41 lines
1.3 KiB
Python
Executable File
41 lines
1.3 KiB
Python
Executable File
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import numpy as np
|
|
from math import ceil
|
|
|
|
def conv_init(m):
|
|
classname = m.__class__.__name__
|
|
if classname.find('Conv') != -1:
|
|
#nn.init.xavier_uniform(m.weight, gain=np.sqrt(2))
|
|
nn.init.normal_(m.weight, mean=0, std=1)
|
|
nn.init.constant(m.bias, 0)
|
|
|
|
class LeNet(nn.Module):
|
|
def __init__(self, num_classes, inputs=3, wide=2):
|
|
super(LeNet, self).__init__()
|
|
self.conv1 = nn.Conv2d(inputs, ceil(6*wide), 5)
|
|
self.conv2 = nn.Conv2d(ceil(6*wide), ceil(16*wide), 5)
|
|
self.fc1 = nn.Linear(ceil(16*5*5*wide), ceil(120*wide))
|
|
self.fc2 = nn.Linear(ceil(120*wide), 84)
|
|
self.fc3 = nn.Linear(84, num_classes)
|
|
|
|
def forward(self, x):
|
|
out = F.relu(self.conv1(x))
|
|
#print(out.size())
|
|
out = F.max_pool2d(out, 2)
|
|
#print(out.size())
|
|
out = F.relu(self.conv2(out))
|
|
#print(out.size())
|
|
out = F.max_pool2d(out, 2)
|
|
#print(out.size())
|
|
out = out.view(out.size(0), -1)
|
|
#print(out.size())
|
|
out = F.relu(self.fc1(out))
|
|
#print(out.size())
|
|
out = F.relu(self.fc2(out))
|
|
#print(out.size())
|
|
out = self.fc3(out)
|
|
#print(out.size())
|
|
#print("END")
|
|
return(out)
|