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

# Copyright 2020 Huawei Technologies Co., Ltd
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"""Power Bijector"""
from .power_transform import PowerTransform


[docs]class Exp(PowerTransform): r""" Exponential Bijector. This Bijector performs the operation: .. math:: Y = \exp(x). Args: name (str): The name of the Bijector. Default: 'Exp'. Supported Platforms: ``Ascend`` ``GPU`` Examples: >>> import mindspore >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> import mindspore.context as context >>> context.set_context(mode=context.GRAPH_MODE) >>> >>> # To initialize an Exp bijector. >>> exp_bijector = nn.probability.bijector.Exp() >>> value = Tensor([1, 2, 3], dtype=mindspore.float32) >>> ans1 = exp_bijector.forward(value) >>> print(ans1.shape) (3,) >>> ans2 = exp_bijector.inverse(value) >>> print(ans2.shape) (3,) >>> ans3 = exp_bijector.forward_log_jacobian(value) >>> print(ans3.shape) (3,) >>> ans4 = exp_bijector.inverse_log_jacobian(value) >>> print(ans4.shape) (3,) """ def __init__(self, name='Exp'): super(Exp, self).__init__(name=name) def extend_repr(self): if self.is_scalar_batch: str_info = 'exp' else: str_info = f'batch_shape = {self.batch_shape}' return str_info