Merge branch 'blis/ops14todo' of https://github.com/Microsoft/CNTK into blis/ops14todo
This commit is contained in:
jeanfad
Коммит
16f4fa5fdf
|
|
@ -25,10 +25,10 @@ def cross_entropy_with_softmax(target_vector, output_vector, name=None):
|
|||
:func:`cntk.ops.softmax`
|
||||
|
||||
Example:
|
||||
>>> cntk.eval(cross_entropy_with_softmax([0., 0., 0., 1.], [1., 1., 1., 50.]))
|
||||
>>> C.eval(C.cross_entropy_with_softmax([0., 0., 0., 1.], [1., 1., 1., 50.]))
|
||||
#[0.]
|
||||
|
||||
>>> cntk.eval(cross_entropy_with_softmax([[0.35, 0.15, 0.05, 0.45], [1., 2., 3., 4.]))
|
||||
>>> C.eval(C.cross_entropy_with_softmax([[0.35, 0.15, 0.05, 0.45], [1., 2., 3., 4.]))
|
||||
#[1.84]
|
||||
|
||||
Args:
|
||||
|
|
@ -50,10 +50,10 @@ def square_error(target_matrix, output_matrix, name=None):
|
|||
This is often used as a training criterion node.
|
||||
|
||||
Example:
|
||||
>>> cntk.eval(square_error([4., 6.], [2., 1.]))
|
||||
>>> C.eval(C.square_error([4., 6.], [2., 1.]))
|
||||
#[29.]
|
||||
|
||||
>>> cntk.eval(square_error([1., 2.], [1., 2.]))
|
||||
>>> C.eval(C.square_error([1., 2.], [1., 2.]))
|
||||
#[0.]
|
||||
|
||||
Args:
|
||||
|
|
@ -77,10 +77,10 @@ def error_prediction(target_vector, output_vector, name=None):
|
|||
defined for it.
|
||||
|
||||
Example:
|
||||
>>> cntk.eval(error_prediction([0., 0., 0., 1.], [1., 2., 3., 4.]))
|
||||
>>> C.eval(C.error_prediction([0., 0., 0., 1.], [1., 2., 3., 4.]))
|
||||
#[0.]
|
||||
|
||||
>>> cntk.eval(error_prediction([0., 0., 1., 0.], [1., 2., 3., 4.]))
|
||||
>>> C.eval(C.error_prediction([0., 0., 1., 0.], [1., 2., 3., 4.]))
|
||||
#[1.]
|
||||
|
||||
Args:
|
||||
|
|
@ -105,6 +105,13 @@ def plus(left, right, name=None):
|
|||
two input tensors. It supports broadcasting. In case of scalars its backward
|
||||
pass propagates the received gradient.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.plus([1, 2, 3], [4, 5, 6]))
|
||||
[array([[ 5., 7., 9.]])]
|
||||
|
||||
>>> C.eval(C.plus([-5, -4, -3, -2, -1], [10]))
|
||||
[array([[ 5., 6., 7., 8., 9.]])]
|
||||
|
||||
Args:
|
||||
left: left side tensor
|
||||
right: right side tensor
|
||||
|
|
@ -122,6 +129,14 @@ def minus(left, right, name=None):
|
|||
right tensor. It supports broadcasting. In case of scalars its backward
|
||||
pass propagates the received gradient.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.minus([1, 2, 3], [4, 5, 6]))
|
||||
[array([[-3., -3., -3.]])]
|
||||
|
||||
>>> C.eval(C.minus([[1,2],[3,4]], 1))
|
||||
[array([[[ 0., 1.],
|
||||
[ 2., 3.]]])]
|
||||
|
||||
Args:
|
||||
left: left side tensor
|
||||
right: right side tensor
|
||||
|
|
@ -139,7 +154,15 @@ def element_times(left, right, name=None):
|
|||
Element-wise multiplication operation. The output of this operation is the
|
||||
element-wise product of the two input tensors. It supports broadcasting. In
|
||||
case of scalars its backward pass to left propagates right
|
||||
times the received gradient and vice versa.
|
||||
times the received gradient and vice versa.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.element_times([1., 1., 1., 1.], [0.5, 0.25, 0.125, 0.]))
|
||||
[array([[ 0.5 , 0.25 , 0.125, 0. ]])]
|
||||
|
||||
>>> C.eval(C.element_times([5., 10., 15., 30.], [2.]))
|
||||
[array([[ 10., 20., 30., 60.]])]
|
||||
|
||||
Args:
|
||||
left: left side tensor
|
||||
right: right side tensor
|
||||
|
|
@ -155,9 +178,16 @@ def element_divide(left, right, name=None):
|
|||
"""
|
||||
Element-wise division operation. The output of this operation is the
|
||||
element-wise division of the two input tensors. It supports broadcasting. In
|
||||
case of scalars its backward pass to left propagates 1/right
|
||||
case of scalars its backward pass to left propagates :math:`1/right`
|
||||
times the received gradient, and the backward pass to right propagates
|
||||
(-left/right^2) times the received gradient.
|
||||
:math:`(-left/right^2)` times the received gradient.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.element_divide([1., 1., 1., 1.], [0.5, 0.25, 0.125, 0.]))
|
||||
[array([[ 2., 4., 8., 0.]])]
|
||||
|
||||
>>> C.eval(C.element_divide([5., 10., 15., 30.], [2.]))
|
||||
[array([[ 2.5, 5. , 7.5, 15. ]])]
|
||||
|
||||
Args:
|
||||
left: left side tensor
|
||||
|
|
@ -177,6 +207,15 @@ def times(left, right, name=None):
|
|||
case of scalars its backward pass to left propagates right
|
||||
times the received gradient and vice versa.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.times([[1,2],[3,4]], [5,6]))
|
||||
[array([[ 17., 39.]])]
|
||||
|
||||
>>> C.eval(C.times([[1,2],[3,4],[5,6]], [[0.5,0.25],[0.25,0.5]]))
|
||||
[array([[[ 1. , 1.25],
|
||||
[ 2.5 , 2.75],
|
||||
[ 4. , 4.25]]])]
|
||||
|
||||
Args:
|
||||
left: left side tensor
|
||||
right: right side tensor
|
||||
|
|
@ -199,18 +238,20 @@ def floor(arg, name=None):
|
|||
element wise value rounded to the largest integer less than
|
||||
or equal to the input.
|
||||
|
||||
Examples:
|
||||
>>> floor([0.2, 1.3, 4., 5.5, 0.0])
|
||||
#[0.0, 1.0, 4.0, 5.0, 0.0]
|
||||
Example:
|
||||
>>> C.eval(C.floor([0.2, 1.3, 4., 5.5, 0.0]))
|
||||
[array([[ 0., 1., 4., 5., 0.]])]
|
||||
|
||||
>>> floor([[0.6, 3.3], [1.9, 5.6]])
|
||||
#[[0.0, 3.0], [1.0, 5.0]]
|
||||
>>> C.eval(C.floor([[0.6, 3.3], [1.9, 5.6]]))
|
||||
[array([[[ 0., 3.],
|
||||
[ 1., 5.]]])]
|
||||
|
||||
>>> floor([-5.5, -4.2, -3., -0.7, 0])
|
||||
#[-6.0, -5.0, -3.0, -1.0, 0.0]
|
||||
>>> C.eval(C.floor([-5.5, -4.2, -3., -0.7, 0]))
|
||||
[array([[-6., -5., -3., -1., 0.]])]
|
||||
|
||||
>>> floor([[-0.6, -4.3], [1.9, -3.2]])
|
||||
#[[-1.0, -5.0], [1.0, -4.0]]
|
||||
>>> C.eval(C.floor([[-0.6, -4.3], [1.9, -3.2]]))
|
||||
[array([[[-1., -5.],
|
||||
[ 1., -4.]]])]
|
||||
|
||||
Args:
|
||||
arg: input tensor
|
||||
|
|
@ -221,24 +262,20 @@ def floor(arg, name=None):
|
|||
from cntk.ops.cntk2 import Floor
|
||||
return Floor(arg, name = name)
|
||||
|
||||
|
||||
def ceil(arg, name=None):
|
||||
"""
|
||||
Ceil operation. The output of this operation is the
|
||||
element wise value rounded to the smallest integer greater than
|
||||
or equal to the input.
|
||||
|
||||
Examples:
|
||||
>>> ceil([0.2, 1.3, 4., 5.5, 0.0])
|
||||
#[1.0, 2.0, 4.0, 6.0, 0.0]
|
||||
|
||||
>>> ceil([[0.6, 3.3], [1.9, 5.6]])
|
||||
#[[1.0, 4.0], [2.0, 6.0]]
|
||||
|
||||
>>> ceil([-5.5, -4.2, -3., -0.7, 0])
|
||||
#[-5.0, -4.0, -3.0, 0.0, 0.0]
|
||||
|
||||
>>> ceil([[-0.6, -4.3], [1.9, -3.2]])
|
||||
#[[0.0, -4.0], [2.0, -3.0]]
|
||||
Example:
|
||||
>>> C.eval(C.ceil([0.2, 1.3, 4., 5.5, 0.0]))
|
||||
[array([[ 1., 2., 4., 6., 0.]])]
|
||||
|
||||
>>> C.eval(C.ceil([[0.6, 3.3], [1.9, 5.6]]))
|
||||
[array([[[ 1., 4.],
|
||||
[ 2., 6.]]])]
|
||||
|
||||
Args:
|
||||
arg: input tensor
|
||||
|
|
@ -249,6 +286,7 @@ def ceil(arg, name=None):
|
|||
from cntk.ops.cntk2 import Ceil
|
||||
return Ceil(arg, name = name)
|
||||
|
||||
|
||||
def round(arg, name=None):
|
||||
"""
|
||||
Round operation. The output of this operation is the
|
||||
|
|
@ -258,18 +296,20 @@ def round(arg, name=None):
|
|||
This is different from the round operation of numpy which follows
|
||||
round half to even.
|
||||
|
||||
Examples:
|
||||
>>> round([0.2, 1.3, 4., 5.5, 0.0])
|
||||
#[0.0, 1.0, 4.0, 6.0, 0.0]
|
||||
Example:
|
||||
>>> C.eval(C.round([0.2, 1.3, 4., 5.5, 0.0]))
|
||||
[array([[ 0., 1., 4., 6., 0.]])]
|
||||
|
||||
>>> round([[0.6, 3.3], [1.9, 5.6]])
|
||||
#[[1.0, 3.0], [2.0, 6.0]]
|
||||
>>> C.eval(C.round([[0.6, 3.3], [1.9, 5.6]]))
|
||||
[array([[[ 1., 3.],
|
||||
[ 2., 6.]]])]
|
||||
|
||||
>>> round([-5.5, -4.2, -3., -0.7, 0])
|
||||
#[-5.0, -4.0, -3.0, -1.0, 0.0]
|
||||
>>> C.eval(C.round([-5.5, -4.2, -3., -0.7, 0]))
|
||||
[array([[-5., -4., -3., -1., 0.]])]
|
||||
|
||||
>>> round([[-0.6, -4.3], [1.9, -3.2]])
|
||||
#[[-1.0, -4.0], [2.0, -3.0]]
|
||||
>>> C.eval(C.round([[-0.6, -4.3], [1.9, -3.2]]))
|
||||
[array([[[-1., -4.],
|
||||
[ 2., -3.]]])]
|
||||
|
||||
Args:
|
||||
arg: input tensor
|
||||
|
|
@ -297,12 +337,12 @@ def clip(x, min_value, max_value, name=None):
|
|||
The backward pass propagates the received gradient if no clipping occurred,
|
||||
and 0 if the value was clipped.
|
||||
|
||||
Examples:
|
||||
>>> clip(2., 4., [1., 2.1, 3.0, 4.1])
|
||||
#[2.0, 2.1, 3.0, 4.0]
|
||||
Example:
|
||||
>>> C.eval(C.clip([1., 2.1, 3.0, 4.1], 2., 4.))
|
||||
[array([[ 2. , 2.1, 3. , 4. ]])]
|
||||
|
||||
>>> clip([-5., -4., 0., 3., 5.], [5., 4., 1., 4., 9.], [-10., -5., 0., 5., 10.])
|
||||
#[-5, -4., 0., 4., 9.]
|
||||
>>> C.eval(C.clip([-10., -5., 0., 5., 10.], [-5., -4., 0., 3., 5.], [5., 4., 1., 4., 9.]))
|
||||
[array([[-5., -4., 0., 4., 9.]])]
|
||||
|
||||
Args:
|
||||
x: tensor to be clipped
|
||||
|
|
@ -322,16 +362,16 @@ def relu(x, name=None):
|
|||
of `x`: ``max(x, 0)``
|
||||
|
||||
The output tensor has the same shape as `x`.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.relu([[-1, -0.5, 0, 1, 2]]))
|
||||
[array([[[ 0., 0., 0., 1., 2.]]])]
|
||||
|
||||
Args:
|
||||
x: any :class:`cntk.graph.ComputationNode` that outputs a tensor
|
||||
|
||||
Returns:
|
||||
:class:`cntk.graph.ComputationNode`
|
||||
|
||||
Example:
|
||||
>>> cntk.eval(cntk.ops.rectified_linear([[-1, -0.5, 0, 1, 2]]))
|
||||
[[[0, 0, 0, 1, 2]]]
|
||||
"""
|
||||
from cntk.ops.cntk2 import Relu
|
||||
return Relu(x, name=name)
|
||||
|
|
@ -345,6 +385,10 @@ def sigmoid(x, name=None):
|
|||
|
||||
The output tensor has the same shape as `x`.
|
||||
|
||||
Example:
|
||||
>>> C.eval(C.sigmoid([-2, -1., 0., 1., 2.]))
|
||||
[array([[ 0.119203, 0.268941, 0.5 , 0.731059, 0.880797]])]
|
||||
|
||||
Args:
|
||||
x: any :class:`cntk.graph.ComputationNode` that outputs a tensor
|
||||
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче