def neural_network(dataset_train, dataset_test):
# Prendere la colonna Target del dataset
classiOutput = set()
for esempio in dataset_train:
classiOutput.add(esempio[len(esempio) - 1])
# Selezionare i Target senza duplicazione
listaClassi = []
for c in classiOutput:
listaClassi.append(c)
# Costruzione dell topologie della Rete
lng = len(dataset_train[0])
numeroInput = lng - 1
# Creo l'oggetto Network
rete = Network(numeroInput, listaClassi, math.tanh)
# Aggiunta degli strati
rete.add_hidden_layer(4)
# Lo strato di Out avrà 3 unità
rete.add_output_layer()
rete.train(dataset_train)
# Classificazione
giuste = 0
sbagliate = 0
lable_learning = list()
for test in dataset_test:
output_class = rete.classify(test[:len(test) - 1]) # Passaggio dell'input alla rete
lable_learning.append(output_class)
if output_class == test[len(test) - 1]:
giuste += 1
else:
sbagliate += 1
rete.close_report()
return lable_learning
from NeuralNetwork import Network zero = [0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0] one = [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0] two = [0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1] three = [1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1] exampleZero = [zero, [0, 0]] exampleOne = [one, [0, 1]] exampleTwo = [two, [1, 0]] exampleThree = [three, [1, 1]] examples = [exampleZero, exampleOne, exampleTwo, exampleThree] network = Network() network.addLayer(10, 20) network.addLayer(2, None) network.train(examples) print network.process(zero) print network.process(one) print network.process(two) print network.process(three)
from NeuralNetwork import Network
nn = Network('lastmodel')
nn.train(10000)
import mnist_loader
from NeuralNetwork import Network
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
net = Network([784, 30, 10])
net.train(training_data, 30, 10, 3.0, test_data=test_data)
net.save_configuration()
print("Done")
