print('Params: ' + str(len(mpl.params)))
print(mpl.size)
print('')
print('Cost Function: ' + str(mpl.cost_function(mpl.params)))
print('Accuracy : ' + str(mpl.accuracy(mpl.h, yl, 1)))
print('')
print('* Gradient Descent')
mpl.gradient_descent(10)
print('Accuracy : ' + str(mpl.accuracy(mpl.h, yl, 1)))
print('')
print('* LBFGS Optimization')
mpl.lbfgs(150)
print('Cost Function: ' + str(mpl.cost_function(mpl.params)))
print('Accuracy : ' + str(mpl.accuracy(mpl.h, yl, 1)))
print('')
print('Mean: ' + str(np.mean(mpl.params)))
print('Sum : ' + str(np.sum(mpl.params)))
print('Norm: ' + str(np.linalg.norm(mpl.params)))
# print(pd.DataFrame(mpl.params))
print('')
if file_option['run_stat']:
print('=======================================================================')
print(' Statistics ')
print('=======================================================================')
#0 0.3155 99.87 23.8 48.38 11.42
for s in struct:
Options["structure"]["hidden"] = s
Options['regularization'] = 1.0
info = str()
tp = Perceptron(train_set['y'], train_set['x'], Options)
print('\nStructure: ' + str(s) + '\tparams: ' + str(len(tp.params)))
print('idx cost acc acc norm time')
print('-----------------------------------------')
for i in range(0, test):
mpl = Perceptron(train_set['y'], train_set['x'], Options)
lb = lambda: mpl.lbfgs(ite_table[3])
time = tm.timeit(lb, number=1)
h1 = mpl.predict(train_set['x'], mpl.params)
h2 = mpl.predict(valid_set['x'], mpl.params)
info = str(i) + '\t' + \
str(mpl.cost_function(mpl.params))[0:6] + '\t' +\
str(mpl.accuracy(h1, train_set['yl'], 1))[0:5] + '\t' +\
str(mpl.accuracy(h2, valid_set['yl'], 1))[0:5] + '\t' +\
str(np.linalg.norm(mpl.params))[0:5] + '\t' +\
str(time)[0:5] + str('')
print(info)
