def __init__(self, gui_worker=None):
print('----> Loading cases...')
training_set = load_all_flat_cases('training')
testing_set = load_all_flat_cases('testing')
provided_datasets = [
training_set,
testing_set
]
self.net = Ann(
structure=[784, 200, 40, 10],
datasets=provided_datasets,
activation_function=[T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid],
learning_rate=0.2,
regression_layer=SumOfSquaredErrors,
gui_worker=gui_worker
)
self.gui_worker = gui_worker
from src.algorithms.ann.sum_of_squared_errors import SumOfSquaredErrors
from src.utils.mnist_basics import load_all_flat_cases, minor_demo
from src.algorithms.ann.ann import Ann
from theano import tensor as T
if __name__ == '__main__':
print('----> Loading cases...')
training_set = load_all_flat_cases('training')
testing_set = load_all_flat_cases('testing')
provided_datasets = [
training_set,
testing_set
]
net1 = Ann(
structure=[784, 150, 10],
datasets=provided_datasets,
activation_function=[T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid],
learning_rate=0.1,
regression_layer=SumOfSquaredErrors
)
net1.train(100)
minor_demo(net1)
net2 = Ann(
structure=[784, 150, 10],
datasets=provided_datasets,
activation_function=[T.nnet.sigmoid, T.nnet.sigmoid, T.nnet.sigmoid],
