#!/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')
