use_validation = False
nb_input = 2
nb_hidden = 10
nb_labels = 2
nb_iters = 10000
nb_epochs = 1
alpha = 3.0
lbda = 0.0
momentum = 0.0
precision = 1e-6
timer = time.clock if (sys.platform == 'win32') else time.time
Plots.scatterplot(X, Y)
for nb_hidden in range(1, 7):
nn = NeuralNetwork(nb_input, nb_hidden, nb_labels, cost_function='cross_entropy')
a,b,c,d = [],[],[],[]
for i in range(nb_epochs):
X_train, Y_train = X, Y
Y_vec = Utils.vectorize_output(Y_train, nb_labels)
print('Epoch %d' % (i+1))
print("-"*30)
nn.initialize_weights()
return self.train_binary(X, Y, alpha, lbda, precision, nb_iters,
verbose)
if __name__ == '__main__':
# X, Y = Reader.read_data('dados/xor.txt', ignore_line_number=False)
# data = {'X':X, 'Y':Y}
# Reader.save_mat('dados/xor.mat', data)
mat = Reader.load_mat('dados/xor.mat')
X_orig, Y = np.matrix(mat['X']), mat['Y']
# X, mu, sigma = Features.normalize(X)
Plots.scatterplot(X_orig, Y)
pyplot.show()
max_degree = 60
iters, times, accs, alphas = [], [], [], []
result_times = open("reg_result_times.txt", "w")
result_times.write("alpha tempo\n")
result_iters = open("reg_result_iters.txt", "w")
result_iters.write("alpha iters\n")
result_accs = open("reg_result_accs.txt", "w")
result_accs.write("alpha acc\n")
result_alphas = open("reg_result_alphas.txt", "w")
if __name__ == '__main__':
# X, Y = Reader.read_data('dados/xor.txt', ignore_line_number=False)
# data = {'X':X, 'Y':Y}
# Reader.save_mat('dados/xor.mat', data)
mat = Reader.load_mat('dados/xor.mat')
X_orig, Y = np.matrix(mat['X']), mat['Y']
# X, mu, sigma = Features.normalize(X)
Plots.scatterplot(X_orig, Y)
pyplot.show()
max_degree = 60
iters, times, accs, alphas = [], [], [], []
result_times = open("reg_result_times.txt", "w")
result_times.write("alpha tempo\n")
result_iters = open("reg_result_iters.txt", "w")
result_iters.write("alpha iters\n")
result_accs = open("reg_result_accs.txt", "w")
result_accs.write("alpha acc\n")
use_validation = False
nb_input = 2
nb_hidden = 10
nb_labels = 2
nb_iters = 10000
nb_epochs = 1
alpha = 3.0
lbda = 0.0
momentum = 0.0
precision = 1e-6
timer = time.clock if (sys.platform == 'win32') else time.time
Plots.scatterplot(X, Y)
for nb_hidden in range(1, 7):
nn = NeuralNetwork(nb_input,
nb_hidden,
nb_labels,
cost_function='cross_entropy')
a, b, c, d = [], [], [], []
for i in range(nb_epochs):
X_train, Y_train = X, Y
Y_vec = Utils.vectorize_output(Y_train, nb_labels)
print('Epoch %d' % (i + 1))