import torch.nn.functional as F class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = torch.flatten(x, 1) # flatten all dimensions except batch x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x net = Net()
I am taking a look at PyTorch Blitz and in the
conv1 layer we can see the
input_channels=3 because it’s the first image so it just has its 3 RGB channels and
Does that mean the number of filters I have are 6? In which case it’d mean the total number of feature maps I would get are
6*3==18? But if that is the case why in
conv2 am I plugging in
input_channels=6, shouldn’t I be plugging in 18 because that was the output from the previous Convolutional layer?