images = utils.read_images(base_dir)
images = utils.setup_images(images, image_size)
uniq_classes = set([cl for _, cl in images])
classes = {cl: i for i, cl in enumerate(uniq_classes)}
im_size = len(images)
input_array = numpy.zeros((im_size, in_layer_size), "float")
output_array = numpy.zeros((im_size, out_layer_size), "float") - 1.0
print "Setup network arrays..."
for i in range(im_size):
(image, fl) = images[i]
a = numpy.array(list(image), "float")
print "#%d image from dir(class): " % i + fl
input_array[i, :] = a.flatten()
output_array[i, classes[fl]] = 1.0
print input_array[i, :]
print output_array[i, :]
layers = (in_layer_size, hidden_layer_size, out_layer_size)
network = NeuralNetwork(layers)
network.train(input_array, output_array, True)
print images[0][0]
a = numpy.array(list(images[0][0]), "float")
cc = numpy.zeros((im_size, in_layer_size), "float")
cc[0, :] = a.flatten()
res = network.classify(cc)
print res[0].shape
from support import Dataset
from neural import NeuralNetwork
from genetic import TournamentGeneticAlgorithm
MAX_ITER = 10000
POPULATION_SIZE = 30
K = 3
if __name__ == '__main__':
data = Dataset("zad7-dataset.txt")
layer_arch = [2, 8, 4, 3]
network = NeuralNetwork(layer_arch)
ga = TournamentGeneticAlgorithm(network, data, K, POPULATION_SIZE,
MAX_ITER)
best_params = ga.run()
network.classify(data, best_params)