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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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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.fc1 = nn.Linear(28*28, 128) |
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self.fc2 = nn.Linear(128, 128) |
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self.fc3 = nn.Linear(128, 64) |
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self.fc4 = nn.Linear(64, 10) |
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def forward(self, x): |
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x = x.view(x.shape[0], -1) |
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x = torch.relu(self.fc1(x)) |
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x = torch.relu(self.fc2(x)) |
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x = torch.relu(self.fc3(x)) |
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return self.fc4(x) |
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class NetConv(nn.Module): |
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def __init__(self): |
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super(NetConv, self).__init__() |
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self.conv1 = nn.Conv2d(1, 32, 3) |
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self.conv2 = nn.Conv2d(32, 64, 3) |
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self.fc1 = nn.Linear(64 * 5 * 5, 128) |
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self.fc2 = nn.Linear(128, 10) |
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def forward(self, x): |
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x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) |
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x = F.max_pool2d(F.relu(self.conv2(x)), 2) |
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x = x.view(-1, self.num_flat_features(x)) |
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x = F.relu(self.fc1(x)) |
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x = self.fc2(x) |
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return F.log_softmax(x, dim=1) |
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def num_flat_features(self, x): |
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size = x.size()[1:] |
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num_features = 1 |
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for s in size: |
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num_features *= s |
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return num_features |
