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https://github.com/kuhyx/WUT_Computer_Science.git
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59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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# load the dataset, split into input (X) and output (y) variables
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dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
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X = dataset[:,0:8]
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y = dataset[:,8]
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X = torch.tensor(X, dtype=torch.float32)
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y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)
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# define the model
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class PimaClassifier(nn.Module):
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def __init__(self):
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super().__init__()
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self.hidden1 = nn.Linear(8, 12)
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self.act1 = nn.ReLU()
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self.hidden2 = nn.Linear(12, 8)
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self.act2 = nn.ReLU()
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self.output = nn.Linear(8, 1)
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self.act_output = nn.Sigmoid()
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def forward(self, x):
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x = self.act1(self.hidden1(x))
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x = self.act2(self.hidden2(x))
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x = self.act_output(self.output(x))
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return x
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model = PimaClassifier()
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print(model)
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# train the model
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loss_fn = nn.BCELoss() # binary cross entropy
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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n_epochs = 100
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batch_size = 10
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for epoch in range(n_epochs):
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for i in range(0, len(X), batch_size):
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Xbatch = X[i:i+batch_size]
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y_pred = model(Xbatch)
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ybatch = y[i:i+batch_size]
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loss = loss_fn(y_pred, ybatch)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# compute accuracy
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y_pred = model(X)
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accuracy = (y_pred.round() == y).float().mean()
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print(f"Accuracy {accuracy}")
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# make class predictions with the model
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predictions = (model(X) > 0.5).int()
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for i in range(5):
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print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i])) |