}
}
print('* Load debugging data')
# load data
x = pd.read_csv('c:/class/test_data/xdebug.csv', header=None).values
y = pd.read_csv('c:/class/test_data/ydebug.csv', header=None).values
t1 = pd.read_csv('c:/class/test_data/t1debug.csv', header=None).values
t2 = pd.read_csv('c:/class/test_data/t2debug.csv', header=None).values
# super-hot thing
y = format_labels(np.transpose(y)[0])
# initialize the neural network with full data set
mpl = Perceptron(y, x, Options)
si = Options['structure']['input']
sh = Options['structure']['hidden'][0]
so = Options['structure']['output']
# Check size
# print('* Params Size')
# print(mpl.size)
# print(t1.shape, t2.shape)
print('')
print('* Load debugging weights')
print(mpl.size)
# set initial weight
"hidden": [25],
"output": 10,
}
}
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' + \
