`.

name: A name for this operation (optional).

Returns:

A `Tensor` with the same type as the tensors in `params` and dimension of

`[ids1, ids2, d1, d2, ...]`.

Raises:

ValueError: If `params` is empty.

"""

with ops.name_scope(name, "EmbeddingLookupUnique", [params, ids]):

ids = ops.convert_to_tensor(ids)

shape = array_ops.shape(ids)

ids_flat = array_ops.reshape(

ids, math_ops.reduce_prod(shape, keep_dims=True))

unique_ids, idx = array_ops.unique(ids_flat)

unique_embeddings = embedding_ops.embedding_lookup(params, unique_ids)

embeds_flat = array_ops.gather(unique_embeddings, idx)

embed_shape = array_ops.concat(

[shape, array_ops.shape(unique_embeddings)[1:]], 0)

embeds = array_ops.reshape(embeds_flat, embed_shape)

embeds.set_shape(ids.get_shape().concatenate(

unique_embeddings.get_shape()[1:]))

return embeds

开发者ID:abhisuri97，项目名称:auto-alt-text-lambda-api，代码行数:37，

示例7: embedding_lookup_unique

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def embedding_lookup_unique(params, ids, name=None):

"""Version of embedding_lookup that avoids duplicate lookups.

This can save communication in the case of repeated ids.

Same interface as embedding_lookup. Except it supports multi-dimensional `ids`

which allows to not reshape input/output to fit gather.

Args:

params: A list of tensors with the same shape and type, or a

`PartitionedVariable`. Shape `[index, d1, d2, ...]`.

ids: A one-dimensional `Tensor` with type `int32` or `int64` containing

the ids to be looked up in `params`. Shape `[ids1, ids2, ...]`.

name: A name for this operation (optional).

Returns:

A `Tensor` with the same type as the tensors in `params` and dimension of

`[ids1, ids2, d1, d2, ...]`.

Raises:

ValueError: If `params` is empty.

"""

with ops.name_scope(name, "EmbeddingLookupUnique", [params, ids]):

ids = ops.convert_to_tensor(ids)

shape = array_ops.shape(ids)

ids_flat = array_ops.reshape(

ids, math_ops.reduce_prod(shape, keep_dims=True))

unique_ids, idx = array_ops.unique(ids_flat)

unique_embeddings = embedding_ops.embedding_lookup(params, unique_ids)

embeds_flat = array_ops.gather(unique_embeddings, idx)

embed_shape = array_ops.concat(

0, [shape, array_ops.shape(unique_embeddings)[1:]])

embeds = array_ops.reshape(embeds_flat, embed_shape)

embeds.set_shape(ids.get_shape().concatenate(

unique_embeddings.get_shape()[1:]))

return embeds

开发者ID:tobegit3hub，项目名称:deep_image_model，代码行数:37，

示例8: _add_variable_to_collections

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _add_variable_to_collections(variable, collections_set, collections_name):

"""Adds variable (or all its parts) to all collections with that name."""

collections = utils.get_variable_collections(

collections_set, collections_name) or []

variables_list = [variable]

if isinstance(variable, tf_variables.PartitionedVariable):

variables_list = [v for v in variable]

for collection in collections:

for var in variables_list:

if var not in ops.get_collection(collection):

ops.add_to_collection(collection, var)

开发者ID:balancap，项目名称:tf-imagenet，代码行数:13，

示例9: _add_variable_to_collections

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _add_variable_to_collections(variable, collections_set, collections_name):

"""Adds variable (or all its parts) to all collections with that name."""

collections = utils.get_variable_collections(collections_set,

collections_name) or []

variables_list = [variable]

if isinstance(variable, tf_variables.PartitionedVariable):

variables_list = [v for v in variable]

for collection in collections:

for var in variables_list:

if var not in ops.get_collection(collection):

ops.add_to_collection(collection, var)

开发者ID:hyperconnect，项目名称:MMNet，代码行数:13，

示例10: _rnn_get_variable

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _rnn_get_variable(self, getter, *args, **kwargs):

variable = getter(*args, **kwargs)

trainable = (variable in tf_variables.trainable_variables() or

(isinstance(variable, tf_variables.PartitionedVariable) and

list(variable)[0] in tf_variables.trainable_variables()))

if trainable and variable not in self._trainable_weights:

self._trainable_weights.append(variable)

elif not trainable and variable not in self._non_trainable_weights:

self._non_trainable_weights.append(variable)

return variable

开发者ID:Trinkle23897，项目名称:Artificial-Neural-Network-THU-2018，代码行数:12，

示例11: begin

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def begin(self):

with tf.compat.v1.variable_scope(

tf.compat.v1.get_variable_scope()) as scope:

scope.reuse_variables()

partitioned_weight = tf.compat.v1.get_variable(

self._var_name, shape=(self._var_dim, 1))

self._test_case.assertTrue(

isinstance(partitioned_weight, variables_lib.PartitionedVariable))

for part in partitioned_weight:

self._test_case.assertEqual(self._var_dim // self._partitions,

part.get_shape()[0])

开发者ID:tensorflow，项目名称:estimator，代码行数:13，

示例12: _create_slots

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _create_slots(self):

"""Make unshrunk internal variables (slots)."""

# Unshrunk variables have the updates before applying L1 regularization.

# Each unshrunk slot variable is either a `Variable` or list of

# `Variable`, depending on the value of its corresponding primary variable.

# We avoid using `PartitionedVariable` for the unshrunk slots since we do

# not need any of the extra information.

self._slots = collections.defaultdict(list)

for name in ['sparse_features_weights', 'dense_features_weights']:

for var in self._variables[name]:

# Our primary variable may be either a PartitionedVariable, or a list

# of Variables (each representing a partition).

if (isinstance(var, var_ops.PartitionedVariable) or

isinstance(var, list)):

var_list = []

for v in var:

with ops.colocate_with(v):

slot_var = tf.Variable(

initial_value=tf.compat.v1.zeros_like(v.initialized_value(),

tf.dtypes.float32),

name=v.op.name + '_unshrunk')

var_list.append(slot_var)

self._slots['unshrunk_' + name].append(var_list)

else:

with tf.compat.v1.device(var.device):

self._slots['unshrunk_' + name].append(

tf.Variable(

tf.compat.v1.zeros_like(var.initialized_value(),

tf.dtypes.float32),

name=var.op.name + '_unshrunk'))

开发者ID:tensorflow，项目名称:estimator，代码行数:32，

示例13: _var_to_list

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _var_to_list(self, var):

"""Wraps var in a list if it is not a list or PartitionedVariable."""

if not isinstance(var, (list, var_ops.PartitionedVariable)):

var = [var]

return var

开发者ID:tensorflow，项目名称:estimator，代码行数:7，

示例14: _convert_n_to_tensor

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _convert_n_to_tensor(self, input_list, as_ref=False):

"""Converts input list to a set of tensors."""

# input_list can be a list of Variables (that are implicitly partitioned),

# in which case the underlying logic in internal_convert_to_tensor will not

# concatenate the partitions together. This method takes care of the

# concatenating (we only allow partitioning on the first axis).

output_list = []

for x in input_list:

tensor_to_convert = x

if isinstance(x, list) or isinstance(x, var_ops.PartitionedVariable):

# We only allow for partitioning on the first axis.

tensor_to_convert = tf.concat(x, axis=0)

output_list.append(

internal_convert_to_tensor(tensor_to_convert, as_ref=as_ref))

return output_list

开发者ID:tensorflow，项目名称:estimator，代码行数:17，

示例15: _rnn_get_variable

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# 需要导入模块: from tensorflow.python.ops import variables [as 别名]

# 或者: from tensorflow.python.ops.variables import PartitionedVariable [as 别名]

def _rnn_get_variable(self, getter, *args, **kwargs):

variable = getter(*args, **kwargs)

if context.in_graph_mode():

trainable = (variable in tf_variables.trainable_variables() or

(isinstance(variable, tf_variables.PartitionedVariable) and

list(variable)[0] in tf_variables.trainable_variables()))

else:

trainable = variable._trainable # pylint: disable=protected-access

if trainable and variable not in self._trainable_weights:

self._trainable_weights.append(variable)

elif not trainable and variable not in self._non_trainable_weights:

self._non_trainable_weights.append(variable)

return variable

开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:15，

注：本文中的tensorflow.python.ops.variables.PartitionedVariable方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。