"""activation"""
import numpy as np
from mindspore._checkparam import Validator as validator
from mindspore._extends import cell_attr_register
from mindspore.common import dtype as mstype
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from ..cell import Cell
__all__ = ['Softmax',
'LogSoftmax',
'ReLU',
'ReLU6',
'Tanh',
'GELU',
'FastGelu',
'Sigmoid',
'PReLU',
'get_activation',
'LeakyReLU',
'HSigmoid',
'HSwish',
'ELU',
'LogSigmoid',
'SoftShrink',
'HShrink',
]
class Softmax(Cell):
r"""
Softmax activation function.
Applies the Softmax function to an n-dimensional input Tensor.
The input is a Tensor of logits transformed with exponential function and then
normalized to lie in range [0, 1] and sum up to 1.
Softmax is defined as:
.. math::
\text{softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_{j=0}^{n-1}\exp(x_j)},
where :math:`x_{i}` is the :math:`i`-th slice in the given dimension of the input Tensor.
Args:
axis (Union[int, tuple[int]]): The axis to apply Softmax operation, -1 means the last dimension. Default: -1.
Inputs:
- **x** (Tensor) - The input of Softmax with data type of float16 or float32.
Outputs:
Tensor, which has the same type and shape as `x` with values in the range[0,1].
Raises:
TypeError: If `axis` is neither an int nor a tuple.
TypeError: If dtype of `x` is neither float16 nor float32.
ValueError: If `axis` is a tuple whose length is less than 1.
ValueError: If `axis` is a tuple whose elements are not all in range [-len(x), len(x)).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> softmax = nn.Softmax()
>>> output = softmax(x)
>>> print(output)
[0.03168 0.01166 0.0861 0.636 0.2341 ]
"""
def __init__(self, axis=-1):
"""Initialize Softmax."""
super(Softmax, self).__init__()
self.softmax = P.Softmax(axis)
def construct(self, x):
return self.softmax(x)
class LogSoftmax(Cell):
r"""
LogSoftmax activation function.
Applies the LogSoftmax function to n-dimensional input tensor.
The input is transformed by the Softmax function and then by the log function to lie in range[-inf,0).
Logsoftmax is defined as:
.. math::
\text{logsoftmax}(x_i) = \log \left(\frac{\exp(x_i)}{\sum_{j=0}^{n-1} \exp(x_j)}\right),
where :math:`x_{i}` is the :math:`i`-th slice in the given dimension of the input Tensor.
Args:
axis (int): The axis to apply LogSoftmax operation, -1 means the last dimension. Default: -1.
Inputs:
- **x** (Tensor) - The input of LogSoftmax, with float16 or float32 data type.
Outputs:
Tensor, which has the same type and shape as the input as `x` with values in the range[-inf,0).
Raises:
TypeError: If `axis` is not an int.
TypeError: If dtype of `x` is neither float16 nor float32.
ValueError: If `axis` is not in range [-len(x), len(x)).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> log_softmax = nn.LogSoftmax()
>>> output = log_softmax(x)
>>> print(output)
[[-5.00672150e+00 -6.72150636e-03 -1.20067215e+01]
[-7.00091219e+00 -1.40009127e+01 -9.12250078e-04]]
"""
def __init__(self, axis=-1):
"""Initialize LogSoftmax."""
super(LogSoftmax, self).__init__()
self.log_softmax = P.LogSoftmax(axis)
def construct(self, x):
return self.log_softmax(x)
class ELU(Cell):
r"""
Exponential Linear Uint activation function.
Applies the exponential linear unit function element-wise.
The activation function is defined as:
.. math::
E_{i} =
\begin{cases}
x, &\text{if } x \geq 0; \cr
\text{alpha} * (\exp(x_i) - 1), &\text{otherwise.}
\end{cases}
The picture about ELU looks like this `ELU <https://en.wikipedia.org/wiki/
Activation_function#/media/File:Activation_elu.svg>`_.
Args:
alpha (float): The coefficient of negative factor whose type is float. Default: 1.0.
Inputs:
- **x** (Tensor) - The input of ELU with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means,any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If `alpha` is not a float.
TypeError: If dtype of `x` is neither float16 nor float32.
ValueError: If `alpha` is not equal to 1.0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float32)
>>> elu = nn.ELU()
>>> result = elu(x)
>>> print(result)
[-0.63212055 -0.86466473 0. 2. 1.]
"""
def __init__(self, alpha=1.0):
"""Initialize ELU."""
super(ELU, self).__init__()
self.elu = P.Elu(alpha)
def construct(self, x):
return self.elu(x)
class ReLU(Cell):
r"""
Rectified Linear Unit activation function.
Applies the rectified linear unit function element-wise.
.. math::
\text{ReLU}(x) = (x)^+ = \max(0, x),
It returns element-wise :math:`\max(0, x)`, specially, the neurons with the negative output
will be suppressed and the active neurons will stay the same.
The picture about ReLU looks like this `ReLU <https://en.wikipedia.org/wiki/
Activation_function#/media/File:Activation_rectified_linear.svg>`_.
Inputs:
- **x** (Tensor) - The input of ReLU. The data type is Number.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is not a number.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16)
>>> relu = nn.ReLU()
>>> output = relu(x)
>>> print(output)
[0. 2. 0. 2. 0.]
"""
def __init__(self):
"""Initialize ReLU."""
super(ReLU, self).__init__()
self.relu = P.ReLU()
def construct(self, x):
return self.relu(x)
class ReLU6(Cell):
r"""
Compute ReLU6 activation function.
ReLU6 is similar to ReLU with a upper limit of 6, which if the inputs are greater than 6, the outputs
will be suppressed to 6.
It computes element-wise as
.. math::
\min(\max(0, x), 6).
The input is a Tensor of any valid shape.
Inputs:
- **x** (Tensor) - The input of ReLU6 with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, which has the same type as `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> relu6 = nn.ReLU6()
>>> output = relu6(x)
>>> print(output)
[0. 0. 0. 2. 1.]
"""
def __init__(self):
"""Initialize ReLU6."""
super(ReLU6, self).__init__()
self.relu6 = P.ReLU6()
def construct(self, x):
return self.relu6(x)
class LeakyReLU(Cell):
r"""
Leaky ReLU activation function.
LeakyReLU is similar to ReLU, but LeakyReLU has a slope that makes it not equal to 0 at x < 0.
The activation function is defined as:
.. math::
\text{leaky_relu}(x) = \begin{cases}x, &\text{if } x \geq 0; \cr
\text{alpha} * x, &\text{otherwise.}\end{cases}
See https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf
Args:
alpha (Union[int, float]): Slope of the activation function at x < 0. Default: 0.2.
Inputs:
- **x** (Tensor) - The input of LeakyReLU.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, has the same type and shape as the `x`.
Raises:
TypeError: If `alpha` is not a float or an int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> leaky_relu = nn.LeakyReLU()
>>> output = leaky_relu(x)
>>> print(output)
[[-0.2 4. -1.6]
[ 2. -1. 9. ]]
"""
def __init__(self, alpha=0.2):
"""Initialize LeakyReLU."""
super(LeakyReLU, self).__init__()
validator.check_value_type('alpha', alpha, [float, int], self.cls_name)
self.greater_equal = P.GreaterEqual()
self.mul = P.Mul()
self.alpha = alpha
self.select_op = P.Maximum()
if self.alpha > 1:
self.select_op = P.Minimum()
def construct(self, x):
alpha_array = P.Cast()(F.scalar_to_array(self.alpha), P.DType()(x))
out = self.select_op(alpha_array * x, x)
return out
class Tanh(Cell):
r"""
Tanh activation function.
Applies the Tanh function element-wise, returns a new tensor with the hyperbolic tangent of the elements of input,
The input is a Tensor with any valid shape.
Tanh function is defined as:
.. math::
tanh(x_i) = \frac{\exp(x_i) - \exp(-x_i)}{\exp(x_i) + \exp(-x_i)} = \frac{\exp(2x_i) - 1}{\exp(2x_i) + 1},
where :math:`x_i` is an element of the input Tensor.
Inputs:
- **x** (Tensor) - The input of Tanh with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([1, 2, 3, 2, 1]), mindspore.float16)
>>> tanh = nn.Tanh()
>>> output = tanh(x)
>>> print(output)
[0.7617 0.964 0.995 0.964 0.7617]
"""
def __init__(self):
"""Initialize Tanh."""
super(Tanh, self).__init__()
self.tanh = P.Tanh()
def construct(self, x):
return self.tanh(x)
class GELU(Cell):
r"""
Gaussian error linear unit activation function.
Applies GELU function to each element of the input. The input is a Tensor with any valid shape.
GELU is defined as:
.. math::
GELU(x_i) = x_i*P(X < x_i),
where :math:`P` is the cumulative distribution function
of standard Gaussian distribution and :math:`x_i` is the element of the input.
The picture about GELU looks like this `GELU <https://en.wikipedia.org/wiki/
Activation_function#/media/File:Activation_gelu.png>`_.
Inputs:
- **x** (Tensor) - The input of GELU with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> gelu = nn.GELU()
>>> output = gelu(x)
>>> print(output)
[[-1.5880802e-01 3.9999299e+00 -3.1077917e-21]
[ 1.9545976e+00 -2.2918017e-07 9.0000000e+00]]
"""
def __init__(self):
"""Initialize GELU."""
super(GELU, self).__init__()
self.gelu = P.GeLU()
def construct(self, x):
return self.gelu(x)
class FastGelu(Cell):
r"""
Fast Gaussian error linear unit activation function.
Applies FastGelu function to each element of the input. The input is a Tensor with any valid shape.
FastGelu is defined as:
.. math::
FastGelu(x_i) = \frac {x_i} {1 + \exp(-1.702 * \left| x_i \right|)} *
\exp(0.851 * (x_i - \left| x_i \right|))
where :math:`x_i` is the element of the input.
Inputs:
- **x** (Tensor) - The input of FastGelu with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend``
Examples:
>>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> fast_gelu = nn.FastGelu()
>>> output = fast_gelu(x)
>>> print(output)
[[-1.5418735e-01 3.9921875e+00 -9.7473649e-06]
[ 1.9375000e+00 -1.0052517e-03 8.9824219e+00]]
"""
def __init__(self):
"""Initialize FastGelu."""
super(FastGelu, self).__init__()
self.fast_gelu = P.FastGeLU()
def construct(self, x):
return self.fast_gelu(x)
class Sigmoid(Cell):
r"""
Sigmoid activation function.
Applies sigmoid-type activation element-wise.
Sigmoid function is defined as:
.. math::
\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)},
where :math:`x_i` is the element of the input.
The picture about Sigmoid looks like this `Sigmoid <https://en.wikipedia.org/wiki/
Sigmoid_function#/media/File:Logistic-curve.svg>`_.
Inputs:
- **x** (Tensor) - The input of Sigmoid with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> sigmoid = nn.Sigmoid()
>>> output = sigmoid(x)
>>> print(output)
[0.2688 0.11914 0.5 0.881 0.7305 ]
"""
def __init__(self):
"""Initialize Sigmoid."""
super(Sigmoid, self).__init__()
self.sigmoid = P.Sigmoid()
def construct(self, x):
return self.sigmoid(x)
class PReLU(Cell):
r"""
PReLU activation function.
Applies the PReLU function element-wise.
PReLU is defined as:
.. math::
prelu(x_i)= \max(0, x_i) + w * \min(0, x_i),
where :math:`x_i` is an element of an channel of the input.
Here :math:`w` is a learnable parameter with a default initial value 0.25.
Parameter :math:`w` has dimensionality of the argument channel. If called without argument
channel, a single parameter :math:`w` will be shared across all channels.
The picture about PReLU looks like this `PReLU <https://en.wikipedia.org/wiki/
Activation_function#/media/File:Activation_prelu.svg>`_.
Args:
channel (int): The elements number of parameter.
It could be an int, and the value is 1 or the channels number of input tensor `x`. Default: 1.
w (Union[float, list, Tensor]): The initial value of parameter. It could be a float, a float list or
a tensor has the same dtype as the input tensor `x`. Default: 0.25.
Inputs:
- **x** (Tensor) - The input of PReLU with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same dtype and shape as the `x`.
Raises:
TypeError: If `channel` is not an int.
TypeError: If `w` is not one of a float, a float list, a float Tensor.
TypeError: If dtype of `x` is neither float16 nor float32.
ValueError: If the `x` is a 0-D or 1-D Tensor on Ascend.
ValueError: If `channel` is less than 1.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> x = Tensor(np.array([[[[0.1, 0.6], [0.9, 0.9]]]]), mindspore.float32)
>>> prelu = nn.PReLU()
>>> output = prelu(x)
>>> print(output)
[[[[0.1 0.6]
[0.9 0.9]]]]
"""
@cell_attr_register(attrs="")
def __init__(self, channel=1, w=0.25):
"""Initialize PReLU."""
super(PReLU, self).__init__()
validator.check_positive_int(channel, 'channel', self.cls_name)
if isinstance(w, (float, np.float32)):
tmp = np.empty((channel,), dtype=np.float32)
tmp.fill(w)
w = Tensor(tmp, dtype=mstype.float32)
elif isinstance(w, list):
if len(w) != channel:
raise ValueError(f"For '{self.cls_name}', the length of 'w' should be equal to the 'channel' when "
f"the 'w' is a list, but got the length of 'w': {len(w)}, the 'channel': {channel}.")
for i in w:
if not isinstance(i, (float, np.float32)):
raise ValueError(f"For '{self.cls_name}', all elements in 'w' should be "
f"float when the 'w' is a list, but got {i}.")
w = Tensor(w, dtype=mstype.float32)
elif isinstance(w, Tensor):
if w.dtype not in (mstype.float16, mstype.float32):
raise ValueError(f"For '{self.cls_name}', the dtype of 'w' should be float16 or "
f"float32 when the 'w' is a tensor, but got {w.dtype}.")
if len(w.shape) != 1 or w.shape[0] != channel:
raise ValueError(f"For '{self.cls_name}', the dimension of 'w' should be 1, and the elements number "
f"should be equal to the 'channel' when the 'w' is a tensor, "
f"but got 'w' shape {w.shape}, the 'channel' {channel}.")
else:
raise TypeError(f"For '{self.cls_name}', the 'w' only supported float, list and tensor, "
f"but got {type(w).__name__}.")
self.w = Parameter(w, name='a')
self.prelu = P.PReLU()
self.relu = P.ReLU()
self.assign = P.Assign()
def construct(self, x):
u = self.relu(self.w)
v = self.prelu(x, F.cast(u, x.dtype))
if self.training:
self.assign(self.w, u)
return v
class HSwish(Cell):
r"""
Hard swish activation function.
Applies hswish-type activation element-wise. The input is a Tensor with any valid shape.
Hard swish is defined as:
.. math::
\text{hswish}(x_{i}) = x_{i} * \frac{ReLU6(x_{i} + 3)}{6},
where :math:`x_{i}` is the :math:`i`-th slice in the given dimension of the input Tensor.
Inputs:
- **x** (Tensor) - The input of HSwish, data type must be float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> hswish = nn.HSwish()
>>> result = hswish(x)
>>> print(result)
[-0.3333 -0.3333 0 1.666 0.6665]
"""
def __init__(self):
"""Initialize HSwish."""
super(HSwish, self).__init__()
self.hswish = P.HSwish()
def construct(self, x):
return self.hswish(x)
class HSigmoid(Cell):
r"""
Hard sigmoid activation function.
Applies hard sigmoid activation element-wise. The input is a Tensor with any valid shape.
Hard sigmoid is defined as:
.. math::
\text{hsigmoid}(x_{i}) = max(0, min(1, \frac{x_{i} + 3}{6})),
where :math:`x_{i}` is the :math:`i`-th slice in the given dimension of the input Tensor.
Inputs:
- **input_x** (Tensor) - The input of HSigmoid. The shape is :math:`(N,*)` where :math:`*` means, any number of
additional dimensions.
Outputs:
Tensor, with the same type and shape as the `input_x`.
Raises:
TypeError: If `input_x` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> hsigmoid = nn.HSigmoid()
>>> result = hsigmoid(x)
>>> print(result)
[0.3333 0.1666 0.5 0.8335 0.6665]
"""
def __init__(self):
"""Initialize HSigmoid."""
super(HSigmoid, self).__init__()
self.hsigmoid = P.HSigmoid()
def construct(self, input_x):
return self.hsigmoid(input_x)
class LogSigmoid(Cell):
r"""
Logsigmoid activation function.
Applies logsigmoid activation element-wise. The input is a Tensor with any valid shape.
Logsigmoid is defined as:
.. math::
\text{logsigmoid}(x_{i}) = log(\frac{1}{1 + \exp(-x_i)}),
where :math:`x_{i}` is the element of the input.
Inputs:
- **x** (Tensor) - The input of LogSigmoid with data type of float16 or float32.
The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
Outputs:
Tensor, with the same type and shape as the `x`.
Raises:
TypeError: If dtype of `x` is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> net = nn.LogSigmoid()
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> output = net(x)
>>> print(output)
[-0.31326166 -0.12692806 -0.04858734]
"""
def __init__(self):
"""Initialize LogSigmoid."""
super(LogSigmoid, self).__init__()
self.mul = P.Mul()
self.exp = P.Exp()
self.add = P.Add()
self.rec = P.Reciprocal()
self.log = P.Log()
def construct(self, input_x):
neg_input = self.mul(input_x, -1)
exp_neg_input = self.exp(neg_input)
exp_neg_input_1 = self.add(exp_neg_input, 1)
rec_exp_neg_input_1 = self.rec(exp_neg_input_1)
ret = self.log(rec_exp_neg_input_1)
return ret
class SoftShrink(Cell):
r"""
Applies the soft shrinkage function elementwise.
.. math::
\text{SoftShrink}(x) =
\begin{cases}
x - \lambda, & \text{ if } x > \lambda \\
x + \lambda, & \text{ if } x < -\lambda \\
0, & \text{ otherwise }
\end{cases}
Args:
lambd: the :math:`\lambda` must be no less than zero value for the Softshrink formulation. Default: 0.5.
Inputs:
- **input_x** (Tensor) - The input of SoftShrink with data type of float16 or float32.
Any number of additional dimensions.
Outputs:
Tensor, has the same shape and data type as `input_x`.
Raises:
TypeError: If lambd is not a float.
TypeError: If input_x is not a Tensor.
TypeError: If dtype of input_x is neither float16 nor float32.
ValueError: If lambd is less than 0.
Supported Platforms:
``Ascend``
Examples:
>>> input_x = Tensor(np.array([[ 0.5297, 0.7871, 1.1754], [ 0.7836, 0.6218, -1.1542]]), mstype.float16)
>>> softshrink = nn.SoftShrink()
>>> output = softshrink(input_x)
>>> print(output)
[[ 0.02979 0.287 0.676 ]
[ 0.2837 0.1216 -0.6543 ]]
"""
def __init__(self, lambd=0.5):
super(SoftShrink, self).__init__()
self.softshrink = P.SoftShrink(lambd)
def construct(self, input_x):
output = self.softshrink(input_x)
return output
class HShrink(Cell):
r"""
Applies the hard shrinkage function element-wise, each element complies the follow function:
.. math::
\text{HardShrink}(x) =
\begin{cases}
x, & \text{ if } x > \lambda \\
x, & \text{ if } x < -\lambda \\
0, & \text{ otherwise }
\end{cases}
Args:
lambd (float): The value for the HardShrink formulation. Default: 0.5
Inputs:
- **input_x** (Tensor) - The input of HardShrink with data type of float16 or float32.
Outputs:
Tensor, the same shape and data type as the input.
Supported Platforms:
``Ascend``
Raises:
TypeError: If `lambd` is not a float.
TypeError: If dtype of `input_x` is neither float16 nor float32.
Examples:
>>> input_x = Tensor(np.array([[ 0.5, 1, 2.0],[0.0533,0.0776,-2.1233]]),mstype.float32)
>>> hshrink = nn.HShrink()
>>> output = hshrink(input_x)
>>> print(output)
[[ 0. 1. 2. ]
[ 0. 0. -2.1233]]
"""
def __init__(self, lambd=0.5):
super(HShrink, self).__init__()
self.hshrink = P.HShrink(lambd)
def construct(self, input_x):
return self.hshrink(input_x)
_activation = {
'softmax': Softmax,
'logsoftmax': LogSoftmax,
'relu': ReLU,
'relu6': ReLU6,
'tanh': Tanh,
'gelu': GELU,
'fast_gelu': FastGelu,
'elu': ELU,
'sigmoid': Sigmoid,
'prelu': PReLU,
'leakyrelu': LeakyReLU,
'hswish': HSwish,
'hsigmoid': HSigmoid,
'logsigmoid': LogSigmoid,
'softshrink': SoftShrink,
'hshrink': HShrink,
}
def get_activation(name, prim_name=None):
"""
Gets the activation function.
Args:
name (str): The name of the activation function.
Returns:
Function, the activation function.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> sigmoid = nn.get_activation('sigmoid')
>>> print(sigmoid)
Sigmoid<>
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if name is None:
return None
if name not in _activation:
raise KeyError(f"{msg_prefix} 'name' should be in {list(_activation.keys())}, but got {name}.")
return _activation[name]()