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221350e3a4
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132352 Approved by: https://github.com/ezyang ghstack dependencies: #132335, #132351
22 lines
680 B
Python
22 lines
680 B
Python
import torch.nn as nn
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import torch.nn.functional as F
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class MNIST(nn.Module):
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def __init__(self) -> None:
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super().__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, dim=1)
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