https://docs.pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html
python3.11
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 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) # 5*5 from image dimension
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, input):
# Convolution layer C1: 1 input image channel, 6 output channels,
# 5x5 square convolution, it uses RELU activation function, and
# outputs a Tensor with size (N, 6, 28, 28), where N is the size of the batch
c1 = F.relu(self.conv1(input))
# Subsampling layer S2: 2x2 grid, purely functional,
# this layer does not have any parameter, and outputs a (N, 6, 14, 14) Tensor
s2 = F.max_pool2d(c1, (2, 2))
# Convolution layer C3: 6 input channels, 16 output channels,
# 5x5 square convolution, it uses RELU activation function, and
# outputs a (N, 16, 10, 10) Tensor
c3 = F.relu(self.conv2(s2))
# Subsampling layer S4: 2x2 grid, purely functional,
# this layer does not have any parameter, and outputs a (N, 16, 5, 5) Tensor
s4 = F.max_pool2d(c3, 2)
# Flatten operation: purely functional, outputs a (N, 400) Tensor
s4 = torch.flatten(s4, 1)
# Fully connected layer F5: (N, 400) Tensor input,
# and outputs a (N, 120) Tensor, it uses RELU activation function
f5 = F.relu(self.fc1(s4))
# Fully connected layer F6: (N, 120) Tensor input,
# and outputs a (N, 84) Tensor, it uses RELU activation function
f6 = F.relu(self.fc2(f5))
# Gaussian layer OUTPUT: (N, 84) Tensor input, and
# outputs a (N, 10) Tensor
output = self.fc3(f6)
return output
net = Net()
print(net)
params = list(net.parameters())
for i,j in enumerate(params):
print(params[i].size())
input = torch.randn(1, 1, 32, 32)
print(input)
output = net(input)
print(output)
target = torch.randn(10) # a dummy target, for example
print(target)
target = target.view(1, -1) # make it the same shape as output
print(target)
criterion = nn.MSELoss()
# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)
# in your training loop:
optimizer.zero_grad() # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
print(loss)
loss.backward()
optimizer.step() # Does the update
# 重み行列
print(net.conv1.weight.data)
print(net.conv2.weight.data)
print(net.fc1.weight.data)
print(net.fc2.weight.data)
print(net.fc3.weight.data)
# バイアス
print(net.conv1.bias.data)
print(net.conv2.bias.data)
print(net.fc1.bias.data)
print(net.fc2.bias.data)
print(net.fc3.bias.data)
