To change the shape of ``x`` without changing its data.
There are some tricks when specifying the target shape.
1. -1 means the value of this dimension is inferred from the total element
number of x and remaining dimensions. Thus one and only one dimension can
be set -1.
2. 0 means the actual dimension value is going to be copied from the
corresponding dimension of x. The index of 0s in shape can not exceed
the dimension of x.
Here are some examples to explain it.
1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [6, 8], the reshape operator will transform x into a 2-D tensor with
shape [6, 8] and leaving x's data unchanged.
2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
specified is [2, 3, -1, 2], the reshape operator will transform x into a
4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
case, one dimension of the target shape is set to -1, the value of this
dimension is inferred from the total element number of x and remaining
dimensions.
3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
besides -1, 0 means the actual dimension value is going to be copied from
the corresponding dimension of x.
Args:
x(ComplexVariable): the input. A ``Tensor`` or ``LoDTensor`` , data
type: ``complex64`` or ``complex128``.
shape(list|tuple|Variable): target shape. At most one dimension of
the target shape can be -1. If ``shape`` is a list or tuple, the
elements of it should be integers or Tensors with shape [1] and
data type ``int32``. If ``shape`` is an Variable, it should be
an 1-D Tensor of data type ``int32``.
inplace(bool, optional): If ``inplace`` is True, the output of
``reshape`` is the same ComplexVariable as the input. Otherwise,
the input and output of ``reshape`` are different
ComplexVariables. Defaults to False. Note that if ``x``is more
than one OPs' input, ``inplace`` must be False.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name` .
Returns:
ComplexVariable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. It is a new ComplexVariable if ``inplace`` is ``False``, otherwise it is ``x``.
Raises:
ValueError: If more than one elements of ``shape`` is -1.
ValueError: If the element of ``shape`` is 0, the corresponding dimension should be less than or equal to the dimension of ``x``.
ValueError: If the elements in ``shape`` is negative except -1.
The element-wise division layer for complex number inputs. At least one of
inputs :attr:`x` and :attr:`y` must be a ComplexVariable. See the detailed
description for the function and other arguments
in :ref:`api_fluid_layers_elementwise_div` .
Args:
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
with dg.guard():
x = dg.to_variable(a)
y = dg.to_variable(b)
out = paddle.complex.elementwise_div(x, y)
print(out.numpy())
# [[0.24137931+0.10344828j 0.35 +0.05j ]
# [0.43396226+0.01886792j 0.5 +0.j ]]
"""
complex_variable_exists([x,y],"elementwise_div")
# (a + bi)/(c + di) = (a + bi)(c - di)/(c^2 + d^2)
(c,d)=(y.real,y.imag)ifis_complex(y)else(y,None)
y_conj=ComplexVariable(c,-d)ifis_real(d)elsec
e=1/(layers.pow(c,2.0)+layers.pow(d,2.0)
)ifis_real(d)else1/layers.pow(c,2.0)
returnelementwise_mul(
elementwise_mul(
x,y_conj,axis=axis,name=name),
e,
axis=axis,
name=name)
deftrace(x,offset=0,axis1=0,axis2=1,name=None):
"""
The layer to compute the trace for a complex number tensor. x :attr:`x` must be a ComplexVariable.
See the detailed description for the function and other arguments
in :ref:`api_tensor_math_trace` .
Args:
x(ComplexVariable): The input ComplexVariable x. Must be at least 2-dimensional.
The supported data types include complex64 and complex128.
offset(int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals).
axis1(int, optional): The first axis with respect to take diagonal. Default: 0.
axis2(int, optional): The second axis with respect to take diagonal. Default: 1.
name (str, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None.
Returns:
ComplexVariable: The trace result of input tensor x, it's data type is the same as input data type.
x (Variable|ComplexVariable): The first input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
y (Variable|ComplexVariable): The second input Variable or ComplexVariable
with any number of dimensions. The supported data types include float32
and float64 when it is a Variable. Otherwise the supported data types
are complex64 or complex128.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
ComplexVariable: The kronecker product, data type: complex64 or complex128, depending on the data type of x and y. If the data types of x and y are float32/complex64, the data type of the output is complex64, else if the data types of x and y are float64/complex128, the data type of the output is complex128.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle import fluid
import paddle.fluid.dygraph as dg
a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
