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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.optim as optim |
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class Net(nn.Module): |
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def __init__(self): |
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super(Net, self).__init__() |
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self.conv1 = nn.Conv2d(1, 10, kernel_size=5) |
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5) |
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self.conv2_drop = nn.Dropout2d() |
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self.fc1 = nn.Linear(320, 50) |
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self.fc2 = nn.Linear(50, 10) |
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def forward(self, x): |
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x = F.relu(F.max_pool2d(self.conv1(x), 2)) |
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x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) |
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x = x.view(-1, 320) |
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x = F.relu(self.fc1(x)) |
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x = F.dropout(x, training=self.training) |
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x = self.fc2(x) |
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return F.log_softmax(x) |