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# Import needed packages | |
import torch | |
import torch.nn as nn | |
class SimpleNet(nn.Module): | |
def __init__(self, num_classes=10): | |
super(SimpleNet, self).__init__() | |
self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1) | |
self.relu1 = nn.ReLU() | |
self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=3, stride=1, padding=1) | |
self.relu2 = nn.ReLU() | |
self.pool = nn.MaxPool2d(kernel_size=2) | |
self.conv3 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1) | |
self.relu3 = nn.ReLU() | |
self.conv4 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, stride=1, padding=1) | |
self.relu4 = nn.ReLU() | |
self.fc = nn.Linear(in_features=16 * 16 * 24, out_features=num_classes) | |
def forward(self, input): | |
output = self.conv1(input) | |
output = self.relu1(output) | |
output = self.conv2(output) | |
output = self.relu2(output) | |
output = self.pool(output) | |
output = self.conv3(output) | |
output = self.relu3(output) | |
output = self.conv4(output) | |
output = self.relu4(output) | |
output = output.view(-1, 16 * 16 * 24) | |
output = self.fc(output) | |
return output |
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