本文整理汇总了Python中tensorflow.python.ops.data_flow_ops.dynamic_stitch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_stitch方法的具体用法？Python data_flow_ops.dynamic_stitch怎么用？Python data_flow_ops.dynamic_stitch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_stitch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: _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_stitch [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2: 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_stitch [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