딥러닝, 야 너도 할 수 있어(feat. PyTorch)
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※다운로드하시면 더 선명한 자료를 보실 수 있습니다. 딥러닝을 처음 시작하시는 분, 특히 개발자에게 맞춰 필요한 내용을 준비하였습니다. 딥러닝의 수학적인 이해보다는 딥러닝을 활용하는 방법을 공유하고자 합니다. 목차 1. 딥러닝 권장 개발 환경 2. 코드 작성을 위한 약간의 이론 3. 가장 빠르게 그럴싸한 모델 만들기 대상 딥러닝에 관심이 있거나 딥러닝을 시작하려는 모든 개발자 ※발표 영상은 제공하지 않습니다.
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딥러닝, 야 너도 할 수 있어(feat. PyTorch)
- 18. Hours Score 10 90 9 80 3 50 2 30
- 19. (“ ” – ” ”)& Model 1 𝐻( 𝑥 = 6.6𝑥 + 22.9 Model 2 𝐻& 𝑥 = 5.5𝑥 + 34.5 Model 3 𝐻3 𝑥 = 8.25𝑥 + 23 𝐱 𝐲 𝐇 𝟏 𝐱 (𝐇 𝟏 𝐱 − 𝐲) 𝟐 𝐇 𝟐 𝐱 (𝐇 𝟐 𝐱 − 𝐲) 𝟐 𝐇 𝟑 𝐱 (𝐇 𝟑 𝐱 − 𝐲) 𝟐
- 20. 0 20 40 60 80 100 0 5 10 15 𝐇 𝐱 = 𝐚𝐱 + 𝐛 𝐇 𝐱 = 𝐰 𝟏×𝐱 + 𝐰 𝟎×𝟏 𝐖 = 𝐰 𝟏 𝐰 𝟎 𝐗 = 𝐱 𝟏 𝐇 𝐱 = 𝐖𝐗 𝐖 𝐖
- 21. 𝛁𝐋𝐨𝐬𝐬 𝐋𝐨𝐬𝐬 𝐰 𝛂 𝐰 𝐖 𝟏 𝐦 ∑𝐢K𝟏 𝐦 𝐖×𝐗 𝐢 − 𝐲 𝐢 𝟐 (예측 값 – 실제 값)& 𝐇 𝐱 = 𝐖𝐗 𝐦𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝑳𝒐𝒔𝒔 𝐰 𝐖
- 22. !"#$$ "#$$ % & % 𝛁𝐋𝐨𝐬𝐬 𝐖 = 𝐖 − 𝛂𝛁𝐋𝐨𝐬𝐬
- 23. 𝝈 𝒙 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑥 = 1 1 + e] 𝐇 𝒙 = 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧_𝐟𝐧(𝒘 𝟎 + 𝒘 𝟏 𝒙 𝟏 + ⋯ + 𝒘 𝒏 𝒙 𝒏) 𝑥f bias(wl) 𝐻 𝑥 𝑥( 𝚺 𝝈 𝑥& 𝝈 𝒙 = 𝑟𝑒𝑙𝑢 𝑥 = r 𝑥 = 0 (𝑥 < 0) 𝑥 = 𝑥 (𝑥 ≥ 0)
- 24. 𝐇 𝒙 = 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 (𝒘 𝟎 + 𝒘 𝟏 𝒙 𝟏 + ⋯ + 𝒘 𝒏 𝒙 𝒏) = 𝟏 𝟏 + 𝐞(∑ 𝒘 𝒊 𝒙 𝒊) 𝑥f w( bias(wl) H 𝑥𝚺 𝝈 𝝈 𝒙 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑥 = 1 1 + e] 𝑥( 𝑤f 𝑥( 𝑥& 𝑥f 𝑥&
- 25. 𝑥f w( bias(wl) H 𝑥𝚺 𝝈 𝑥( 𝑤f class MyModel(torch.nn.Module): def __init__(self): super(MyScoreModel, self).__init__() self.weights = torch.nn.Parameter(torch.randn(n)) self.bias = torch.nn.Parameter(torch.randn(1)) self.activation_fn = torch.nn.Sigmoid() def forward(self, x): output = self.weights * x + self.bias output = self.activation_fn(output) return output
- 26. 𝑥f bias 𝐻( 𝑥 𝑥( class MyModel(torch.nn.Module): def __init__(self, input_num=n, output_num=1): super(MyScoreModel, self).__init__() self.linear = torch.nn.Linear(in_features=n, out_features=1, bias = True) self.activation_fn = torch.nn.Sigmoid() def forward(self, x): output = self.linear(x) output = self.activation_fn(output) return output
- 29. 𝑥f 𝑥( Weights 𝑦& 𝑦( Weights Weights class MyModel(torch.nn.Module): def __init__(self): super(MyScoreModel, self).__init__() self.linear1 = torch.nn.Linear(in_features=n, out_features =m) self.linear2 = torch.nn.Linear(in_features=m, out_features =p) self.linear3 = torch.nn.Linear(in_features=p, out_features =2) self.activation_fn = torch.nn.Sigmoid() def forward(self, x): output = self.activation_fn(self.linear1(x)) output = self.activation_fn(self.linear2(output)) output = self.linear3(output) return output 𝑜(( 𝑜(• 𝑜&( 𝑜&‚
- 37. 𝑤( 𝑤& 𝑤3 𝑤{ 𝝈
- 40. class ConvModel(torch.nn.Module): def __init__(self): super().__init__() self.conv1 = torch.nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3) self.pool = torch.nn.MaxPool2d(kernel_size=2) self.conv2 = torch.nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3) self.fc1 = torch.nn.Linear(in_features=11*11*32, out_features=128) self.fc2 = torch.nn.Linear(in_features=128, out_features=10) self.relu = torch.nn.ReLU() def forward(self, x): x = self.conv1(x) x = self.relu(x) x = self.pool(x) x = self.conv2(x) x = self.relu(x) x = x.view(x.shape[0], -1) x = self.fc1(x) x = self.relu(x) x. = self.fc2(x) return x
- 48. 0 0 0
- 50. class FoodDataset(torch.utils.data.Dataset): def __init__(self, datafile): self.samples = [] lines = open(datafile).read().splitlines() for line in lines: path, label = line.split(‘:’) self.samples.append((path, label)) def __getitem__(self, idx): path, label = self.samples[idx] image = cv2.imread(path) ... return image, float(label) def __len__(self): return len(self.samples)
- 51. train_dataset = FoodDataset('dataset/data.list’) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) image_converter = transforms.ToPILImage() for images, labels in train_loader: label = str(labels[0].numpy()) image = image_converter(images[0]) print (label, id_to_names[label]) plt.imshow(image) plt.show()
- 52. fc1000 import torchvision.models as models class FoodModel(torch.nn.Module): def __init__(self): super().__init__() self.model = models.resnet18(pretrained=True) self.model.fc = torch.nn.Linear(512, 20) def forward(self, x): return self.model(x)