@ -16,6 +16,7 @@ math functions
"""
from __future__ import print_function
from paddle . common_ops_import import *
from . . fluid . framework import core
from . . fluid . layers . layer_function_generator import _generate_doc_string_
@ -43,9 +44,9 @@ __all__ = [
# 'floor',
# 'increment',
# 'log',
# 'mul'
' mul ' ,
# 'multiplex',
# 'pow'
' pow ' ,
# 'reciprocal',
# 'reduce_max',
# 'reduce_min',
@ -143,6 +144,157 @@ Examples:
""" % o p_type
return func
@templatedoc ( )
def pow ( input , exponent , out = None , name = None ) :
"""
This is Pow Activation Operator .
: math : ` out = input ^ { exponent } `
Args :
input ( Variable ) : A ` ` Tensor ` ` or ` ` LoDTensor ` ` . The data type is ` ` float32 ` ` or ` ` float64 ` ` .
exponent ( float32 | Variable ) : A scalar with type ` ` float32 ` ` or a ` ` Tensor ` ` with shape [ 1 ] and type ` ` float32 ` ` .
out ( Variable , optional ) : The Variable that stores results of the operation .
If out is None , a new Variable will be created to store the results .
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 :
Variable : A ` ` Tensor ` ` or ` ` LoDTensor ` ` . The data type is same as ` ` input ` ` .
Examples :
. . code - block : : python
import paddle
x = paddle . fluid . data ( name = " x " , shape = [ 32 , 32 ] , dtype = " float32 " )
# example 1: argument exponent is float
res = paddle . fluid . data ( name = " output " , shape = [ 32 , 32 ] , dtype = " float32 " )
y_1 = paddle . pow ( x , 2.0 , out = res )
# y_1 is x^{2.0}
# example 2: argument exponent is Variable
exponet_tensor = fluid . layers . fill_constant ( [ 1 ] , " float32 " , 3.0 )
res = paddle . fluid . data ( name = " output " , shape = [ 32 , 32 ] , dtype = " float32 " )
y_2 = paddle . pow ( x , exponent_tensor , out = res )
# y_2 is x^{3.0}
"""
helper = LayerHelper ( ' pow ' , * * locals ( ) )
inputs = { ' X ' : input }
attrs = { }
if isinstance ( exponent , Variable ) :
exponent . stop_gradient = True
inputs [ ' FactorTensor ' ] = exponent
else :
attrs [ ' factor ' ] = exponent
if out is None :
out = helper . create_variable_for_type_inference ( dtype = input . dtype )
else :
check_dtype (
out . dtype , out . name ,
convert_dtype ( input . dtype ) , ' pow ' ,
' (The out data type in pow must be the same with input data type.) ' )
if name :
warnings . warn (
" The output Variable name of the paddle.tensor.pow operation can only be given by parameter out or name. \
When parameter out and name are set at the same time , out has a higher priority than name . \
Finally , the output Variable name is same as the out name % s "
%
out . name ,
category = UserWarning ,
stacklevel = 2 )
helper . append_op (
type = ' pow ' , inputs = inputs , outputs = { ' Out ' : out } , attrs = attrs )
return out
def mul ( x , y , x_num_col_dims = 1 , y_num_col_dims = 1 , out = None , name = None ) :
"""
Mul Operator .
This operator is used to perform matrix multiplication for input $ x $ and $ y $ .
The equation is :
. . math : :
Out = x * y
Both the input $ x $ and $ y $ can carry the LoD ( Level of Details ) information , or not .
But the output only shares the LoD information with input $ x $ .
Args :
x ( Variable ) : The first input Tensor / LoDTensor of mul_op .
y ( Variable ) : The second input Tensor / LoDTensor of mul_op .
x_num_col_dims ( int , optional ) : The mul_op can take tensors with more than two dimensions as its inputs .
If the input $ x $ is a tensor with more than two dimensions , $ x $ will be flattened into a two - dimensional
matrix first . The flattening rule is : the first ` num_col_dims ` will be flattened to form the first
dimension of the final matrix ( the height of the matrix ) , and the rest ` rank ( x ) - num_col_dims `
dimensions are flattened to form the second dimension of the final matrix ( the width of the matrix ) .
As a result , height of the flattened matrix is equal to the product of $ x $ ' s first `x_num_col_dims` dimensions '
sizes , and width of the flattened matrix is equal to the product of $ x $ ' s last `rank(x) - num_col_dims`
dimensions ' size. For example, suppose $x$ is a 6-dimensional tensor with the shape [2, 3, 4, 5, 6],
and ` x_num_col_dims ` = 3. Thus , the flattened matrix will have a shape [ 2 x 3 x 4 , 5 x 6 ] = [ 24 , 30 ] . Default is 1.
y_num_col_dims ( int , optional ) : The mul_op can take tensors with more than two dimensions as its inputs . If the
input $ y $ is a tensor with more than two dimensions , $ y $ will be flattened into a two - dimensional matrix first .
The attribute ` y_num_col_dims ` determines how $ y $ is flattened . See comments of ` x_num_col_dims ` for more details .
Default is 1.
out ( Variable , optinal ) : The Variable that stores results of the operation . If out is None ,
a new Variable will be created to store the results .
name ( str , optional ) : Name of the output . Normally there is no need for user to set this property .
For more information , please refer to : ref : ` api_guide_Name ` . Default is None . If both of out and name are not None ,
the output name will be same as out .
Returns :
Variable ( Tensor / LoDTensor ) : The output Tensor / LoDTensor of mul op .
Examples :
. . code - block : : python
import paddle
dataX = paddle . fluid . data ( name = " dataX " , append_batch_size = False , shape = [ 2 , 5 ] , dtype = " float32 " )
dataY = paddle . fluid . data ( name = " dataY " , append_batch_size = False , shape = [ 5 , 3 ] , dtype = " float32 " )
res = paddle . fluid . data ( name = " output " , append_batch_size = False , shape = [ 2 , 3 ] , dtype = " float32 " )
output = paddle . mul ( dataX , dataY ,
x_num_col_dims = 1 ,
y_num_col_dims = 1 ,
out = res )
"""
inputs = { " X " : [ x ] , " Y " : [ y ] }
attrs = { " x_num_col_dims " : x_num_col_dims , " y_num_col_dims " : y_num_col_dims }
if in_dygraph_mode ( ) :
outs = core . ops . mul ( inputs , attrs )
return outs [ ' Out ' ] [ 0 ]
helper = LayerHelper ( " mul " , * * locals ( ) )
check_variable_and_dtype ( x , ' x ' , [ ' float16 ' , ' float32 ' , ' float64 ' ] , ' mul ' )
check_variable_and_dtype ( y , ' y ' , [ ' float16 ' , ' float32 ' , ' float64 ' ] , ' mul ' )
if out is None :
out = helper . create_variable_for_type_inference ( dtype = x . dtype )
else :
check_dtype (
out . dtype , out . name ,
convert_dtype ( x . dtype ) , ' mul ' ,
' (The out data type in pow must be the same with input data type.) ' )
if name :
warnings . warn (
" The output Variable name of the paddle.tensor.pow operation can only be given by parameter out or name. \
When parameter out and name are set at the same time , out has a higher priority than name . \
Finally , the output Variable name is same as the out name % s "
%
out . name ,
category = UserWarning ,
stacklevel = 2 )
helper . append_op (
type = " mul " , inputs = { " X " : x ,
" Y " : y } , attrs = attrs , outputs = { " Out " : out } )
return out
__ops__noattr__ = [
' atan ' ,
Steffy-zxf
5 years ago
committed by
GitHub