35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
from torch import nn
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import torch.nn.functional as F
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class Network(nn.Module):
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def __init__(self):
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super().__init__()
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding='same')
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self.conv2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding='same')
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self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding='same')
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self.conv4 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding='same')
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self.fc1 = nn.Linear(2048, 1024)
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self.fc2 = nn.Linear(1024, 128)
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self.fc3 = nn.Linear(128, 10)
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(0.3)
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def forward(self, x):
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x = self.relu(self.conv1(x))
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x = self.relu(self.conv2(x))
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x = self.pool(x)
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x = self.dropout(x)
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x = self.relu(self.conv3(x))
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x = self.relu(self.conv4(x))
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x = self.pool(x)
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x = self.dropout(x)
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x = x.reshape((x.shape[0], -1))
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x = self.relu(self.fc1(x))
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x = self.dropout(x)
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x = self.relu(self.fc2(x))
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x = self.dropout(x)
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x = self.fc3(x)
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return x
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