def reduce_join(inputs, axis=None,
keep_dims=False,
separator="",
name=None,
reduction_indices=None):
reduction_indices = _reduce_join_reduction_dims(
inputs, axis, reduction_indices)
return gen_string_ops.reduce_join(
inputs=inputs,
reduction_indices=reduction_indices,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join(inputs, axis=None,
keep_dims=False,
separator="",
name=None,
reduction_indices=None):
reduction_indices = _reduce_join_reduction_dims(
inputs, axis, reduction_indices)
return gen_string_ops.reduce_join(
inputs=inputs,
reduction_indices=reduction_indices,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring
keep_dims=False,
separator="",
name=None,
reduction_indices=None):
inputs_t = ops.convert_to_tensor(inputs)
reduction_indices = _reduce_join_reduction_dims(
inputs_t, axis, reduction_indices)
return gen_string_ops.reduce_join(
inputs=inputs_t,
reduction_indices=reduction_indices,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join_v2( # pylint: disable=missing-docstring
inputs,
axis=None,
keepdims=False,
separator="",
name=None):
with ops.name_scope(None, "ReduceJoin", [inputs, axis]):
inputs_t = ops.convert_to_tensor(inputs)
axis = _reduce_join_reduction_dims(inputs_t, axis)
return gen_string_ops.reduce_join(inputs=inputs_t,
reduction_indices=axis,
keep_dims=keepdims,
separator=separator,
name=name)
def reduce_join(inputs, axis=None,
keep_dims=False,
separator="",
name=None,
reduction_indices=None):
axis = deprecation.deprecated_argument_lookup("axis", axis,
"reduction_indices",
reduction_indices)
if axis is None:
raise ValueError("axis must be specified.")
return gen_string_ops.reduce_join(
inputs=inputs,
reduction_indices=axis,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring
keep_dims=False,
separator="",
name=None,
reduction_indices=None,
keepdims=None):
keep_dims = deprecation.deprecated_argument_lookup(
"keepdims", keepdims, "keep_dims", keep_dims)
inputs_t = ops.convert_to_tensor(inputs)
reduction_indices = _reduce_join_reduction_dims(
inputs_t, axis, reduction_indices)
return gen_string_ops.reduce_join(
inputs=inputs_t,
reduction_indices=reduction_indices,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring
keep_dims=False,
separator="",
name=None,
reduction_indices=None,
keepdims=None):
keep_dims = deprecation.deprecated_argument_lookup(
"keepdims", keepdims, "keep_dims", keep_dims)
inputs_t = ops.convert_to_tensor(inputs)
reduction_indices = _reduce_join_reduction_dims(
inputs_t, axis, reduction_indices)
return gen_string_ops.reduce_join(
inputs=inputs_t,
reduction_indices=reduction_indices,
keep_dims=keep_dims,
separator=separator,
name=name)
def reduce_join_v2( # pylint: disable=missing-docstring
inputs,
axis=None,
keepdims=False,
separator="",
name=None):
"""Joins all strings into a single string, or joins along an axis.
This is the reduction operation for the elementwise `tf.strings.join` op.
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']]).numpy()
b'abc123def456'
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']], axis=-1).numpy()
array([b'abc123', b'def456'], dtype=object)
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']],
... axis=-1,
... separator=" ").numpy()
array([b'abc 123', b'def 456'], dtype=object)
Args:
inputs: A `tf.string` tensor.
axis: Which axis to join along. The default behavior is to join all
elements, producing a scalar.
keepdims: If true, retains reduced dimensions with length 1.
separator: a string added between each string being joined.
name: A name for the operation (optional).
Returns:
A `tf.string` tensor.
"""
with ops.name_scope(None, "ReduceJoin", [inputs, axis]):
inputs_t = ops.convert_to_tensor(inputs)
axis = _reduce_join_reduction_dims(inputs_t, axis)
return gen_string_ops.reduce_join(inputs=inputs_t,
reduction_indices=axis,
keep_dims=keepdims,
separator=separator,
name=name)
