mindspore.nn.probability.bijector.invert 源代码

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"""Invert Bijector"""
from mindspore import _checkparam as validator
from .bijector import Bijector


[文档]class Invert(Bijector): r""" Invert Bijector. Compute the inverse function of the input bijector. If the function of the forward mapping, namely the input of `bijector` below, is :math:`Y = g(X)`, then the function of corresponding inverse mapping Bijector is :math:`Y = h(X) = g^{-1}(X)`. Args: bijector (Bijector): Base Bijector. name (str): The name of the Bijector. Default: "". When name is set to "", it is actually 'Invert' + bijector.name. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.bijector as msb >>> from mindspore import Tensor >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.origin = msb.ScalarAffine(scale=2.0, shift=1.0) ... self.invert = msb.Invert(self.origin) ... ... def construct(self, x_): ... return self.invert.forward(x_) >>> forward = Net() >>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32) >>> ans = forward(Tensor(x, dtype=mindspore.float32)) >>> print(ans.shape) (4,) """ def __init__(self, bijector, name=""): param = dict(locals()) validator.check_value_type('bijector', bijector, [Bijector], "Invert") name = name or ('Invert' + bijector.name) param["name"] = name super(Invert, self).__init__(is_constant_jacobian=bijector.is_constant_jacobian, is_injective=bijector.is_injective, name=name, dtype=bijector.dtype, param=param) self._bijector = bijector self._batch_shape = self.bijector.batch_shape self._is_scalar_batch = self.bijector.is_scalar_batch @property def bijector(self): """Return base bijector.""" return self._bijector
[文档] def inverse(self, y): """ Perform the inverse transformation of the inverse bijector, namely the forward transformation of the underlying bijector. Args: y (Tensor): the value of the transformed random variable. Output: Tensor, the value of the input random variable. """ return self.bijector("forward", y)
[文档] def forward(self, x): """ Perform the forward transformation of the inverse bijector, namely the inverse transformation of the underlying bijector. Args: x (Tensor): the value of the input random variable. Output: Tensor, the value of the transformed random variable. """ return self.bijector("inverse", x)
[文档] def inverse_log_jacobian(self, y): """ Logarithm of the derivative of the inverse transformation of the inverse bijector, namely logarithm of the derivative of the forward transformation of the underlying bijector. Args: y (Tensor): the value of the transformed random variable. Output: Tensor, logarithm of the derivative of the inverse transformation of the inverse bijector. """ return self.bijector("forward_log_jacobian", y)
[文档] def forward_log_jacobian(self, x): """ Logarithm of the derivative of the forward transformation of the inverse bijector, namely logarithm of the derivative of the inverse transformation of the underlying bijector. Args: x (Tensor): the value of the input random variable. Output: Tensor, logarithm of the derivative of the forward transformation of the inverse bijector. """ return self.bijector("inverse_log_jacobian", x)