自学教程：Pytorch中Softmax和LogSoftmax的使用详解

class Softmax(Module):    r"""Applies the Softmax function to an n-dimensional input Tensor    rescaling them so that the elements of the n-dimensional output Tensor    lie in the range [0,1] and sum to 1.    Softmax is defined as:    .. math::        /text{Softmax}(x_{i}) = /frac{/exp(x_i)}{/sum_j /exp(x_j)}    Shape:        - Input: :math:`(*)` where `*` means, any number of additional          dimensions        - Output: :math:`(*)`, same shape as the input    Returns:        a Tensor of the same dimension and shape as the input with        values in the range [0, 1]    Arguments:        dim (int): A dimension along which Softmax will be computed (so every slice            along dim will sum to 1).    .. note::        This module doesn't work directly with NLLLoss,        which expects the Log to be computed between the Softmax and itself.        Use `LogSoftmax` instead (it's faster and has better numerical properties).    Examples::        >>> m = nn.Softmax(dim=1)        >>> input = torch.randn(2, 3)        >>> output = m(input)    """    __constants__ = ['dim']     def __init__(self, dim=None):        super(Softmax, self).__init__()        self.dim = dim     def __setstate__(self, state):        self.__dict__.update(state)        if not hasattr(self, 'dim'):            self.dim = None     def forward(self, input):        return F.softmax(input, self.dim, _stacklevel=5)     def extra_repr(self):        return 'dim={dim}'.format(dim=self.dim)

class LogSoftmax(Module):    r"""Applies the :math:`/log(/text{Softmax}(x))` function to an n-dimensional    input Tensor. The LogSoftmax formulation can be simplified as:    .. math::        /text{LogSoftmax}(x_{i}) = /log/left(/frac{/exp(x_i) }{ /sum_j /exp(x_j)} /right)    Shape:        - Input: :math:`(*)` where `*` means, any number of additional          dimensions        - Output: :math:`(*)`, same shape as the input    Arguments:        dim (int): A dimension along which LogSoftmax will be computed.    Returns:        a Tensor of the same dimension and shape as the input with        values in the range [-inf, 0)    Examples::        >>> m = nn.LogSoftmax()        >>> input = torch.randn(2, 3)        >>> output = m(input)    """    __constants__ = ['dim']     def __init__(self, dim=None):        super(LogSoftmax, self).__init__()        self.dim = dim     def __setstate__(self, state):        self.__dict__.update(state)        if not hasattr(self, 'dim'):            self.dim = None     def forward(self, input):        return F.log_softmax(input, self.dim, _stacklevel=5)

import torchimport torch.nn as nnimport numpy as np batch_size = 4class_num = 6inputs = torch.randn(batch_size, class_num)for i in range(batch_size):    for j in range(class_num):        inputs[i][j] = (i + 1) * (j + 1) print("inputs:", inputs)

Softmax = nn.Softmax(dim=1)probs = Softmax(inputs)print("probs:/n", probs)

LogSoftmax = nn.LogSoftmax(dim=1)log_probs = LogSoftmax(inputs)print("log_probs:/n", log_probs)

# probs_sum in dim=1probs_sum = [0 for i in range(batch_size)] for i in range(batch_size):    for j in range(class_num):        probs_sum[i] += probs[i][j]    print(i, "row probs sum:", probs_sum[i])

# to numpynp_probs = probs.data.numpy()print("numpy probs:/n", np_probs) # np.log()log_np_probs = np.log(np_probs)print("log numpy probs:/n", log_np_probs)

import torchimport torch.nn as nnimport numpy as np batch_size = 4class_num = 6inputs = torch.randn(batch_size, class_num)for i in range(batch_size):    for j in range(class_num):        inputs[i][j] = (i + 1) * (j + 1) print("inputs:", inputs)Softmax = nn.Softmax(dim=1)probs = Softmax(inputs)print("probs:/n", probs) LogSoftmax = nn.LogSoftmax(dim=1)log_probs = LogSoftmax(inputs)print("log_probs:/n", log_probs) # probs_sum in dim=1probs_sum = [0 for i in range(batch_size)] for i in range(batch_size):    for j in range(class_num):        probs_sum[i] += probs[i][j]    print(i, "row probs sum:", probs_sum[i]) # to numpynp_probs = probs.data.numpy()print("numpy probs:/n", np_probs) # np.log()log_np_probs = np.log(np_probs)print("log numpy probs:/n", log_np_probs)

import torchimport numpy as npinput = torch.autograd.Variable(torch.rand(1, 3))print(input)print('softmax={}'.format(torch.nn.functional.softmax(input, dim=1)))print('logsoftmax={}'.format(np.log(torch.nn.functional.softmax(input, dim=1))))