def test_waldo():
"""
This method will run the test on waldo dataset.
"""
#dg = waldo(dimensions = (256,256)) # Initialize a dataset creator
dg = waldo() # Initialize a dataset creator
training_data, training_labels = dg.query_data(samples=1000)
testing_data, testing_labels = dg.query_data(samples=1000)
#dg._demo()
num = [5, 6, 7, 8, 9, 10, 20, 40, 50, 70, 100, 120, 140]
for i in range(len(num)):
print 'Hidden layers: ', num[i]
t0 = time.time()
n = mlnn(
training_data, training_labels,
num[i]) # This call should return a net object that is trained.
params = n.get_params(
) # This call should reaturn parameters of the model that are
# fully trained.
predictions = n.get_predictions(
testing_data) # This call should return predictions.
acc = accuracy(testing_labels, predictions)
print "Accuracy of predictions on waldo data = " + str(acc) + "%"
t1 = time.time()
print "Time taken: ", t1 - t0
return acc
def test_waldo():
"""
This method will run the test on waldo dataset.
"""
dg = waldo() # Initialize a dataset creator
training_data, training_labels = dg.query_data(samples=100)
n = mlnn(training_data, training_labels
) # This call should return a net object that is trained.
params = n.get_params(
) # This call should reaturn parameters of the model that are
# fully trained.
testing_data, testing_labels = dg.query_data(samples=100)
predictions = n.get_predictions(
testing_data) # This call should return predictions.
print "Accuracy of predictions on waldo data = " + str(
accuracy(testing_labels, predictions)) + "%"