本文整理汇总了Python中tensorflow.python.ops.data_flow_ops.dynamic_partition方法的典型用法代码示例。如果您正苦于以下问题：Python data_flow_ops.dynamic_partition方法的具体用法？Python data_flow_ops.dynamic_partition怎么用？Python data_flow_ops.dynamic_partition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类tensorflow.python.ops.data_flow_ops的用法示例。
在下文中一共展示了data_flow_ops.dynamic_partition方法的7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: scatter_update
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def scatter_update(cls, factor, indices, values, sharding_func, name=None):
"""Helper function for doing sharded scatter update."""
assert isinstance(factor, list)
if len(factor) == 1:
with ops.colocate_with(factor[0]):
# TODO(agarwal): assign instead of scatter update for full batch update.
return state_ops.scatter_update(factor[0], indices, values,
name=name).op
else:
num_shards = len(factor)
assignments, new_ids = sharding_func(indices)
assert assignments is not None
assignments = math_ops.cast(assignments, dtypes.int32)
sharded_ids = data_flow_ops.dynamic_partition(new_ids, assignments,
num_shards)
sharded_values = data_flow_ops.dynamic_partition(values, assignments,
num_shards)
updates = []
for i in xrange(num_shards):
updates.append(state_ops.scatter_update(factor[i], sharded_ids[i],
sharded_values[i]))
return control_flow_ops.group(*updates, name=name) 示例2: insert
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def insert(self, keys, values, name=None):
self._check_keys(keys)
num_shards = self._num_shards
if num_shards == 1:
return self._table_shards[0].insert(keys, values, name=name)
shard_indices = self._shard_indices(keys)
# TODO(andreasst): support 'keys' that are not vectors
key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
num_shards)
value_shards = data_flow_ops.dynamic_partition(values, shard_indices,
num_shards)
return_values = [
self._table_shards[i].insert(key_shards[i], value_shards[i], name=name)
for i in range(num_shards)
]
return control_flow_ops.group(*return_values) 示例3: scatter_update
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def scatter_update(cls, factor, indices, values, sharding_func):
"""Helper function for doing sharded scatter update."""
assert isinstance(factor, list)
if len(factor) == 1:
with ops.colocate_with(factor[0]):
# TODO(agarwal): assign instead of scatter update for full batch update.
return state_ops.scatter_update(factor[0], indices, values).op
else:
num_shards = len(factor)
assignments, new_ids = sharding_func(indices)
assert assignments is not None
assignments = math_ops.cast(assignments, dtypes.int32)
sharded_ids = data_flow_ops.dynamic_partition(new_ids, assignments,
num_shards)
sharded_values = data_flow_ops.dynamic_partition(values, assignments,
num_shards)
updates = []
for i in xrange(num_shards):
updates.append(
state_ops.scatter_update(factor[i], sharded_ids[i], sharded_values[
i]))
return control_flow_ops.group(*updates) 示例4: _DynamicPartitionGrads
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def _DynamicPartitionGrads(op, *grads):
"""Gradients for DynamicPartition."""
data = op.inputs[0]
indices = op.inputs[1]
num_partitions = op.get_attr("num_partitions")
prefix_shape = array_ops.shape(indices)
original_indices = array_ops.reshape(
math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape)
partitioned_indices = data_flow_ops.dynamic_partition(
original_indices, indices, num_partitions)
reconstructed = data_flow_ops.dynamic_stitch(partitioned_indices, grads)
reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data))
return [reconstructed, None] 示例5: _select_which_to_enqueue
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def _select_which_to_enqueue(tensor_list, keep_input):
"""Select which examples to enqueue based on vector `keep_input`."""
select_i = math_ops.cast(keep_input, dtypes.int32)
tensor_list = [
data_flow_ops.dynamic_partition(x, select_i, num_partitions=2)[1]
for x in tensor_list]
return tensor_list 示例6: lookup
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def lookup(self, keys, name=None):
if keys.dtype != self._key_dtype:
raise TypeError('Signature mismatch. Keys must be dtype %s, got %s.' %
(self._key_dtype, keys.dtype))
self._check_keys(keys)
num_shards = self._num_shards
if num_shards == 1:
return self._table_shards[0].lookup(keys, name=name)
shard_indices = self._shard_indices(keys)
# TODO(andreasst): support 'keys' that are not vectors
key_shards = data_flow_ops.dynamic_partition(keys, shard_indices,
num_shards)
value_shards = [
self._table_shards[i].lookup(key_shards[i], name=name)
for i in range(num_shards)
]
num_keys = keys.get_shape().dims[0]
original_indices = math_ops.range(num_keys)
partitioned_indices = data_flow_ops.dynamic_partition(original_indices,
shard_indices,
num_shards)
result = data_flow_ops.dynamic_stitch(partitioned_indices, value_shards)
result.set_shape(
tensor_shape.TensorShape([num_keys]).concatenate(self._value_shape))
return result 示例7: _select_which_to_enqueue
# 需要导入模块: from tensorflow.python.ops import data_flow_ops [as 别名]
# 或者: from tensorflow.python.ops.data_flow_ops import dynamic_partition [as 别名]
def _select_which_to_enqueue(tensor_list, keep_input):
"""Select which examples to enqueue based on vector `keep_input`."""
select_i = math_ops.to_int32(keep_input)
tensor_list = [
data_flow_ops.dynamic_partition(x, select_i, num_partitions=2)[1]
for x in tensor_list]
return tensor_list 开发者ID:PacktPublishing，项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda，代码行数:9，代码来源:input.py