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