/
neuralNetworkCIFAR.py
57 lines (52 loc) · 1.82 KB
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neuralNetworkCIFAR.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class CIFAR10_NET(nn.Module):
def __init__(self):
super(CIFAR10_NET, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Dropout(0.2))
self.conv2 = nn.Sequential(
nn.Conv2d(96, 96, kernel_size=3, stride=1, padding=1),
nn.ReLU())
self.conv3 = nn.Sequential(
nn.Conv2d(96, 96, kernel_size=3, stride=2, padding=1),
nn.ReLU())
self.conv4 = nn.Sequential(
nn.Conv2d(96, 192, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Dropout(0.5))
self.conv5 = nn.Sequential(
nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),
nn.ReLU())
self.conv6 = nn.Sequential(
nn.Conv2d(192, 192, kernel_size=3, stride=2, padding=1),
nn.ReLU())
self.conv7 = nn.Sequential(
nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Dropout(0.5))
self.conv8 = nn.Sequential(
nn.Conv2d(192, 192, kernel_size=1, stride=1),
nn.ReLU())
self.conv9 = nn.Sequential(
nn.Conv2d(192, 10, kernel_size=1, stride=1),
nn.ReLU())
self.avgPool1 = nn.AvgPool2d(kernel_size=6, stride=1)
self.fc1 = nn.Linear(3 * 3 * 10, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.avgPool1(x)
x = x.view(-1, 90)
x = self.fc1(x)
return F.softmax(x, dim=1)