def learn_with_gradient_testing(fname):
matFileContent = scipy.io.loadmat(fname) # corresponding MAT file
x_train = np.array(matFileContent['Xtrain'].tolist())
t_train = np.array(matFileContent['Ytrain'].flatten())
d = x_train.shape[1]
hidden_layers_list = [10]
mlp = Mlp(hidden_layers_list, d, True)
stopping_criterion = Mlp.BasicStoppingCriterion(0.05, 10)
error_data = mlp.train_network(x_train, t_train, x_train, t_train,
stopping_criterion)
print "Error data:"
error_data = map(lambda x: repr(x), error_data)
print reduce(lambda x, y: x+'\n'+y, error_data)
print "Log error:"
print mlp.get_input_error(x_train, t_train)
try:
x_test = np.array(matFileContent['Xtest'].tolist())
t_test = np.array(matFileContent['Ytest'].flatten())
print "Test log error:"
print mlp.get_input_error(x_test, t_test)
except:
pass
def learn(argv):
fname = argv[1]
if len(argv) == 3:
hidden_layers_list = eval(argv[2])
else:
hidden_layers_list = [10]
matFileContent = scipy.io.loadmat(fname) # corresponding MAT file
x_train = np.array(matFileContent['TrainSet'].tolist())
t_train = np.array(matFileContent['TrainClass'].tolist())
x_valid = np.array(matFileContent['ValidSet'].tolist())
t_valid = np.array(matFileContent['ValidClass'].tolist())
d = x_train.shape[1]
mlp = Mlp(hidden_layers_list, d)
stopping_criterion = Mlp.EarlyStoppingCriterion(5, 1e-5)
#stopping_criterion = Mlp.BasicStoppingCriterion(0.001, 100)
(error_data, best_epoch) = mlp.train_network(x_train, t_train,
x_valid, t_valid, stopping_criterion)
lrate = defaults.LEARNING_RATE_DEFAULT
mterm = defaults.MOMENTUM_TERM_DEFAULT
terms = str(lrate)[2:]+"_"+str(mterm)[2:]
arch_desc = reduce(lambda x, y:str(x)+"_"+str(y),
hidden_layers_list, "")
plt_file = 'plots/errors_' + terms + arch_desc + '.png'
plot_network_errors(error_data, best_epoch, plt_file)
print "Train log error and accuracy:"
print mlp.get_input_error(x_train, t_train), \
mlp.get_accuracy(x_train, t_train), "%"
print "Valid log error and accuracy:"
print mlp.get_input_error(x_valid, t_valid), \
mlp.get_accuracy(x_valid, t_valid), "%"
x_test = np.array(matFileContent['TestSet'].tolist())
t_test = np.array(matFileContent['TestClass'].tolist())
print "Test log error and accuracy:"
print mlp.get_input_error(x_test, t_test), \
mlp.get_accuracy(x_test, t_test), "%"
pickle.dump(mlp, open('trained_network.dat', 'wb'))
