Pytorch入門-ロジスティック回帰

Pytorchでロジスティック回帰

from sklearn.datasets import load_iris
iris = load_iris()

X = iris.data[:100]
y = iris.target[:100]

X = torch.tensor(X, dtype=torch.float32)
y = torch.tensor(y, dtype=torch.float32)

net = nn.Linear(4, 1)

loss_fn = nn.BCEWithLogitsLoss()

optimizer = optim.SGD(net.parameters(), lr=0.25)

losses = []

for epoch in range(100):
    
    optimizer.zero_grad()
    
    y_pred = net(X)
    
    loss = loss_fn(y_pred.view_as(y), y)
    loss.backward()
    
    optimizer.step()
    losses.append(loss.item())
    
    print("epoch: %d , loss: %f" % (epoch+1, loss))
    
    if(epoch != 0):
        if(abs(losses[epoch-1] - losses[epoch]) < 0.00001):
            break
    
%matplotlib inline
from matplotlib import pyplot as plt
plt.plot(losses)
plt.savefig('losses.png')
plt.show()

出力

参考