def __init__(self):
super(DeepNet2, self).__init__()
- nb_channels = 512
+ self.nb_channels = 512
self.conv1 = nn.Conv2d( 1, 32, kernel_size=7, stride=4, padding=3)
self.conv2 = nn.Conv2d( 32, nb_channels, kernel_size=5, padding=2)
self.conv3 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
self.conv5 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
- self.fc1 = nn.Linear(16 * nb_channels, 512)
+ self.fc1 = nn.Linear(16 * self.nb_channels, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 2)
x = fn.max_pool2d(x, kernel_size=2)
x = fn.relu(x)
- x = x.view(-1, 4096)
+ x = x.view(-1, 16 * self.nb_channels)
x = self.fc1(x)
x = fn.relu(x)