The document discusses the development of machine learning models using PyTorch, including details on neural network architectures such as linear models, convolutional networks, and the use of activation functions like sigmoid and ReLU. It highlights the process of model training and evaluation on a food dataset sourced from an external file, along with the utilization of data loaders for handling batches of images. Additionally, it references the adaptation of pre-trained models for custom outputs tailored to the specific dataset.

12. https://jupyter.org

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
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0 5 10 15
𝐇 𝐱 = 𝐚𝐱 + 𝐛
𝐇 𝐱 = 𝐰 𝟏×𝐱 + 𝐰 𝟎×𝟏
𝐖 = 𝐰 𝟏 𝐰 𝟎 𝐗 =
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𝐖×𝐗 𝐢 − 𝐲 𝐢 𝟐
(예측 값 – 실제 값)&
𝐇 𝐱 = 𝐖𝐗
𝐦𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝑳𝒐𝒔𝒔 𝐰
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𝛁𝐋𝐨𝐬𝐬
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23. 𝝈 𝒙 = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑥 =
1
1 + e]
𝐇 𝒙 = 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧_𝐟𝐧(𝒘 𝟎 + 𝒘 𝟏 𝒙 𝟏 + ⋯ + 𝒘 𝒏 𝒙 𝒏)
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bias(wl)
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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]
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25. 𝑥f
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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

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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
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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

45. https://github.com/karansikka1/iFood_2019

47. ImageNet Food Dataset

48. 0
0
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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)