File size: 1,410 Bytes
ffbb48e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
import torch.nn as nn
class AlexNet(nn.Module):
def __init__(self, num_classes=3):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv3d(1, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=3, stride=2),
nn.Conv3d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=3, stride=2),
nn.Conv3d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv3d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv3d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
self.reset_parameters()
def reset_parameters(self):
for weight in self.parameters():
weight.data.uniform_(-0.1, 0.1)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), 256 * 6 * 6 * 6)
x = self.classifier(x)
return x
|