/
math_ops.py
2540 lines (2067 loc) · 82.1 KB
/
math_ops.py
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic arithmetic operators.
See the @{$python/math_ops} guide.
@@add
@@subtract
@@multiply
@@scalar_mul
@@div
@@divide
@@truediv
@@floordiv
@@realdiv
@@truncatediv
@@floor_div
@@truncatemod
@@floormod
@@mod
@@cross
@@add_n
@@abs
@@negative
@@sign
@@reciprocal
@@square
@@round
@@sqrt
@@rsqrt
@@pow
@@exp
@@expm1
@@log
@@log1p
@@sinh
@@cosh
@@asinh
@@acosh
@@atanh
@@ceil
@@floor
@@maximum
@@minimum
@@cos
@@sin
@@lbeta
@@tan
@@acos
@@asin
@@atan
@@atan2
@@lgamma
@@digamma
@@erf
@@erfc
@@squared_difference
@@igamma
@@igammac
@@zeta
@@polygamma
@@betainc
@@rint
@@diag
@@diag_part
@@trace
@@transpose
@@eye
@@matrix_diag
@@matrix_diag_part
@@matrix_band_part
@@matrix_set_diag
@@matrix_transpose
@@matmul
@@norm
@@matrix_determinant
@@matrix_inverse
@@cholesky
@@cholesky_solve
@@matrix_solve
@@matrix_triangular_solve
@@matrix_solve_ls
@@qr
@@self_adjoint_eig
@@self_adjoint_eigvals
@@svd
@@tensordot
@@complex
@@conj
@@imag
@@angle
@@real
@@fft
@@ifft
@@fft2d
@@ifft2d
@@fft3d
@@ifft3d
@@reduce_sum
@@reduce_prod
@@reduce_min
@@reduce_max
@@reduce_mean
@@reduce_all
@@reduce_any
@@reduce_logsumexp
@@count_nonzero
@@accumulate_n
@@einsum
@@bincount
@@cumsum
@@cumprod
@@segment_sum
@@segment_prod
@@segment_min
@@segment_max
@@segment_mean
@@unsorted_segment_sum
@@unsorted_segment_max
@@sparse_segment_sum
@@sparse_segment_mean
@@sparse_segment_sqrt_n
@@argmin
@@argmax
@@setdiff1d
@@where
@@unique
@@edit_distance
@@invert_permutation
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.eager import context
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_control_flow_ops
from tensorflow.python.ops import gen_data_flow_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import gen_sparse_ops
from tensorflow.python.ops import gen_spectral_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import state_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_math_ops import *
# pylint: enable=wildcard-import
from tensorflow.python.util import compat
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.deprecation import deprecated_args
# Aliases for some automatically-generated names.
linspace = gen_math_ops.lin_space
arg_max = deprecated(None, "Use `argmax` instead")(arg_max) # pylint: disable=used-before-assignment
arg_min = deprecated(None, "Use `argmin` instead")(arg_min) # pylint: disable=used-before-assignment
def _set_doc(doc):
def _decorator(func):
func.__doc__ = doc
return func
return _decorator
# pylint: disable=redefined-builtin
@deprecated_args(None, "Use the `axis` argument instead", "dimension")
@_set_doc(gen_math_ops.arg_max.__doc__
.replace("dimensions", "axes")
.replace("dimension", "axis"))
def argmax(input,
axis=None,
name=None,
dimension=None,
output_type=dtypes.int64):
if dimension is not None:
if axis is not None:
raise ValueError("Cannot specify both 'axis' and 'dimension'")
axis = dimension
elif axis is None:
axis = 0
return gen_math_ops.arg_max(input, axis, name=name, output_type=output_type)
@deprecated_args(None, "Use the `axis` argument instead", "dimension")
@_set_doc(gen_math_ops.arg_min.__doc__
.replace("dimensions", "axes")
.replace("dimension", "axis"))
def argmin(input,
axis=None,
name=None,
dimension=None,
output_type=dtypes.int64):
if dimension is not None:
if axis is not None:
raise ValueError("Cannot specify both 'axis' and 'dimension'")
axis = dimension
elif axis is None:
axis = 0
return gen_math_ops.arg_min(input, axis, name=name, output_type=output_type)
# pylint: enable=redefined-builtin
# pylint: disable=anomalous-backslash-in-string,protected-access
# pylint: disable=g-docstring-has-escape
def abs(x, name=None):
r"""Computes the absolute value of a tensor.
Given a tensor `x` of complex numbers, this operation returns a tensor of type
`float32` or `float64` that is the absolute value of each element in `x`. All
elements in `x` must be complex numbers of the form \\(a + bj\\). The
absolute value is computed as \\( \sqrt{a^2 + b^2}\\). For example:
```python
x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x) # [5.25594902, 6.60492229]
```
Args:
x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`,
`int64`, `complex64` or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` the same size and type as `x` with absolute
values.
Note, for `complex64` or `complex128' input, the returned `Tensor` will be
of type `float32` or `float64`, respectively.
"""
with ops.name_scope(name, "Abs", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
if x.values.dtype in (dtypes.complex64, dtypes.complex128):
x_abs = gen_math_ops._complex_abs(
x.values, Tout=x.values.dtype.real_dtype, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_abs, dense_shape=x.dense_shape)
x_abs = gen_math_ops._abs(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_abs, dense_shape=x.dense_shape)
else:
x = ops.convert_to_tensor(x, name="x")
if x.dtype in (dtypes.complex64, dtypes.complex128):
return gen_math_ops._complex_abs(x, Tout=x.dtype.real_dtype, name=name)
return gen_math_ops._abs(x, name=name)
# pylint: enable=g-docstring-has-escape
# pylint: disable=redefined-builtin
def _bucketize(input, boundaries, name=None):
return gen_math_ops._bucketize(input=input, boundaries=boundaries, name=name)
# pylint: enable=redefined-builtin
class DivideDelegateWithName(object):
"""Use Python2/Python3 division delegation to implement divide for tensors."""
def __init__(self, x, name):
"""Construct DivideDelegateWithName.
Args:
x: Tensor to use as left operand in operator overloads
name: The name that is preferred for the op created.
"""
self.x = x
self.name = name
def __truediv__(self, y):
return _truediv_python3(self.x, y, self.name)
def __floordiv__(self, y):
return floordiv(self.x, y, self.name)
def __div__(self, y):
return _div_python2(self.x, y, self.name)
def divide(x, y, name=None):
"""Computes Python style division of `x` by `y`."""
if name is not None:
# Cannot use tensors operator overload, because it has no way to track
# override names. Use a dummy class to track the runtime division behavior
return DivideDelegateWithName(x, name) / y
else:
return x / y
def multiply(x, y, name=None):
return gen_math_ops._mul(x, y, name)
multiply.__doc__ = gen_math_ops._mul.__doc__.replace("Mul", "`tf.multiply`")
# TODO(aselle): put deprecation in after another round of global code changes
@deprecated(
"2016-12-30",
"`tf.mul(x, y)` is deprecated, please use `tf.multiply(x, y)` or `x * y`")
def _mul(x, y, name=None):
return gen_math_ops._mul(x, y, name)
_mul.__doc__ = (gen_math_ops._mul.__doc__ +
("" if _mul.__doc__ is None else _mul.__doc__))
def subtract(x, y, name=None):
return gen_math_ops._sub(x, y, name)
subtract.__doc__ = gen_math_ops._sub.__doc__.replace("`Sub`", "`tf.subtract`")
# TODO(aselle): put deprecation in after another round of global code changes
@deprecated(
"2016-12-30",
"`tf.sub(x, y)` is deprecated, please use `tf.subtract(x, y)` or `x - y`")
def _sub(x, y, name=None):
return gen_math_ops._sub(x, y, name)
_sub.__doc__ = (gen_math_ops._sub.__doc__ +
("" if _sub.__doc__ is None else _sub.__doc__))
# pylint: disable=g-docstring-has-escape
def negative(x, name=None):
"""Computes numerical negative value element-wise.
I.e., \\(y = -x\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
with ops.name_scope(name, "Neg", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_neg = gen_math_ops._neg(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_neg, dense_shape=x.dense_shape)
else:
return gen_math_ops._neg(x, name=name)
# pylint: enable=g-docstring-has-escape
# pylint: disable=g-docstring-has-escape
@deprecated("2016-12-30",
"`tf.neg(x)` is deprecated, please use `tf.negative(x)` or `-x`")
def _neg(x, name=None):
"""Computes numerical negative value element-wise.
I.e., \\(y = -x\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
return negative(x, name)
# pylint: enable=g-docstring-has-escape
def sign(x, name=None):
"""Returns an element-wise indication of the sign of a number.
`y = sign(x) = -1` if `x < 0`; 0 if `x == 0` or `tf.is_nan(x)`; 1 if `x > 0`.
Zero is returned for NaN inputs.
For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
@compatibility(numpy)
Equivalent to numpy.sign except for the behavior for input values of NaN.
@end_compatibility
"""
with ops.name_scope(name, "Sign", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_sign = gen_math_ops.sign(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_sign, dense_shape=x.dense_shape)
else:
return gen_math_ops.sign(x, name=name)
def square(x, name=None):
r"""Computes square of x element-wise.
I.e., \\(y = x * x = x^2\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Square", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_square = gen_math_ops.square(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_square, dense_shape=x.dense_shape)
else:
return gen_math_ops.square(x, name=name)
def sqrt(x, name=None):
r"""Computes square root of x element-wise.
I.e., \\(y = \sqrt{x} = x^{1/2}\\).
Args:
x: A `Tensor` or `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`, `complex64`, `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
with ops.name_scope(name, "Sqrt", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_sqrt = gen_math_ops.sqrt(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_sqrt, dense_shape=x.dense_shape)
else:
return gen_math_ops.sqrt(x, name=name)
def erf(x, name=None):
"""Computes the Gauss error function of `x` element-wise.
Args:
x: A `Tensor` of `SparseTensor`. Must be one of the following types: `half`,
`float32`, `float64`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor`, respectively. Has the same type as `x`.
"""
with ops.name_scope(name, "Erf", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
x_erf = gen_math_ops.erf(x.values, name=name)
return sparse_tensor.SparseTensor(
indices=x.indices, values=x_erf, dense_shape=x.dense_shape)
else:
return gen_math_ops.erf(x, name=name)
def scalar_mul(scalar, x):
"""Multiplies a scalar times a `Tensor` or `IndexedSlices` object.
Intended for use in gradient code which might deal with `IndexedSlices`
objects, which are easy to multiply by a scalar but more expensive to
multiply with arbitrary tensors.
Args:
scalar: A 0-D scalar `Tensor`. Must have known shape.
x: A `Tensor` or `IndexedSlices` to be scaled.
Returns:
`scalar * x` of the same type (`Tensor` or `IndexedSlices`) as `x`.
Raises:
ValueError: if scalar is not a 0-D `scalar`.
"""
scalar = ops.convert_to_tensor(
scalar, dtype=x.dtype.base_dtype, name="scalar")
shape = scalar.get_shape()
if shape.ndims == 0:
if isinstance(x, ops.IndexedSlices):
return ops.IndexedSlices(scalar * x.values, x.indices, x.dense_shape)
else:
return scalar * x
else:
raise ValueError("Only scalar multiply works, got shape %s" % shape)
def pow(x, y, name=None):
r"""Computes the power of one value to another.
Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
corresponding elements in `x` and `y`. For example:
```python
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
```
Args:
x: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
y: A `Tensor` of type `float32`, `float64`, `int32`, `int64`, `complex64`,
or `complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor`.
"""
with ops.name_scope(name, "Pow", [x]) as name:
return gen_math_ops._pow(x, y, name=name)
# pylint: disable=redefined-builtin,redefined-outer-name
def complex(real, imag, name=None):
r"""Converts two real numbers to a complex number.
Given a tensor `real` representing the real part of a complex number, and a
tensor `imag` representing the imaginary part of a complex number, this
operation returns complex numbers elementwise of the form \\(a + bj\\), where
*a* represents the `real` part and *b* represents the `imag` part.
The input tensors `real` and `imag` must have the same shape.
For example:
```python
real = tf.constant([2.25, 3.25])
imag = tf.constant([4.75, 5.75])
tf.complex(real, imag) # [[2.25 + 4.75j], [3.25 + 5.75j]]
```
Args:
real: A `Tensor`. Must be one of the following types: `float32`,
`float64`.
imag: A `Tensor`. Must have the same type as `real`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `complex64` or `complex128`.
"""
real = ops.convert_to_tensor(real, name="real")
imag = ops.convert_to_tensor(imag, name="imag")
with ops.name_scope(name, "Complex", [real, imag]) as name:
input_types = (real.dtype, imag.dtype)
if input_types == (dtypes.float64, dtypes.float64):
Tout = dtypes.complex128
elif input_types == (dtypes.float32, dtypes.float32):
Tout = dtypes.complex64
else:
raise TypeError("real and imag have incorrect types: "
"{} {}".format(real.dtype.name, imag.dtype.name))
return gen_math_ops._complex(real, imag, Tout=Tout, name=name)
def real(input, name=None):
r"""Returns the real part of a complex number.
Given a tensor `input` of complex numbers, this operation returns a tensor of
type `float32` or `float64` that is the real part of each element in `input`.
All elements in `input` must be complex numbers of the form \\(a + bj\\),
where *a* is the real part returned by this operation and *b* is the
imaginary part.
For example:
```python
x = tf.constant([-2.25 + 4.75j, 3.25 + 5.75j])
tf.real(x) # [-2.25, 3.25]
```
If `input` is already real, it is returned unchanged.
Args:
input: A `Tensor`. Must have numeric type.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32` or `float64`.
"""
with ops.name_scope(name, "Real", [input]) as name:
real_dtype = input.dtype.real_dtype
if input.dtype.base_dtype == real_dtype:
return input
return gen_math_ops.real(input, Tout=real_dtype, name=name)
def imag(input, name=None):
r"""Returns the imaginary part of a complex number.
Given a tensor `input` of complex numbers, this operation returns a tensor of
type `float` that is the argument of each element in `input`. All elements in
`input` must be complex numbers of the form \\(a + bj\\), where *a*
is the real part and *b* is the imaginary part returned by the operation.
For example:
```python
x = tf.constant([-2.25 + 4.75j, 3.25 + 5.75j])
tf.imag(x) # [4.75, 5.75]
```
Args:
input: A `Tensor`. Must be one of the following types: `complex64`,
`complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32` or `float64`.
"""
with ops.name_scope(name, "Imag", [input]) as name:
return gen_math_ops.imag(input, Tout=input.dtype.real_dtype, name=name)
def angle(input, name=None):
r"""Returns the argument of a complex number.
Given a tensor `input` of complex numbers, this operation returns a tensor of
type `float32` or `float64` that is the argument of each element in `input`.
All elements in `input` must be complex numbers of the form \\(a + bj\\),
where *a* is the real part and *b* is the imaginary part.
The argument returned by this function is of the form \\(atan2(b, a)\\).
For example:
```
# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
tf.angle(input) ==> [2.0132, 1.056]
```
Args:
input: A `Tensor`. Must be one of the following types: `complex64`,
`complex128`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32` or `float64`.
"""
with ops.name_scope(name, "Angle", [input]) as name:
return gen_math_ops.angle(input, Tout=input.dtype.real_dtype, name=name)
# pylint: enable=redefined-outer-name,redefined-builtin
def round(x, name=None):
"""Rounds the values of a tensor to the nearest integer, element-wise.
Rounds half to even. Also known as bankers rounding. If you want to round
according to the current system rounding mode use tf::cint.
For example:
```python
x = tf.constant([0.9, 2.5, 2.3, 1.5, -4.5])
tf.round(x) # [ 1.0, 2.0, 2.0, 2.0, -4.0 ]
```
Args:
x: A `Tensor` of type `float32` or `float64`.
name: A name for the operation (optional).
Returns:
A `Tensor` of same shape and type as `x`.
"""
x = ops.convert_to_tensor(x, name="x")
if x.dtype.is_integer:
return x
else:
return gen_math_ops.round(x, name=name)
def cast(x, dtype, name=None):
"""Casts a tensor to a new type.
The operation casts `x` (in case of `Tensor`) or `x.values`
(in case of `SparseTensor`) to `dtype`.
For example:
```python
x = tf.constant([1.8, 2.2], dtype=tf.float32)
tf.cast(x, tf.int32) # [1, 2], dtype=tf.int32
```
Args:
x: A `Tensor` or `SparseTensor`.
dtype: The destination type.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x`.
Raises:
TypeError: If `x` cannot be cast to the `dtype`.
"""
base_type = dtypes.as_dtype(dtype).base_dtype
with ops.name_scope(name, "Cast", [x]) as name:
if isinstance(x, sparse_tensor.SparseTensor):
values_cast = cast(x.values, base_type, name=name)
return sparse_tensor.SparseTensor(x.indices, values_cast, x.dense_shape)
else:
# TODO(josh11b): If x is not already a Tensor, we could return
# ops.convert_to_tensor(x, dtype=dtype, ...) here, but that
# allows some conversions that cast() can't do, e.g. casting numbers to
# strings.
x = ops.convert_to_tensor(x, name="x")
if x.dtype.base_dtype == base_type:
return x
return gen_math_ops.cast(x, base_type, name=name)
def saturate_cast(value, dtype, name=None):
"""Performs a safe saturating cast of `value` to `dtype`.
This function casts the input to `dtype` without applying any scaling. If
there is a danger that values would over or underflow in the cast, this op
applies the appropriate clamping before the cast.
Args:
value: A `Tensor`.
dtype: The desired output `DType`.
name: A name for the operation (optional).
Returns:
`value` safely cast to `dtype`.
"""
# When casting to a type with smaller representable range, clamp.
# Note that this covers casting to unsigned types as well.
with ops.name_scope(name, "saturate_cast", [value]) as name:
value = ops.convert_to_tensor(value, name="value")
dtype = dtypes.as_dtype(dtype).base_dtype
if value.dtype.min < dtype.min:
value = gen_math_ops.maximum(value,
ops.convert_to_tensor(
dtype.min, dtype=value.dtype,
name="min"))
if value.dtype.max > dtype.max:
value = gen_math_ops.minimum(value,
ops.convert_to_tensor(
dtype.max, dtype=value.dtype,
name="max"))
return cast(value, dtype, name=name)
def to_float(x, name="ToFloat"):
"""Casts a tensor to type `float32`.
Args:
x: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`.
Raises:
TypeError: If `x` cannot be cast to the `float32`.
"""
return cast(x, dtypes.float32, name=name)
def to_double(x, name="ToDouble"):
"""Casts a tensor to type `float64`.
Args:
x: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`.
Raises:
TypeError: If `x` cannot be cast to the `float64`.
"""
return cast(x, dtypes.float64, name=name)
def to_int32(x, name="ToInt32"):
"""Casts a tensor to type `int32`.
Args:
x: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x` with type `int32`.
Raises:
TypeError: If `x` cannot be cast to the `int32`.
"""
return cast(x, dtypes.int32, name=name)
def to_int64(x, name="ToInt64"):
"""Casts a tensor to type `int64`.
Args:
x: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x` with type `int64`.
Raises:
TypeError: If `x` cannot be cast to the `int64`.
"""
return cast(x, dtypes.int64, name=name)
def to_bfloat16(x, name="ToBFloat16"):
"""Casts a tensor to type `bfloat16`.
Args:
x: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` or `SparseTensor` with same shape as `x` with type `bfloat16`.
Raises:
TypeError: If `x` cannot be cast to the `bfloat16`.
"""
return cast(x, dtypes.bfloat16, name=name)
ops.Tensor._override_operator("__neg__", gen_math_ops._neg)
ops.Tensor._override_operator("__abs__", abs)
# __invert__ corresponds to the ~ operator. Here we follow the numpy convention
# ~ marks an elementwise bit-wise inverse. This is only implemented for boolean
# tensors and will throw a TypeError if used on nonboolean arrays
ops.Tensor._override_operator("__invert__", gen_math_ops.logical_not)
def _OverrideBinaryOperatorHelper(func, op_name, clazz_object=ops.Tensor):
"""Register operators with different tensor and scalar versions.
If `clazz_object` is `SparseTensor`, assumes `func` takes `(sp_indices,
sp_values, sp_shape, dense)` and outputs `(new_sp_values)`.
Args:
func: the operator
op_name: name of the operator being overridden
clazz_object: class to override for. Either `Tensor` or `SparseTensor`.
"""
def binary_op_wrapper(x, y):
with ops.name_scope(None, op_name, [x, y]) as name:
if not isinstance(y, sparse_tensor.SparseTensor):
try:
y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
except TypeError:
# If the RHS is not a tensor, it might be a tensor aware object
# that can implement the operator with knowledge of itself
# and the tensor.
if hasattr(type(y), "__r%s__" % op_name):
return NotImplemented
else:
raise
return func(x, y, name=name)
def binary_op_wrapper_sparse(sp_x, y):
with ops.name_scope(None, op_name, [sp_x, y]) as name:
y = ops.convert_to_tensor(y, dtype=sp_x.dtype.base_dtype, name="y")
return sparse_tensor.SparseTensor(sp_x.indices,
func(
sp_x.indices,
sp_x.values,
sp_x.dense_shape,
y,
name=name), sp_x.dense_shape)
def r_binary_op_wrapper(y, x):
with ops.name_scope(None, op_name, [x, y]) as name:
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
return func(x, y, name=name)
# Propagate func.__doc__ to the wrappers
try:
doc = func.__doc__
except AttributeError:
doc = None
binary_op_wrapper.__doc__ = doc
r_binary_op_wrapper.__doc__ = doc
binary_op_wrapper_sparse.__doc__ = doc
if clazz_object is ops.Tensor:
clazz_object._override_operator("__%s__" % op_name, binary_op_wrapper)
del binary_op_wrapper
clazz_object._override_operator("__r%s__" % op_name, r_binary_op_wrapper)
del r_binary_op_wrapper
else:
clazz_object._override_operator("__%s__" % op_name,
binary_op_wrapper_sparse)
del binary_op_wrapper_sparse
# Conversion table for __truediv__. None entries mean no conversion required.
_TRUEDIV_TABLE = {
dtypes.uint8: dtypes.float32,
dtypes.int8: dtypes.float32,
dtypes.uint16: dtypes.float32,
dtypes.int16: dtypes.float32,
dtypes.int32: dtypes.float64,
dtypes.int64: dtypes.float64,
dtypes.float16: None,
dtypes.float32: None,
dtypes.float64: None,
dtypes.complex64: None,
dtypes.complex128: None,
}
# NOTE: the support of "sparse (true)div dense" is currently not baked in into
# "tf.(true_)div()". Until such an API decision is made, the supported usage is
# to explicitly use the "/" operator to invoke either truediv or div.
def _sparse_dense_truediv(sp_indices, sp_values, sp_shape, y, name=None):
"""Internal helper function for 'sp_t / dense_t'."""
with ops.name_scope(name, "truediv", [sp_indices, sp_values, sp_shape,
y]) as name:
sp_values = ops.convert_to_tensor(sp_values, name="sp_values")
y = ops.convert_to_tensor(y, name="y")
x_dtype = sp_values.dtype.base_dtype
y_dtype = y.dtype.base_dtype
if x_dtype != y_dtype:
raise TypeError("x and y must have the same dtype, got %r != %r" %
(x_dtype, y_dtype))
try:
dtype = _TRUEDIV_TABLE[x_dtype]
except KeyError:
raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
if dtype is not None:
sp_values = cast(sp_values, dtype)
y = cast(y, dtype)
return gen_sparse_ops.sparse_dense_cwise_div(
sp_indices, sp_values, sp_shape, y, name=name)
def _truediv_python3(x, y, name=None):
with ops.name_scope(name, "truediv", [x, y]) as name:
x = ops.convert_to_tensor(x, name="x")
y = ops.convert_to_tensor(y, name="y")
x_dtype = x.dtype.base_dtype
y_dtype = y.dtype.base_dtype
if x_dtype != y_dtype:
raise TypeError("x and y must have the same dtype, got %r != %r" %
(x_dtype, y_dtype))
try:
dtype = _TRUEDIV_TABLE[x_dtype]
except KeyError:
raise TypeError("Invalid dtype %r in __truediv__" % x_dtype)
if dtype is not None:
x = cast(x, dtype)
y = cast(y, dtype)
return gen_math_ops._real_div(x, y, name=name)
def _div_python2(x, y, name=None):
"""Divide two values using Python 2 semantics. Used for Tensor.__div__.
Args:
x: `Tensor` numerator of real numeric type.
y: `Tensor` denominator of real numeric type.
name: A name for the operation (optional).
Returns:
`x / y` returns the quotient of x and y.