33 lines
896 B
Python
Executable File
33 lines
896 B
Python
Executable File
#!/usr/bin/python3
|
|
|
|
import torch
|
|
import pandas as pd
|
|
|
|
|
|
data = pd.read_csv('iris.data',sep = ',', header = None)
|
|
data[5] = data[4].apply(lambda x: 1 if x == 'Iris-versicolor' else 0)
|
|
|
|
x1 = torch.tensor(data[0], dtype=torch.float)
|
|
x0 = torch.ones(x1.size(0))
|
|
x = torch.stack((x0, x1)).transpose(0, 1)
|
|
y = torch.tensor(data[5], dtype=torch.float)
|
|
|
|
w = torch.rand(2, requires_grad=True)
|
|
|
|
learning_rate = torch.tensor(0.005)
|
|
|
|
for _ in range(3000):
|
|
ypredicted = torch.nn.functional.sigmoid(x @ w)
|
|
|
|
# cost = torch.sum((ypredicted - y) ** 2)
|
|
cost = - (torch.sum(y*torch.log(ypredicted) + (torch.ones_like(y) - y) * torch.log(1- ypredicted)))
|
|
accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
|
|
print(w, " => ", cost, " => ", accuracy, '% accuracy')
|
|
|
|
cost.backward()
|
|
|
|
with torch.no_grad():
|
|
w = w - learning_rate * w.grad
|
|
|
|
w.requires_grad_(True)
|