1.预构建网络

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class Net(nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    # 1 input image channel, 6 output channels, 5*5 square convolution
    # kernel
 
    self.conv1 = nn.Conv2d(1, 6, 5)
    self.conv2 = nn.Conv2d(6, 16, 5)
    # an affine operation: y = Wx + b
    self.fc1 = nn.Linear(16 * 5 * 5, 120)
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84, 10)
 
  def forward(self, x):
    # max pooling over a (2, 2) window
    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
    # If size is a square you can only specify a single number
    x = F.max_pool2d(F.relu(self.conv2(x)), 2)
    x = x.view(-1, self.num_flat_features(x))
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x
 
  def num_flat_features(self, x):
    size = x.size()[1:] # all dimensions except the batch dimension
    num_features = 1
    for s in size:
      num_features *= s
    return num_features
 
net = Net()

新闻标题：Pytorch之parameters的使用-创新互联
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