42 lines
970 B
Python
42 lines
970 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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x = torch.tensor(data[[0,1]].values, dtype=torch.float)
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y = torch.tensor(data[5], dtype=torch.float)
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y = y.reshape(100,1)
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class Network(torch.nn.Module):
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def __init__(self):
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super(Network, self).__init__()
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self.fc = torch.nn.Linear(2,1)
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def forward(self,x):
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x = self.fc(x)
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x = torch.nn.functional.sigmoid(x)
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return x
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network = Network()
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optimizer = torch.optim.SGD(network.parameters(), lr=0.002)
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criterion = torch.nn.BCELoss()
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for _ in range(3000):
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optimizer.zero_grad()
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ypredicted = network(x)
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loss = criterion(ypredicted,y)
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accuracy = 100 * sum((ypredicted > 0.5) == y).item() / len(ypredicted)
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print('{:.3}'.format(loss.item()), "\t => ", accuracy, '% accuracy')
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loss.backward()
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optimizer.step()
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