def main():
train_set, valid_set, train_labels, valid_labels = get_dataset.main()
neural_net = new_neuralnet(train_set)
iteracoes_grid = int(sys.argv[1])
iteracoes_train = int(sys.argv[2])
# Treinando
batch_size= 256
learning_rate, lamb = grid_search(new_neuralnet, train_set, train_labels, iteracoes_grid)
print_acuracia = True
neural_net.train_neuralnet(train_set, train_labels, valid_set, valid_labels, lamb, learning_rate,batch_size,iteracoes_train, print_acuracia, 'nn_onehidden')
neural_net.save_model("exp3_onehidden.npy")
def main():
train_set, valid_set, train_labels, valid_labels = get_dataset.main()
neural_softmax = new_neuralnet(train_set)
iteracoes_grid = int(sys.argv[1])
iteracoes_train = int(sys.argv[2])
# Treinando
batch_size = 256
learning_rate, lamb = grid_search(new_neuralnet, train_set, train_labels, iteracoes_grid)
print_acuracia = True
neural_softmax.train_neuralnet(train_set, train_labels, valid_set, valid_labels, lamb, learning_rate, batch_size, iteracoes_train, print_acuracia, 'softmax_regression')
neural_softmax.save_model("softmax.npy")
def main():
train_set, valid_set, train_labels, valid_labels = get_dataset.main()
X = train_set
y = train_labels
Xv = valid_set
yv = valid_labels
iteracoes_grid = int(sys.argv[1])
iteracoes_train = int(sys.argv[2])
batch_size = 256
print_acc = True
alpha = 0.02
lamb = 0.001
alpha, lamb = grid_search(OneVsAllClassifier, X, y, iteracoes_grid)
#print("Vamos fazer one vs all no toy set!")
cl = OneVsAllClassifier(X)
cl.train_neuralnet(X, y, Xv, yv, alpha, lamb, batch_size, iteracoes_train,
print_acc, 'oneVall')