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Rolling forward change by fixing issue with lack of dispatch for & and |

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PiperOrigin-RevId: 345178161
Change-Id: I6a9218edbb764e9daace94141a85082d06a3ffcd
This commit is contained in:
Rohan Jain 2020-12-02 00:05:16 -08:00 committed by TensorFlower Gardener
parent 2ba6502de5
commit 1e01276add
8 changed files with 460 additions and 209 deletions

2
tensorflow/core/api_def/python_api/api_def_Add.pbtxt
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@ -63,5 +63,7 @@ Another example with two arrays of different dimension.
>>> tf.add(x, y).shape.as_list()
[1, 2, 3, 4]
The reduction version of this elementwise operation is `tf.math.reduce_sum`
END
}

64
tensorflow/core/api_def/python_api/api_def_LogicalAnd.pbtxt
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@ -1,4 +1,66 @@
op {
graph_op_name: "LogicalAnd"
visibility: HIDDEN
endpoint {
name: "math.logical_and"
}
endpoint {
name: "logical_and"
}
description: <<END
Logical AND function.
Requires that `x` and `y` have the same shape or have
[broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
shapes. For example, `x` and `y` can be:
- Two single elements of type `bool`.
- One `tf.Tensor` of type `bool` and one single `bool`, where the result will
be calculated by applying logical AND with the single element to each
element in the larger Tensor.
- Two `tf.Tensor` objects of type `bool` of the same shape. In this case,
the result will be the element-wise logical AND of the two input tensors.
You can also use the `&` operator instead.
Usage:
>>> a = tf.constant([True])
>>> b = tf.constant([False])
>>> tf.math.logical_and(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
>>> a & b
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
>>> c = tf.constant([True])
>>> x = tf.constant([False, True, True, False])
>>> tf.math.logical_and(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
>>> c & x
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
>>> y = tf.constant([False, False, True, True])
>>> z = tf.constant([False, True, False, True])
>>> tf.math.logical_and(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>
>>> y & z
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>
This op also supports broadcasting
>>> tf.logical_and([[True, False]], [[True], [False]])
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[ True, False],
[False, False]])>
The reduction version of this elementwise operation is `tf.math.reduce_all`.
Args:
x: A `tf.Tensor` of type bool.
y: A `tf.Tensor` of type bool.
name: A name for the operation (optional).
Returns:
A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.
END
}

57
tensorflow/core/api_def/python_api/api_def_LogicalOr.pbtxt
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@ -6,4 +6,61 @@ op {
endpoint {
name: "logical_or"
}
description: <<END
Logical OR function.
Requires that `x` and `y` have the same shape or have
[broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
shapes. For example, `x` and `y` can be:
- Two single elements of type `bool`.
- One `tf.Tensor` of type `bool` and one single `bool`, where the result will
be calculated by applying logical OR with the single element to each
element in the larger Tensor.
- Two `tf.Tensor` objects of type `bool` of the same shape. In this case,
the result will be the element-wise logical OR of the two input tensors.
You can also use the `|` operator instead.
Usage:
>>> a = tf.constant([True])
>>> b = tf.constant([False])
>>> tf.math.logical_or(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
>>> a | b
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
>>> c = tf.constant([False])
>>> x = tf.constant([False, True, True, False])
>>> tf.math.logical_or(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
>>> c | x
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
>>> y = tf.constant([False, False, True, True])
>>> z = tf.constant([False, True, False, True])
>>> tf.math.logical_or(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>
>>> y | z
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>
This op also supports broadcasting
>>> tf.logical_or([[True, False]], [[True], [False]])
<tf.Tensor: shape=(2, 2), dtype=bool, numpy=
array([[ True, True],
[ True, False]])>
The reduction version of this elementwise operation is `tf.math.reduce_any`.
Args:
x: A `tf.Tensor` of type bool.
y: A `tf.Tensor` of type bool.
name: A name for the operation (optional).
Returns:
A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.
END
}

9
tensorflow/core/api_def/python_api/api_def_Maximum.pbtxt
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@ -14,5 +14,14 @@ Example:
>>> tf.math.maximum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 0., 2., 5.], dtype=float32)>
Note that `maximum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.
>>> x = tf.constant([-5., 0., 0., 0.])
>>> y = tf.constant([-3.])
>>> tf.math.maximum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-3., 0., 0., 0.], dtype=float32)>
The reduction version of this elementwise operation is `tf.math.reduce_max`
END
}

9
tensorflow/core/api_def/python_api/api_def_Minimum.pbtxt
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@ -17,19 +17,14 @@ Examples:
>>> tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>
Note that `minimum` supports broadcast semantics.
Note that `minimum` supports [broadcast semantics](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) for `x` and `y`.
>>> x = tf.constant([-5., 0., 0., 0.])
>>> y = tf.constant([-3.])
>>> tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>
If inputs are not tensors, they will be converted to tensors. See
`tf.convert_to_tensor`.
>>> x = tf.constant([-3.], dtype=tf.float32)
>>> tf.math.minimum([-5], x)
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-5.], dtype=float32)>
The reduction version of this elementwise operation is `tf.math.reduce_min`
END
}

337
tensorflow/python/ops/math_ops.py
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@ -500,6 +500,8 @@ def multiply(x, y, name=None):
array([[1., 1.],
[1., 1.]], dtype=float32)>
The reduction version of this elementwise operation is `tf.math.reduce_prod`
Args:
x: A Tensor. Must be one of the following types: `bfloat16`,
`half`, `float32`, `float64`, `uint8`, `int8`, `uint16`,
@ -1506,7 +1508,9 @@ def logical_xor(x, y, name="LogicalXor"):
x ^ y = (x | y) & ~(x & y)
The operation works for the following input types:
Requires that `x` and `y` have the same shape or have
[broadcast-compatible](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
shapes. For example, `x` and `y` can be:
- Two single elements of type `bool`
- One `tf.Tensor` of type `bool` and one single `bool`, where the result will
@ -1547,48 +1551,6 @@ def logical_xor(x, y, name="LogicalXor"):
name=name)
@tf_export("math.logical_and", "logical_and")
@dispatch.add_dispatch_support
def logical_and(x, y, name=None):
"""Logical AND function.
The operation works for the following input types:
- Two single elements of type `bool`
- One `tf.Tensor` of type `bool` and one single `bool`, where the result will
be calculated by applying logical AND with the single element to each
element in the larger Tensor.
- Two `tf.Tensor` objects of type `bool` of the same shape. In this case,
the result will be the element-wise logical AND of the two input tensors.
Usage:
>>> a = tf.constant([True])
>>> b = tf.constant([False])
>>> tf.math.logical_and(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([False])>
>>> c = tf.constant([True])
>>> x = tf.constant([False, True, True, False])
>>> tf.math.logical_and(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
>>> y = tf.constant([False, False, True, True])
>>> z = tf.constant([False, True, False, True])
>>> tf.math.logical_and(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, False, False, True])>
Args:
x: A `tf.Tensor` type bool.
y: A `tf.Tensor` of type bool.
name: A name for the operation (optional).
Returns:
A `tf.Tensor` of type bool with the same size as that of x or y.
"""
return gen_math_ops.logical_and(x, y, name)
def and_(x, y, name=None):
if x.dtype == dtypes.bool:
return gen_math_ops.logical_and(x, y, name)
@ -1915,6 +1877,8 @@ def reduce_sum_v1(input_tensor,
keep_dims=None):
"""Computes the sum of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.add` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
of the entries in `axis`, which must be unique. If `keepdims` is true, the
@ -1925,14 +1889,34 @@ def reduce_sum_v1(input_tensor,
For example:
```python
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.reduce_sum(x) # 6
tf.reduce_sum(x, 0) # [2, 2, 2]
tf.reduce_sum(x, 1) # [3, 3]
tf.reduce_sum(x, 1, keepdims=True) # [[3], [3]]
tf.reduce_sum(x, [0, 1]) # 6
```
>>> # x has a shape of (2, 3) (two rows and three columns):
>>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
>>> x.numpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
>>> # sum all the elements
>>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
>>> tf.reduce_sum(x).numpy()
6
>>> # reduce along the first dimension
>>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
>>> # reduce along the second dimension
>>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
>>> tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
>>> # keep the original dimensions
>>> tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
[3]], dtype=int32)
>>> # reduce along both dimensions
>>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
>>> # or, equivalently, reduce along rows, then reduce the resultant array
>>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> # 2 + 2 + 2 = 6
>>> tf.reduce_sum(x, [0, 1]).numpy()
6
Args:
input_tensor: The tensor to reduce. Should have numeric type.
@ -1965,6 +1949,8 @@ def reduce_sum_v1(input_tensor,
def reduce_sum(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the sum of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.add` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
of the entries in `axis`, which must be unique. If `keepdims` is true, the
@ -1975,35 +1961,34 @@ def reduce_sum(input_tensor, axis=None, keepdims=False, name=None):
For example:
>>> # x has a shape of (2, 3) (two rows and three columns):
>>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
>>> x.numpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
>>> # sum all the elements
>>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
>>> tf.reduce_sum(x).numpy()
6
>>> # reduce along the first dimension
>>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
>>> # reduce along the second dimension
>>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
>>> tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
>>> # keep the original dimensions
>>> tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
[3]], dtype=int32)
>>> # reduce along both dimensions
>>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
>>> # or, equivalently, reduce along rows, then reduce the resultant array
>>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> # 2 + 2 + 2 = 6
>>> tf.reduce_sum(x, [0, 1]).numpy()
6
>>> # x has a shape of (2, 3) (two rows and three columns):
>>> x = tf.constant([[1, 1, 1], [1, 1, 1]])
>>> x.numpy()
array([[1, 1, 1],
[1, 1, 1]], dtype=int32)
>>> # sum all the elements
>>> # 1 + 1 + 1 + 1 + 1+ 1 = 6
>>> tf.reduce_sum(x).numpy()
6
>>> # reduce along the first dimension
>>> # the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
>>> # reduce along the second dimension
>>> # the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
>>> tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
>>> # keep the original dimensions
>>> tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
[3]], dtype=int32)
>>> # reduce along both dimensions
>>> # the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
>>> # or, equivalently, reduce along rows, then reduce the resultant array
>>> # [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
>>> # 2 + 2 + 2 = 6
>>> tf.reduce_sum(x, [0, 1]).numpy()
6
Args:
input_tensor: The tensor to reduce. Should have numeric type.
@ -2468,7 +2453,9 @@ def reduce_std(input_tensor, axis=None, keepdims=False, name=None):
@tf_export("math.reduce_prod", "reduce_prod", v1=[])
@dispatch.add_dispatch_support
def reduce_prod(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the product of elements across dimensions of a tensor.
"""Computes `tf.math.multiply` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.multiply` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2478,6 +2465,17 @@ def reduce_prod(input_tensor, axis=None, keepdims=False, name=None):
If `axis` is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
>>> x = tf.constant([[1., 2.], [3., 4.]])
>>> tf.math.reduce_prod(x)
<tf.Tensor: shape=(), dtype=float32, numpy=24.>
>>> tf.math.reduce_prod(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([3., 8.], dtype=float32)>
>>> tf.math.reduce_prod(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2., 12.],
dtype=float32)>
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default), reduces all
@ -2512,7 +2510,9 @@ def reduce_prod_v1(input_tensor,
name=None,
reduction_indices=None,
keep_dims=None):
"""Computes the product of elements across dimensions of a tensor.
"""Computes `tf.math.multiply` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.multiply` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2522,6 +2522,17 @@ def reduce_prod_v1(input_tensor,
If `axis` is None, all dimensions are reduced, and a
tensor with a single element is returned.
For example:
>>> x = tf.constant([[1., 2.], [3., 4.]])
>>> tf.math.reduce_prod(x)
<tf.Tensor: shape=(), dtype=float32, numpy=24.>
>>> tf.math.reduce_prod(x, 0)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([3., 8.], dtype=float32)>
>>> tf.math.reduce_prod(x, 1)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2., 12.],
dtype=float32)>
Args:
input_tensor: The tensor to reduce. Should have numeric type.
axis: The dimensions to reduce. If `None` (the default), reduces all
@ -2558,7 +2569,9 @@ def reduce_min_v1(input_tensor,
name=None,
reduction_indices=None,
keep_dims=None):
"""Computes the minimum of elements across dimensions of a tensor.
"""Computes the `tf.math.minimum` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.minimum` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2568,6 +2581,26 @@ def reduce_min_v1(input_tensor,
If `axis` is None, all dimensions are reduced, and a
tensor with a single element is returned.
Usage example:
>>> x = tf.constant([5, 1, 2, 4])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=int32, numpy=1>
>>> x = tf.constant([-5, -1, -2, -4])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=int32, numpy=-5>
>>> x = tf.constant([4, float('nan')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('nan'), float('nan')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('-inf'), float('inf')])
>>> tf.reduce_min(x)
<tf.Tensor: shape=(), dtype=float32, numpy=-inf>
See the numpy docs for `np.amin` and `np.nanmin` behavior.
Args:
input_tensor: The tensor to reduce. Should have real numeric type.
axis: The dimensions to reduce. If `None` (the default), reduces all
@ -2580,10 +2613,6 @@ def reduce_min_v1(input_tensor,
Returns:
The reduced tensor.
@compatibility(numpy)
Equivalent to np.min
@end_compatibility
"""
axis = deprecation.deprecated_argument_lookup("axis", axis,
"reduction_indices",
@ -2596,7 +2625,9 @@ def reduce_min_v1(input_tensor,
@tf_export("math.reduce_min", "reduce_min", v1=[])
@dispatch.add_dispatch_support
def reduce_min(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the minimum of elements across dimensions of a tensor.
"""Computes the `tf.math.minimum` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.minimum` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2666,7 +2697,9 @@ def reduce_max_v1(input_tensor,
name=None,
reduction_indices=None,
keep_dims=None):
"""Computes the maximum of elements across dimensions of a tensor.
"""Computes `tf.math.maximum` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.maximum` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2676,6 +2709,26 @@ def reduce_max_v1(input_tensor,
If `axis` is None, all dimensions are reduced, and a
tensor with a single element is returned.
Usage example:
>>> x = tf.constant([5, 1, 2, 4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=5>
>>> x = tf.constant([-5, -1, -2, -4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=-1>
>>> x = tf.constant([4, float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('nan'), float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('-inf'), float('inf')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=inf>
See the numpy docs for `np.amax` and `np.nanmax` behavior.
Args:
input_tensor: The tensor to reduce. Should have real numeric type.
axis: The dimensions to reduce. If `None` (the default), reduces all
@ -2688,10 +2741,6 @@ def reduce_max_v1(input_tensor,
Returns:
The reduced tensor.
@compatibility(numpy)
Equivalent to np.max
@end_compatibility
"""
axis = deprecation.deprecated_argument_lookup("axis", axis,
"reduction_indices",
@ -2704,7 +2753,9 @@ def reduce_max_v1(input_tensor,
@tf_export("math.reduce_max", "reduce_max", v1=[])
@dispatch.add_dispatch_support
def reduce_max(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the maximum of elements across dimensions of a tensor.
"""Computes `tf.math.maximum` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.maximum` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2716,21 +2767,21 @@ def reduce_max(input_tensor, axis=None, keepdims=False, name=None):
Usage example:
>>> x = tf.constant([5, 1, 2, 4])
>>> print(tf.reduce_max(x))
tf.Tensor(5, shape=(), dtype=int32)
>>> x = tf.constant([-5, -1, -2, -4])
>>> print(tf.reduce_max(x))
tf.Tensor(-1, shape=(), dtype=int32)
>>> x = tf.constant([4, float('nan')])
>>> print(tf.reduce_max(x))
tf.Tensor(nan, shape=(), dtype=float32)
>>> x = tf.constant([float('nan'), float('nan')])
>>> print(tf.reduce_max(x))
tf.Tensor(nan, shape=(), dtype=float32)
>>> x = tf.constant([float('-inf'), float('inf')])
>>> print(tf.reduce_max(x))
tf.Tensor(inf, shape=(), dtype=float32)
>>> x = tf.constant([5, 1, 2, 4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=5>
>>> x = tf.constant([-5, -1, -2, -4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=-1>
>>> x = tf.constant([4, float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('nan'), float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('-inf'), float('inf')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=inf>
See the numpy docs for `np.amax` and `np.nanmax` behavior.
@ -2771,7 +2822,9 @@ def reduce_all_v1(input_tensor,
name=None,
reduction_indices=None,
keep_dims=None):
"""Computes the "logical and" of elements across dimensions of a tensor.
"""Computes `tf.math.logical_and` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.logical_and` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2783,12 +2836,13 @@ def reduce_all_v1(input_tensor,
For example:
```python
x = tf.constant([[True, True], [False, False]])
tf.reduce_all(x) # False
tf.reduce_all(x, 0) # [False, False]
tf.reduce_all(x, 1) # [True, False]
```
>>> x = tf.constant([[True, True], [False, False]])
>>> tf.math.reduce_all(x)
<tf.Tensor: shape=(), dtype=bool, numpy=False>
>>> tf.math.reduce_all(x, 0)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])>
>>> tf.math.reduce_all(x, 1)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
Args:
input_tensor: The boolean tensor to reduce.
@ -2818,7 +2872,9 @@ def reduce_all_v1(input_tensor,
@tf_export("math.reduce_all", "reduce_all", v1=[])
@dispatch.add_dispatch_support
def reduce_all(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the "logical and" of elements across dimensions of a tensor.
"""Computes `tf.math.logical_and` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.logical_and` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2830,12 +2886,13 @@ def reduce_all(input_tensor, axis=None, keepdims=False, name=None):
For example:
```python
x = tf.constant([[True, True], [False, False]])
tf.reduce_all(x) # False
tf.reduce_all(x, 0) # [False, False]
tf.reduce_all(x, 1) # [True, False]
```
>>> x = tf.constant([[True, True], [False, False]])
>>> tf.math.reduce_all(x)
<tf.Tensor: shape=(), dtype=bool, numpy=False>
>>> tf.math.reduce_all(x, 0)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])>
>>> tf.math.reduce_all(x, 1)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
Args:
input_tensor: The boolean tensor to reduce.
@ -2871,7 +2928,9 @@ def reduce_any_v1(input_tensor,
name=None,
reduction_indices=None,
keep_dims=None):
"""Computes the "logical or" of elements across dimensions of a tensor.
"""Computes `tf.math.logical_or` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.logical_or` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2883,12 +2942,13 @@ def reduce_any_v1(input_tensor,
For example:
```python
x = tf.constant([[True, True], [False, False]])
tf.reduce_any(x) # True
tf.reduce_any(x, 0) # [True, True]
tf.reduce_any(x, 1) # [True, False]
```
>>> x = tf.constant([[True, True], [False, False]])
>>> tf.reduce_any(x)
<tf.Tensor: shape=(), dtype=bool, numpy=True>
>>> tf.reduce_any(x, 0)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, True])>
>>> tf.reduce_any(x, 1)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
Args:
input_tensor: The boolean tensor to reduce.
@ -2918,7 +2978,9 @@ def reduce_any_v1(input_tensor,
@tf_export("math.reduce_any", "reduce_any", v1=[])
@dispatch.add_dispatch_support
def reduce_any(input_tensor, axis=None, keepdims=False, name=None):
"""Computes the "logical or" of elements across dimensions of a tensor.
"""Computes `tf.math.logical_or` of elements across dimensions of a tensor.
This is the reduction operation for the elementwise `tf.math.logical_or` op.
Reduces `input_tensor` along the dimensions given in `axis`.
Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
@ -2930,12 +2992,13 @@ def reduce_any(input_tensor, axis=None, keepdims=False, name=None):
For example:
```python
x = tf.constant([[True, True], [False, False]])
tf.reduce_any(x) # True
tf.reduce_any(x, 0) # [True, True]
tf.reduce_any(x, 1) # [True, False]
```
>>> x = tf.constant([[True, True], [False, False]])
>>> tf.reduce_any(x)
<tf.Tensor: shape=(), dtype=bool, numpy=True>
>>> tf.reduce_any(x, 0)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, True])>
>>> tf.reduce_any(x, 1)
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, False])>
Args:
input_tensor: The boolean tensor to reduce.

186
tensorflow/python/ops/sparse_ops.py
View File

@ -1215,7 +1215,9 @@ def sparse_to_dense(sparse_indices,
@tf_export("sparse.reduce_max", v1=[])
def sparse_reduce_max_v2(
sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None):
"""Computes the max of elements across dimensions of a SparseTensor.
"""Computes `tf.sparse.maximum` of elements across dimensions of a SparseTensor.
This is the reduction operation for the elementwise `tf.sparse.maximum` op.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_max()`. In particular, this Op also returns a dense `Tensor`
@ -1241,21 +1243,32 @@ def sparse_reduce_max_v2(
For example:
```python
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_max(x) ==> 3
tf.sparse.reduce_max(x, 0) ==> [1, 3, 2]
tf.sparse.reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis.
tf.sparse.reduce_max(x, 1, keepdims=True) ==> [[2], [3]]
tf.sparse.reduce_max(x, [0, 1]) ==> 3
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
# 'y' represents [[-7, ?]
# [ 4, 3]
# [ ?, ?]
tf.sparse.reduce_max(x, 1) ==> [-7, 4, 0]
```
>>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3])
>>> tf.sparse.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
>>> tf.sparse.reduce_max(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 3, 2], dtype=int32)>
>>> tf.sparse.reduce_max(x, 1)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 3], dtype=int32)>
>>> tf.sparse.reduce_max(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
[3]], dtype=int32)>
>>> tf.sparse.reduce_max(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>
# 'y' represents [[-7, ?]
# [ 4, 3]
# [ ?, ?]
>>> y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3],
... [3, 2])
>>> tf.sparse.reduce_max(y, 1)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([-7, 4, 0], dtype=int32)>
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
@ -1303,7 +1316,9 @@ def sparse_reduce_max_v2(
"reduction_axes")
def sparse_reduce_max(sp_input, axis=None, keepdims=None,
reduction_axes=None, keep_dims=None):
"""Computes the max of elements across dimensions of a SparseTensor.
"""Computes `tf.sparse.maximum` of elements across dimensions of a SparseTensor.
This is the reduction operation for the elementwise `tf.sparse.maximum` op.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_max()`. In particular, this Op also returns a dense `Tensor`
@ -1328,21 +1343,32 @@ def sparse_reduce_max(sp_input, axis=None, keepdims=None,
For example:
```python
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_max(x) ==> 3
tf.sparse.reduce_max(x, 0) ==> [1, 3, 2]
tf.sparse.reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis.
tf.sparse.reduce_max(x, 1, keepdims=True) ==> [[2], [3]]
tf.sparse.reduce_max(x, [0, 1]) ==> 3
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
# 'y' represents [[-7, ?]
# [ 4, 3]
# [ ?, ?]
tf.sparse.reduce_max(x, 1) ==> [-7, 4, 0]
```
>>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3])
>>> tf.sparse.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
>>> tf.sparse.reduce_max(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 3, 2], dtype=int32)>
>>> tf.sparse.reduce_max(x, 1)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 3], dtype=int32)>
>>> tf.sparse.reduce_max(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
[3]], dtype=int32)>
>>> tf.sparse.reduce_max(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>
# 'y' represents [[-7, ?]
# [ 4, 3]
# [ ?, ?]
>>> y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3],
... [3, 2])
>>> tf.sparse.reduce_max(y, 1)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([-7, 4, 0], dtype=int32)>
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
@ -1423,7 +1449,9 @@ def sparse_reduce_max_sparse(sp_input,
@tf_export("sparse.reduce_sum", v1=[])
def sparse_reduce_sum_v2(
sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None):
"""Computes the sum of elements across dimensions of a SparseTensor.
"""Computes `tf.sparse.add` of elements across dimensions of a SparseTensor.
This is the reduction operation for the elementwise `tf.sparse.add` op.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
@ -1443,16 +1471,23 @@ def sparse_reduce_sum_v2(
For example:
```python
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_sum(x) ==> 3
tf.sparse.reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse.reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis.
tf.sparse.reduce_sum(x, 1, keepdims=True) ==> [[2], [1]]
tf.sparse.reduce_sum(x, [0, 1]) ==> 3
```
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
>>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3])
>>> tf.sparse.reduce_sum(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
>>> tf.sparse.reduce_sum(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)>
>>> tf.sparse.reduce_sum(x, 1) # Can also use -1 as the axis
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>
>>> tf.sparse.reduce_sum(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
[1]], dtype=int32)>
>>> tf.sparse.reduce_sum(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
@ -1499,7 +1534,9 @@ def sparse_reduce_sum_v2(
"reduction_axes")
def sparse_reduce_sum(sp_input, axis=None, keepdims=None,
reduction_axes=None, keep_dims=None):
"""Computes the sum of elements across dimensions of a SparseTensor.
"""Computes `tf.sparse.add` of elements across dimensions of a SparseTensor.
This is the reduction operation for the elementwise `tf.sparse.add` op.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
@ -1516,16 +1553,23 @@ def sparse_reduce_sum(sp_input, axis=None, keepdims=None,
For example:
```python
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_sum(x) ==> 3
tf.sparse.reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse.reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis.
tf.sparse.reduce_sum(x, 1, keepdims=True) ==> [[2], [1]]
tf.sparse.reduce_sum(x, [0, 1]) ==> 3
```
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
>>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 1, 1], [2, 3])
>>> tf.sparse.reduce_sum(x)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
>>> tf.sparse.reduce_sum(x, 0)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 1, 1], dtype=int32)>
>>> tf.sparse.reduce_sum(x, 1) # Can also use -1 as the axis
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 1], dtype=int32)>
>>> tf.sparse.reduce_sum(x, 1, keepdims=True)
<tf.Tensor: shape=(2, 1), dtype=int32, numpy=
array([[2],
[1]], dtype=int32)>
>>> tf.sparse.reduce_sum(x, [0, 1])
<tf.Tensor: shape=(), dtype=int32, numpy=3>
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
@ -2651,14 +2695,22 @@ def sparse_maximum(sp_a, sp_b, name=None):
"""Returns the element-wise max of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse.maximum(sp_zero, sp_one).eval()
# "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]).
```
>>> sp_zero = tf.sparse.SparseTensor([[0]], [0], [7])
>>> sp_one = tf.sparse.SparseTensor([[1]], [1], [7])
>>> res = tf.sparse.maximum(sp_zero, sp_one)
>>> res.indices
<tf.Tensor: shape=(2, 1), dtype=int64, numpy=
array([[0],
[1]])>
>>> res.values
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 1], dtype=int32)>
>>> res.dense_shape
<tf.Tensor: shape=(1,), dtype=int64, numpy=array([7])>
The reduction version of this elementwise operation is `tf.sparse.reduce_max`
Args:
sp_a: a `SparseTensor` operand whose dtype is real, and indices
@ -2689,14 +2741,20 @@ def sparse_minimum(sp_a, sp_b, name=None):
"""Returns the element-wise min of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse.minimum(sp_zero, sp_one).eval()
# "res" should be equal to SparseTensor([[0], [1]], [0, 0], [7]).
```
>>> sp_zero = tf.sparse.SparseTensor([[0]], [0], [7])
>>> sp_one = tf.sparse.SparseTensor([[1]], [1], [7])
>>> res = tf.sparse.minimum(sp_zero, sp_one)
>>> res.indices
<tf.Tensor: shape=(2, 1), dtype=int64, numpy=
array([[0],
[1]])>
>>> res.values
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([0, 0], dtype=int32)>
>>> res.dense_shape
<tf.Tensor: shape=(1,), dtype=int64, numpy=array([7])>
Args:
sp_a: a `SparseTensor` operand whose dtype is real, and indices

5
tensorflow/python/ops/string_ops.py
View File

@ -338,6 +338,8 @@ def reduce_join_v2( # pylint: disable=missing-docstring
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'
@ -559,6 +561,9 @@ def string_join(inputs, separator="", name=None):
... separator=" ").numpy()
array([b'abc def', b'123 456'], dtype=object)
The reduction version of this elementwise operation is
`tf.strings.reduce_join`
Args:
inputs: A list of `tf.Tensor` objects of same size and `tf.string` dtype.
separator: A string added between each string being joined.