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2dfdcd4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import torch
import torch.nn as nn
import torch.optim as optim
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(64, 128, kernel_size=5, padding=2)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.relu3 = nn.ReLU()
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv4 = nn.Conv2d(256, 384, kernel_size=5, padding=1)
self.relu4 = nn.ReLU()
self.conv5 = nn.Conv2d(384, 256, kernel_size=1, padding=0)
self.relu5 = nn.ReLU()
self.avgpool = nn.AdaptiveAvgPool2d((3, 3))
self.flatten = nn.Flatten()
self.dropout1 = nn.Dropout(p=0.1)
self.fc1 = nn.Linear(256 * 3 * 3, 1024)
self.relu6 = nn.ReLU()
self.dropout2 = nn.Dropout(p=0.1)
self.fc2 = nn.Linear(1024, 512)
self.relu7 = nn.ReLU()
self.fc3 = nn.Linear(512, 200)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = self.pool1(self.relu1(self.conv1(x)))
x = self.pool2(self.relu2(self.conv2(x)))
x = self.pool3(self.relu3(self.conv3(x)))
x = self.relu4(self.conv4(x))
x = self.relu5(self.conv5(x))
x = self.avgpool(x)
x = self.flatten(x)
x = self.dropout1(x)
x = self.relu6(self.fc1(x))
x = self.dropout2(x)
x = self.relu7(self.fc2(x))
x = self.fc3(x)
x = self.softmax(x)
return x
model = CNNModel()
loss_fn = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0003) |