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example.py
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example.py
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from time import time
import torch
import sklearn.datasets
import sklearn.preprocessing
import sklearn.model_selection
import numpy as np
import onlinehd
# loads simple mnist dataset
def load():
# fetches data
x, y = sklearn.datasets.fetch_openml('mnist_784', return_X_y=True)
x = x.astype(np.float)
y = y.astype(np.int)
# split and normalize
x, x_test, y, y_test = sklearn.model_selection.train_test_split(x, y)
scaler = sklearn.preprocessing.Normalizer().fit(x)
x = scaler.transform(x)
x_test = scaler.transform(x_test)
# changes data to pytorch's tensors
x = torch.from_numpy(x).float()
y = torch.from_numpy(y).long()
x_test = torch.from_numpy(x_test).float()
y_test = torch.from_numpy(y_test).long()
return x, x_test, y, y_test
# simple OnlineHD training
def main():
print('Loading...')
x, x_test, y, y_test = load()
classes = y.unique().size(0)
features = x.size(1)
model = onlinehd.OnlineHD(classes, features)
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
x_test = x_test.cuda()
y_test = y_test.cuda()
model = model.to('cuda')
print('Using GPU!')
print('Training...')
t = time()
model = model.fit(x, y, bootstrap=1.0, lr=0.035, epochs=20)
t = time() - t
print('Validating...')
yhat = model(x)
yhat_test = model(x_test)
acc = (y == yhat).float().mean()
acc_test = (y_test == yhat_test).float().mean()
print(f'{acc = :6f}')
print(f'{acc_test = :6f}')
print(f'{t = :6f}')
if __name__ == '__main__':
main()