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.nn.layer.activation import LogSigmoid
from ..distribution._utils.custom_ops import exp_generic, log_generic
from .bijector import Bijector


[docs]class Softplus(Bijector): r""" Softplus Bijector. This Bijector performs the operation: .. math:: Y = \frac{\log(1 + e ^ {kX})}{k} where k is the sharpness factor. Args: sharpness (float, list, numpy.ndarray, Tensor): The scale factor. Default: 1.0. name (str): The name of the Bijector. Default: 'Softplus'. Supported Platforms: ``Ascend`` ``GPU`` Note: The dtype of `sharpness` must be float. Raises: TypeError: When the dtype of the sharpness is not float. Examples: >>> import mindspore >>> import mindspore.nn as nn >>> import mindspore.nn.probability.bijector as msb >>> from mindspore import Tensor >>> >>> # To initialize a Softplus bijector of sharpness 2.0. >>> softplus = msb.Softplus(2.0) >>> # To use a ScalarAffine bijector in a network. >>> value = Tensor([1, 2, 3], dtype=mindspore.float32) >>> ans1 = softplus.forward(value) >>> print(ans1.shape) (3,) >>> ans2 = softplus.inverse(value) >>> print(ans2.shape) (3,) >>> ans3 = softplus.forward_log_jacobian(value) >>> print(ans3.shape) (3,) >>> ans4 = softplus.inverse_log_jacobian(value) >>> print(ans4.shape) (3,) """ def __init__(self, sharpness=1.0, name='Softplus'): """ Constructor of Softplus Bijector. """ param = dict(locals()) param['param_dict'] = {'sharpness': sharpness} super(Softplus, self).__init__(name=name, dtype=None, param=param) self._sharpness = self._add_parameter(sharpness, 'sharpness') self.exp = exp_generic self.log = log_generic self.expm1 = P.Expm1() self.abs = P.Abs() self.dtypeop = P.DType() self.cast = P.Cast() 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(self.dtypeop(x), 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, (-1) * self.threshold) too_small_value = self.log(x) too_large_value = x ones = self.fill(self.dtypeop(x), 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((-1)*x))) return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y)) @property def sharpness(self): return self._sharpness
[docs] def extend_repr(self): """Display instance object as string.""" if self.is_scalar_batch: str_info = 'sharpness = {}'.format(self.sharpness) else: str_info = 'batch_shape = {}'.format(self.batch_shape) return str_info
def _forward(self, x): x = self._check_value_dtype(x) sharpness_local = self.cast_param_by_value(x, self.sharpness) scaled_value = sharpness_local * x forward_v = self.softplus(scaled_value) / sharpness_local return forward_v 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_dtype(y) sharpness_local = self.cast_param_by_value(y, self.sharpness) scaled_value = sharpness_local * y inverse_v = self.inverse_softplus(scaled_value) / sharpness_local return inverse_v 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_dtype(x) sharpness_local = self.cast_param_by_value(x, self.sharpness) scaled_value = sharpness_local * x forward_log_j = self.log_sigmoid(scaled_value) return forward_log_j 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_dtype(y) sharpness_local = self.cast_param_by_value(y, self.sharpness) scaled_value = sharpness_local * y inverse_log_j = scaled_value - self.inverse_softplus(scaled_value) return inverse_log_j