pytorch神经网络之卷积层与全连接层参数的设置方法

class AlexNet(nn.Module):
  def __init__(self):
    super(AlexNet, self).__init__()

    self.conv = nn.Sequential(
      nn.Conv2d(3, 96, kernel_size=11, stride=4),
      nn.ReLU(inplace=True),
      nn.MaxPool2d(kernel_size=3, stride=2),

      nn.Conv2d(96, 256, kernel_size=5, padding=2),
      nn.ReLU(inplace=True),
      nn.MaxPool2d(kernel_size=3, stride=2),

      nn.Conv2d(256, 384, kernel_size=3, padding=1),
      nn.ReLU(inplace=True),
      nn.Conv2d(384, 384, kernel_size=3, padding=1),
      nn.ReLU(inplace=True),
      nn.Conv2d(384, 256, kernel_size=3, padding=1),
      nn.ReLU(inplace=True),
      nn.MaxPool2d(kernel_size=3, stride=2)
    )

    self.fc = nn.Sequential(
      nn.Linear(？？？, 4096)
      ......
      ......
    )

  def forward(self, x):
    x = self.conv(x)
    print x.size()

import torch
from Alexnet.AlexNet import *
from torch.autograd import Variable

if __name__ == '__main__':
  net = AlexNet()

  data_input = Variable(torch.randn([1, 3, 96, 96])) # 这里假设输入图片是96x96
  print data_input.size()
  net(data_input)

(1L, 3L, 96L, 96L)
(1L, 256L, 1L, 1L)