#!/usr/bin/env python # -*- coding: utf-8 -*- __author__ = 'Will Brennan' # Built-in Modules # Standard Modules import theano # Custom Modules import Scripts import DeepConv if __name__ == '__main__': args = Scripts.get_args() logger = Scripts.get_logger(quiet=args.quiet, debug=args.debug) data = Scripts.get_mnist() data = Scripts.normalise(data) x, x_test, y, y_test = Scripts.sklearn2theano(data) classifier = DeepConv.DeepConv(save=args.save, load=args.load, debug=args.debug) classifier.fit(data=x, labels=y, test_data=x_test, test_labels=y_test, n_epochs=args.n_epochs, batch_size=args.batch_size) y_pred = classifier.predict(x_test) classifier.score_report(y_test=y_test, y_pred=y_pred) logger.info('Classifier Scoring: {0}'.format( classifier.score(x_test, y_test))) Scripts.confusion_matrix(y_test, y_pred)
#!/usr/bin/env python # -*- coding: utf-8 -*- __author__ = 'Will Brennan' # Built-in Modules # Standard Modules import theano # Custom Modules import Scripts import DeepConv from sklearn.externals import joblib if __name__ == '__main__': args = Scripts.get_args() logger = Scripts.get_logger(quiet=args.quiet, debug=args.debug) data = Scripts.get_mnist() data = Scripts.normalise(data) x, x_test, y, y_test = Scripts.sklearn2theano(data) classifier = DeepConv.DeepConv(args) classifier.fit(data=x, labels=y, test_data=x_test, test_labels=y_test, n_epochs=args.n_epochs, batch_size=args.batch_size) y_pred = classifier.predict(x_test) classifier.score_report(y_test=y_test, y_pred=y_pred) logger.info('Classifier Scoring: {0}'.format(classifier.score(x_test, y_test))) joblib.dumps(classifier, 'digits.pkl')