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 torch
import torch.nn as nn
import numpy as np
 
batch_size = 4
class_num = 6
inputs = 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=1
probs_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 numpy
np_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 torch
import torch.nn as nn
import numpy as np
 
batch_size = 4
class_num = 6
inputs = 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=1
probs_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 numpy
np_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 torch
import numpy as np
input = 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))))