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| # model.py | |
| import torch.nn as nn | |
| # neural network architecture | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.conv1 = nn.Conv2d(1, 10, kernel_size=5) | |
| self.conv2 = nn.Conv2d(10, 20, kernel_size=5) | |
| self.dropout = nn.Dropout2d() | |
| self.fc1 = nn.Linear(320, 50) | |
| self.fc2 = nn.Linear(50, 10) | |
| def forward(self, x): | |
| x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2)) | |
| x = nn.functional.relu(nn.functional.max_pool2d(self.dropout(self.conv2(x)), 2)) | |
| x = x.view(-1, 320) | |
| x = nn.functional.relu(self.fc1(x)) | |
| x = nn.functional.dropout(x, training=self.training) | |
| x = self.fc2(x) | |
| return nn.functional.log_softmax(x, dim=1) | |
| model = Net() | |