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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- # Python version: 3.6
- from torch import nn
- import torch.nn.functional as F
- class MLP(nn.Module):
- def __init__(self, dim_in, dim_hidden, dim_out):
- super(MLP, self).__init__()
- self.layer_input = nn.Linear(dim_in, dim_hidden)
- self.relu = nn.ReLU()
- self.dropout = nn.Dropout()
- self.layer_hidden = nn.Linear(dim_hidden, dim_out)
- self.softmax = nn.Softmax(dim=1)
- def forward(self, x):
- x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
- x = self.layer_input(x)
- x = self.dropout(x)
- x = self.relu(x)
- x = self.layer_hidden(x)
- return self.softmax(x)
- class CNNMnist(nn.Module):
- def __init__(self, args):
- super(CNNMnist, self).__init__()
- self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
- self.conv2_drop = nn.Dropout2d()
- self.fc1 = nn.Linear(320, 50)
- self.fc2 = nn.Linear(50, args.num_classes)
- def forward(self, x):
- x = F.relu(F.max_pool2d(self.conv1(x), 2))
- x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
- x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
- x = F.relu(self.fc1(x))
- x = F.dropout(x, training=self.training)
- x = self.fc2(x)
- return F.log_softmax(x, dim=1)
- class CNNFashion_Mnist(nn.Module):
- def __init__(self, args):
- super(CNNFashion_Mnist, self).__init__()
- self.layer1 = nn.Sequential(
- nn.Conv2d(1, 16, kernel_size=5, padding=2),
- nn.BatchNorm2d(16),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer2 = nn.Sequential(
- nn.Conv2d(16, 32, kernel_size=5, padding=2),
- nn.BatchNorm2d(32),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.fc = nn.Linear(7*7*32, 10)
- def forward(self, x):
- out = self.layer1(x)
- out = self.layer2(out)
- out = out.view(out.size(0), -1)
- out = self.fc(out)
- return out
- class CNNCifar(nn.Module):
- def __init__(self, args):
- super(CNNCifar, self).__init__()
- self.conv1 = nn.Conv2d(3, 6, 5)
- self.pool = nn.MaxPool2d(2, 2)
- self.conv2 = nn.Conv2d(6, 16, 5)
- self.fc1 = nn.Linear(16 * 5 * 5, 120)
- self.fc2 = nn.Linear(120, 84)
- self.fc3 = nn.Linear(84, args.num_classes)
- def forward(self, x):
- x = self.pool(F.relu(self.conv1(x)))
- x = self.pool(F.relu(self.conv2(x)))
- x = x.view(-1, 16 * 5 * 5)
- x = F.relu(self.fc1(x))
- x = F.relu(self.fc2(x))
- x = self.fc3(x)
- return F.log_softmax(x, dim=1)
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