def random_uniform(minval, maxval, dims, name=None):
minval = ops.convert_to_tensor(minval)
return random_ops.random_uniform(dims,
minval,
maxval,
dtype=minval.dtype,
name=name)
recv = gen_xla_ops.xla_recv
reduce = gen_xla_ops.xla_reduce
variadic_reduce = gen_xla_ops.xla_variadic_reduce
ops.no_gradient("XlaVariadicReduce")
def reduce_window(operand,
init,
reducer,
window_dimensions,
window_strides=None,
base_dilations=None,
window_dilations=None,
padding=None,
name=None):
"""Wraps the XLA ReduceWindow operator.
ReduceWindow is documented at
https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
[dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64])
result = gen_ragged_math_ops.ragged_range(
starts, limits, deltas, Tsplits=row_splits_dtype, name=name)
return ragged_tensor.RaggedTensor.from_row_splits(
result.rt_dense_values, result.rt_nested_splits, validate=False)
def _infer_matching_dtype(tensors, dtype_hierarchy):
"""Infers a matching dtype for tensors, and casts them to that dtype."""
assert all(t.dtype in dtype_hierarchy for t in tensors)
inferred_dtype = max([t.dtype for t in tensors], key=dtype_hierarchy.index)
return [math_ops.cast(t, inferred_dtype) for t in tensors]
ops.no_gradient('RaggedRange')
#===============================================================================
# ragged_segment_<AGGREGATE>
#===============================================================================
# Docstring template used for the raggged_segment_<AGGREGATE> ops.
_RAGGED_SEGMENT_DOCSTRING = """\
Computes the %(combination)s along segments of a RaggedTensor.
Returns a RaggedTensor `output` with `num_segments` rows, where the row
`output[i]` is formed by taking the %(combination)s of all rows of `data`
whose corresponding `segment_id` is `i`.
The length of the row `output[i]` will be the maximum of the lengths of
all rows of `data` whose corresponding `segment_id` is `i`. If no `data`
