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models.py
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models.py
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import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 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, 10)
def forward(self, x, get_features = False):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
# save features of this layer for expirement
x_features = x.view(-1, 320)
x = F.relu(self.fc1(x_features))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
if get_features:
return (F.log_softmax(x, dim=1), x_features)
else:
return F.log_softmax(x, dim=1)