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()