Source code for mindspore.nn.probability.bijector.gumbel_cdf

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


[文档]class GumbelCDF(Bijector): r""" GumbelCDF Bijector. This Bijector performs the operation: .. math:: Y = \exp(-\exp(\frac{-(X - loc)}{scale})) Args: loc (float, list, numpy.ndarray, Tensor): The location. Default: 0.0. scale (float, list, numpy.ndarray, Tensor): The scale. Default: 1.0. name (str): The name of the Bijector. Default: 'GumbelCDF'. Inputs and Outputs of APIs: The accessible APIs of the Gumbel_cdf bijector are defined in the base class, including: - **forward** - **inverse** - **forward_log_jacobian** - **inverse_log_jacobian** It should be notice that the inputs of APIs of the Gumbel_cdf bijector should be always a tensor, with a shape that can be broadcasted to that of `loc` and `scale`. For more details of all APIs, including the inputs and outputs of APIs of the Gumbel_cdf bijector, please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below. Supported Platforms: ``Ascend`` ``GPU`` Note: `scale` must be greater than zero. For `inverse` and `inverse_log_jacobian`, input should be in range of (0, 1). The dtype of `loc` and `scale` must be float. If `loc`, `scale` are passed in as numpy.ndarray or tensor, they have to have the same dtype otherwise an error will be raised. Raises: TypeError: When the dtype of `loc` or `scale` is not float, or when the dtype of `loc` and `scale` is not same. Examples: >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.bijector as msb >>> from mindspore import Tensor >>> >>> # To initialize a GumbelCDF bijector of loc 1.0, and scale 2.0. >>> gumbel_cdf = msb.GumbelCDF(1.0, 2.0) >>> # To use a GumbelCDF bijector in a network. >>> x = Tensor([1, 2, 3], dtype=mindspore.float32) >>> y = Tensor([0.1, 0.2, 0.3], dtype=mindspore.float32) >>> ans1 = gumbel_cdf.forward(x) >>> print(ans1.shape) (3,) >>> ans2 = gumbel_cdf.inverse(y) >>> print(ans2.shape) (3,) >>> ans3 = gumbel_cdf.forward_log_jacobian(x) >>> print(ans3.shape) (3,) >>> ans4 = gumbel_cdf.inverse_log_jacobian(y) >>> print(ans4.shape) (3,) """ def __init__(self, loc=0.0, scale=1.0, name='GumbelCDF'): """ Constructor of GumbelCDF Bijector. """ param = dict(locals()) param['param_dict'] = {'loc': loc, 'scale': scale} super(GumbelCDF, self).__init__(name=name, param=param) self._loc = self._add_parameter(loc, 'loc') self._scale = self._add_parameter(scale, 'scale') check_greater_zero(self._scale, "scale") self.cast = P.Cast() self.exp = exp_generic self.log = log_generic @property def loc(self): """ Return the loc parameter of the bijector. Output: Tensor, the loc parameter of the bijector. """ return self._loc @property def scale(self): """ Return the scale parameter of the bijector. Output: Tensor, the scale parameter of the bijector. """ return self._scale def extend_repr(self): """Display instance object as string.""" if self.is_scalar_batch: str_info = 'loc = {}, scale = {}'.format(self.loc, self.scale) else: str_info = 'batch_shape = {}'.format(self.batch_shape) return str_info def _forward(self, x): x = self._check_value_dtype(x) loc_local = self.cast_param_by_value(x, self.loc) scale_local = self.cast_param_by_value(x, self.scale) z = (x - loc_local) / scale_local return self.exp((-1) * self.exp(-z)) def _inverse(self, y): y = self._check_value_dtype(y) loc_local = self.cast_param_by_value(y, self.loc) scale_local = self.cast_param_by_value(y, self.scale) return loc_local - scale_local * self.log((-1) * self.log(y)) def _forward_log_jacobian(self, x): x = self._check_value_dtype(x) loc_local = self.cast_param_by_value(x, self.loc) scale_local = self.cast_param_by_value(x, self.scale) z = (x - loc_local) / scale_local return -z - self.exp(-z) - self.log(scale_local) def _inverse_log_jacobian(self, y): y = self._check_value_dtype(y) scale_local = self.cast_param_by_value(y, self.scale) return self.log(scale_local / (-1. * y * self.log(y)))