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

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"""Softplus Bijector"""
import numpy as np
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from mindspore.nn.layer.activation import LogSigmoid
from mindspore._checkparam import Validator as validator
from ..distribution._utils.utils import cast_to_tensor
from ..distribution._utils.custom_ops import exp_generic, expm1_generic, log_generic
from .bijector import Bijector


[docs]class Softplus(Bijector): r""" Softplus Bijector. This Bijector performs the operation, where k is the sharpness factor. .. math:: Y = \frac{\log(1 + e ^ {kX})}{k} Args: sharpness (float): scale factor. Default: 1.0. name (str): name of the bijector. Default: 'Softplus'. Examples: >>> # To initialize a Softplus bijector of sharpness 2 >>> softplus = nn.probability.bijector.Softfplus(2) >>> >>> # To use ScalarAffine bijector in a network >>> class net(Cell): >>> def __init__(self): >>> super(net, self).__init__(): >>> self.sp1 = nn.probability.bijector.Softflus(2) >>> >>> def construct(self, value): >>> # Similar calls can be made to other probability functions >>> # by replacing 'forward' with the name of the function >>> ans1 = self.sp1.forward(value) >>> ans2 = self.sp1.inverse(value) >>> ans3 = self.sp1.forward_log_jacobian(value) >>> ans4 = self.sp1.inverse_log_jacobian(value) """ def __init__(self, sharpness=1.0, name='Softplus'): param = dict(locals()) validator.check_value_type('sharpness', sharpness, [int, float], type(self).__name__) super(Softplus, self).__init__(name=name, param=param) self._sharpness = cast_to_tensor(sharpness) self.exp = exp_generic self.log = log_generic self.expm1 = expm1_generic self.abs = P.Abs() self.fill = P.Fill() self.greater = P.Greater() self.less = P.Less() self.log_sigmoid = LogSigmoid() self.logicalor = P.LogicalOr() self.select = P.Select() self.shape = P.Shape() self.sigmoid = P.Sigmoid() self.softplus = self._softplus self.inverse_softplus = self._inverse_softplus self.threshold = np.log(np.finfo(np.float32).eps) + 1 self.tiny = np.exp(self.threshold) def _softplus(self, x): too_small = self.less(x, self.threshold) too_large = self.greater(x, -self.threshold) too_small_value = self.exp(x) too_large_value = x ones = self.fill(mstype.float32, self.shape(x), 1.0) too_small_or_too_large = self.logicalor(too_small, too_large) x = self.select(too_small_or_too_large, ones, x) y = self.log(self.exp(x) + 1.0) return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y)) def _inverse_softplus(self, x): r""" .. math:: f(x) = \frac{\log(1 + e^{x}))} f^{-1}(y) = \frac{\log(e^{y} - 1)} """ too_small = self.less(x, self.tiny) too_large = self.greater(x, -self.threshold) too_small_value = self.log(x) too_large_value = x ones = self.fill(mstype.float32, self.shape(x), 1.0) too_small_or_too_large = self.logicalor(too_small, too_large) x = self.select(too_small_or_too_large, ones, x) y = x + self.log(self.abs(self.expm1(-x))) return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y)) @property def sharpness(self): return self._sharpness def extend_repr(self): str_info = f'sharpness = {self.sharpness}' return str_info def shape_mapping(self, shape): return shape def _forward(self, x): x = self._check_value(x, 'value') scaled_value = self.sharpness * x return self.softplus(scaled_value) / self.sharpness def _inverse(self, y): r""" .. math:: f(x) = \frac{\log(1 + e^{kx}))}{k} f^{-1}(y) = \frac{\log(e^{ky} - 1)}{k} """ y = self._check_value(y, 'value') scaled_value = self.sharpness * y return self.inverse_softplus(scaled_value) / self.sharpness def _forward_log_jacobian(self, x): r""" .. math: f(x) = \log(1 + e^{kx}) / k f'(x) = \frac{e^{kx}}{ 1 + e^{kx}} \log(f'(x)) = kx - \log(1 + e^{kx}) = kx - f(kx) """ x = self._check_value(x, 'value') scaled_value = self.sharpness * x return self.log_sigmoid(scaled_value) def _inverse_log_jacobian(self, y): r""" .. math: f(y) = \frac{\log(e^{ky} - 1)}{k} f'(y) = \frac{e^{ky}}{e^{ky} - 1} \log(f'(y)) = ky - \log(e^{ky} - 1) = ky - f(ky) """ y = self._check_value(y, 'value') scaled_value = self.sharpness * y return scaled_value - self.inverse_softplus(scaled_value)