43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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import torch.nn as nn
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from layers.misc import FlattenLayer
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def conv_init(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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#nn.init.xavier_uniform(m.weight, gain=np.sqrt(2))
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nn.init.normal_(m.weight, mean=0, std=1)
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nn.init.constant(m.bias, 0)
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class ThreeConvThreeFC(nn.Module):
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"""
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To train on CIFAR-10:
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https://arxiv.org/pdf/1207.0580.pdf
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"""
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def __init__(self, outputs, inputs):
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super(ThreeConvThreeFC, self).__init__()
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self.features = nn.Sequential(
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nn.Conv2d(inputs, 32, 5, stride=1, padding=2),
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nn.Softplus(),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(32, 64, 5, stride=1, padding=2),
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nn.Softplus(),
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nn.MaxPool2d(kernel_size=3, stride=2),
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nn.Conv2d(64, 128, 5, stride=1, padding=1),
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nn.Softplus(),
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nn.MaxPool2d(kernel_size=3, stride=2),
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)
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self.classifier = nn.Sequential(
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FlattenLayer(2 * 2 * 128),
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nn.Linear(2 * 2 * 128, 1000),
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nn.Softplus(),
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nn.Linear(1000, 1000),
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nn.Softplus(),
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nn.Linear(1000, outputs)
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)
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def forward(self, x):
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x = self.features(x)
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x = self.classifier(x)
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return x
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