| class CNN(nn.Module): | |
| def __init__(self): | |
| super(CNN, self).__init__() | |
| self.relu = nn.ReLU() | |
| self.maxpool = nn.MaxPool2d(kernel_size = 2, stride = 2) | |
| self.conv1 = nn.Conv2d(3,32,3,stride = 1, padding = 1) | |
| self.conv2 = nn.Conv2d(32,64,3,stride = 1, padding = 1) | |
| self.conv3 = nn.Conv2d(64,128,3,stride = 1, padding = 1) | |
| self.conv4 = nn.Conv2d(128,256,3,stride = 1, padding = 1) | |
| self.dropout = nn.Dropout(p = 0.5) | |
| self.fc1 = nn.Linear(14*14*256, 4096) | |
| self.fc2 = nn.Linear(4096,1024) | |
| self.fc3 = nn.Linear(1024, 10) | |
| def forward(self, x): | |
| x = self.maxpool(self.relu(self.conv1(x))) | |
| x = self.maxpool(self.relu(self.conv2(x))) | |
| x = self.maxpool(self.relu(self.conv3(x))) | |
| x = self.maxpool(self.relu(self.conv4(x))) | |
| x = x.view(-1, 14*14*256) | |
| x = self.dropout(self.relu(self.fc1(x))) | |
| x = self.dropout(self.relu(self.fc2(x))) | |
| x = self.fc3(x) | |
| return x | |
| model = CNN().to(device) | |
| criterion = nn.CrossEntropyLoss() | |
| optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate) | |
| ############## TENSORBOARD ######################## | |
| writer.add_graph(model, example_data.to(device)) | |
| writer.close() |