| import torch.nn as nn |
| import torch.nn.functional as F |
| |
| |
| class MNIST(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.conv1 = nn.Conv2d(1, 10, kernel_size=5) |
| self.conv2 = nn.Conv2d(10, 20, kernel_size=5) |
| self.conv2_drop = nn.Dropout2d() |
| self.fc1 = nn.Linear(320, 50) |
| self.fc2 = nn.Linear(50, 10) |
| |
| def forward(self, x): |
| x = F.relu(F.max_pool2d(self.conv1(x), 2)) |
| x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) |
| x = x.view(-1, 320) |
| x = F.relu(self.fc1(x)) |
| x = F.dropout(x, training=self.training) |
| x = self.fc2(x) |
| return F.log_softmax(x, dim=1) |
