[PyTorch] Logistic Regression & Softmax

 

 

 

[Logistic]

활성화 함수 : sigmoid

loss function : binary cross entropy

 

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

torch.manual_seed(1)

class BinaryClassifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2,1)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self,x):
        return self.sigmoid(self.linear(x))
    
x_data = [[1,2],[2,3],[3,1],[4,3],[5,3],[6,2]]
y_data = [[0],[0],[0],[1],[1],[1]]

model = BinaryClassifier()
optimizer = optim.SGD(model.parameters(), lr = 1)

x_train = torch.FloatTensor(x_data)
y_train = torch.FloatTensor(y_data)

for epoch in range(1001):
    prediction = model(x_train)
    loss = F.binary_cross_entropy(prediction, y_train)
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    print(loss.item())

 

[softmax & cross-entropy]

 

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

x_train = [[1, 2, 1, 1],
           [2, 1, 3, 2],
           [3, 1, 3, 4],
           [4, 1, 5, 5],
           [1, 7, 5, 5],
           [1, 2, 5, 6],
           [1, 6, 6, 6],
           [1, 7, 7, 7]]
y_train = [2, 2, 2, 1, 1, 1, 0, 0]

x_train = torch.FloatTensor(x_train)
y_train = torch.LongTensor(y_train)

class softmaxClassifierModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(4,3)
    
    def forward(self, x):
        return self.linear(x)

model = softmaxClassifierModel()
optimizer = optim.SGD(model.parameters(), lr = 0.1)
        
nb_epochs = 1000
for epoch in range(nb_epochs + 1):

    prediction = model(x_train)
    
    # softmax 연산과 one-hot encoding 생략
    loss = F.cross_entropy(prediction, y_train)
    
    # cost로 H(x) 개선
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 100번마다 로그 출력
    if epoch % 100 == 0:
        print('Epoch {:4d}/{} Cost: {:.6f}'.format(
            epoch, nb_epochs, loss.item()
        ))

 

 

 

 

출처 : https://youtu.be/HgPWRqtg254