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import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import torchvision.transforms as transforms | |
from torchvision.datasets import CIFAR10 | |
from torch.utils.data import DataLoader | |
class AlexNet(nn.Module): | |
def __init__(self, num_classes=10): | |
super(AlexNet, self).__init__() | |
self.features = nn.Sequential( | |
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), | |
nn.ReLU(inplace=True), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
nn.Conv2d(64, 192, kernel_size=5, padding=2), | |
nn.ReLU(inplace=True), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
nn.Conv2d(192, 384, kernel_size=3, padding=1), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(384, 256, kernel_size=3, padding=1), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(256, 256, kernel_size=3, padding=1), | |
nn.ReLU(inplace=True), | |
nn.MaxPool2d(kernel_size=3, stride=2), | |
) | |
self.avgpool = nn.AdaptiveAvgPool2d((6, 6)) | |
self.classifier = nn.Sequential( | |
nn.Dropout(), | |
nn.Linear(256 * 6 * 6, 4096), | |
nn.ReLU(inplace=True), | |
nn.Dropout(), | |
nn.Linear(4096, 4096), | |
nn.ReLU(inplace=True), | |
nn.Linear(4096, num_classes), | |
) | |
def forward(self, x): | |
x = self.features(x) | |
x = self.avgpool(x) | |
x = torch.flatten(x, 1) | |
x = self.classifier(x) | |
return x |