def run_multiclass():
from data_loader import smile_dataset_clear, \
smile_dataset_blur, \
data_loader_mnist
import time
datasets = [(smile_dataset_clear(), 'Clear smile data', 3),
(smile_dataset_blur(), 'Blur smile data', 3),
(data_loader_mnist(), 'MNIST', 10)]
for data, name, num_classes in datasets:
print('%s: %d class classification' % (name, num_classes))
X_train, X_test, y_train, y_test = data
for gd_type in ["sgd", "gd"]:
s = time.time()
w, b = sol.multiclass_train(X_train,
y_train,
C=num_classes,
gd_type=gd_type)
print(gd_type + ' training time: %0.6f seconds' %
(time.time() - s))
train_preds = sol.multiclass_predict(X_train, w=w, b=b)
preds = sol.multiclass_predict(X_test, w=w, b=b)
print('train acc: %f, test acc: %f' % (accuracy_score(
y_train, train_preds), accuracy_score(y_test, preds)))
print()
from data_loader import smile_dataset_clear, \
smile_dataset_blur, \
data_loader_mnist
import time
datasets = [(smile_dataset_clear(), 'Clear smile data', 3),
(smile_dataset_blur(), 'Blur smile data', 3),
(data_loader_mnist(), 'MNIST', 10)]
for data, name, num_classes in datasets:
print('%s: %d class classification' % (name, num_classes))
X_train, X_test, y_train, y_test = data