def run_multiclass():
from data_loader import toy_data_multiclass_3_classes_non_separable, \
toy_data_multiclass_5_classes, \
data_loader_mnist
datasets = [(toy_data_multiclass_3_classes_non_separable(),
'Synthetic data', 3),
(toy_data_multiclass_5_classes(), 'Synthetic data', 5),
(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
print('One-versus-rest:')
w, b = OVR_train(X_train, y_train, C=num_classes)
train_preds = OVR_predict(X_train, w=w, b=b)
preds = OVR_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('Multinomial:')
w, b = multinomial_train(X_train, y_train, C=num_classes)
train_preds = multinomial_predict(X_train, w=w, b=b)
preds = multinomial_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)))
return preds
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def accuracy_score(true, preds):
return np.sum(true == preds).astype(float) / len(true)
from data_loader import toy_data_multiclass_3_classes_non_separable, \
toy_data_multiclass_5_classes, \
data_loader_mnist
datasets = [(toy_data_multiclass_3_classes_non_separable(), 'Synthetic data',
3), (toy_data_multiclass_5_classes(), 'Synthetic data', 5),
(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
print('One-versus-rest:')
w, b = OVR_train(X_train, y_train, C=num_classes)
train_preds = OVR_predict(X_train, w=w, b=b)
preds = OVR_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)))
