mindspore.nn.probability.bijector.power_transform 源代码

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"""PowerTransform Bijector"""
from mindspore.ops import operations as P
from ..distribution._utils.utils import check_greater_equal_zero
from ..distribution._utils.custom_ops import exp_generic, log_generic
from .bijector import Bijector


[文档]class PowerTransform(Bijector): r""" PowerTransform Bijector. This Bijector performs the operation: .. math:: Y = g(X) = (1 + X * c)^{1 / c}, X >= -1 / c where c >= 0 is the power. The power transform maps inputs from `[-1/c, inf]` to `[0, inf]`. This Bijector is equivalent to the :class:`mindspore.nn.probability.bijector.Exp` bijector when `c=0`. Args: power (float, list, numpy.ndarray, Tensor): The scale factor. Default: 0. name (str): The name of the bijector. Default: 'PowerTransform'. Note: The dtype of `power` must be float. Raises: ValueError: When `power` is less than 0 or is not known statically. TypeError: When the dtype of `power` is not float. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.bijector as msb >>> from mindspore import Tensor >>> # To initialize a PowerTransform bijector of power 0.5. >>> powertransform = msb.PowerTransform(0.5) >>> value = Tensor([1, 2, 3], dtype=mindspore.float32) >>> ans1 = powertransform.forward(value) >>> print(ans1.shape) (3,) >>> ans2 = powertransform.inverse(value) >>> print(ans2.shape) (3,) >>> ans3 = powertransform.forward_log_jacobian(value) >>> print(ans3.shape) (3,) >>> ans4 = powertransform.inverse_log_jacobian(value) >>> print(ans4.shape) (3,) """ def __init__(self, power=0., name='PowerTransform'): param = dict(locals()) param['param_dict'] = {'power': power} super(PowerTransform, self).__init__(name=name, param=param) self._power = self._add_parameter(power, 'power') check_greater_equal_zero(self._power, 'Power') self.pow = P.Pow() self.dtypeop = P.DType() self.cast = P.Cast() self.equal_base = P.Equal() self.exp = exp_generic self.expm1 = P.Expm1() self.fill = P.Fill() self.log = log_generic self.log1p = P.Log1p() self.select_base = P.Select() self.shape = P.Shape() @property def power(self): """ Return the power parameter of the bijector. Returns: Tensor, the power parameter of the bijector. """ return self._power def extend_repr(self): """Display instance object as string.""" if self.is_scalar_batch: str_info = 'power = {}'.format(self.power) else: str_info = 'batch_shape = {}'.format(self.batch_shape) return str_info def _forward(self, x): """ Evaluate the forward mapping. """ x = self._check_value_dtype(x) power_local = self.cast_param_by_value(x, self.power) # broad cast the value of x and power ones = self.fill(self.dtypeop(power_local), self.shape(x + power_local), 1.) power_local = power_local * ones x = x * ones safe_power = self.select_base(self.equal_base(power_local, P.ZerosLike()(power_local)), ones, power_local) forward_v = self.select_base(self.equal_base(power_local, P.ZerosLike()(power_local)), self.exp(x), self.exp(self.log1p(x * safe_power) / safe_power)) return forward_v def _inverse(self, y): """ Evaluate the inverse mapping. """ y = self._check_value_dtype(y) power_local = self.cast_param_by_value(y, self.power) # broad cast the value of x and power ones = self.fill(self.dtypeop(power_local), self.shape(y + power_local), 1.) power_local = power_local * ones y = y * ones safe_power = self.select_base(self.equal_base(power_local, P.ZerosLike()(power_local)), ones, power_local) inverse_v = self.select_base(self.equal_base(power_local, P.ZerosLike()(power_local)), self.log(y), self.expm1(self.log(y) * safe_power) / safe_power) return inverse_v def _forward_log_jacobian(self, x): r""" .. math: if c == 0: f(x) = e^x f'(x) = e^x \log(f'(x)) = \log(e^x) = x else: f(x) = e^\frac{\log(xc + 1)}{c} f'(x) = e^\frac{\log(xc + 1)}{c} * \frac{1}{xc + 1} \log(f'(x)) = (\frac{1}{c} - 1) * \log(xc + 1) """ x = self._check_value_dtype(x) power_local = self.cast_param_by_value(x, self.power) # broad cast the value of x and power ones = self.fill(self.dtypeop(power_local), self.shape(x + power_local), 1.) power_local = power_local * ones x = x * ones forward_log_j = self.select_base(self.equal_base(power_local, P.ZerosLike()(power_local)), x, (1. / power_local - 1) * self.log1p(x * power_local)) return forward_log_j def _inverse_log_jacobian(self, y): r""" .. math: if c == 0: f(x) = \log(x) f'(x) = \frac{1}{x} \log(f'(x)) = \log(\frac{1}{x}) = -\log(x) else: f(x) = \frac{e^\log(y)*c + 1}{c} f'(x) = \frac{e^c\log(y)}{y} \log(f'(x)) = \log(\frac{e^c\log(y)}{y}) = (c-1) * \log(y) """ y = self._check_value_dtype(y) power_local = self.cast_param_by_value(y, self.power) inverse_log_j = (power_local - 1) * self.log(y) return inverse_log_j