def call(self, *args, **kwargs):
gent = self._read_data
if self.modelstr == 'EEGNet':
_co = {}
elif self.modelstr == 'rawEEGConv' or self.modelstr == 'TSGLEEGNet':
_co = {'TSG': TSG}
else:
_co = {}
acc_list = []
kappa_list = []
data = {'x_test': None, 'y_test': None}
for subject in self.subs:
pred_list = []
for path in walk_files(
os.path.join(self.basepath, '{:0>2d}'.format(subject))):
if not self._readed:
for data['x_test'], data['y_test'] in gent(subject=subject,
mode='test'):
self._readed = True
if self.standardizing:
data = self._standardize(data)
model = tf.keras.models.load_model(path, custom_objects=_co)
if self.cropping:
_Pred = []
for cpd in self._cropping_data((data['x_test'], )):
pd = model.predict(cpd, verbose=0)
_Pred.append(
np.argmax(pd, axis=1) == np.squeeze(
data['y_test']))
_Pred = np.array(_Pred)
Pred = []
for i in np.arange(_Pred.shape[1]):
if _Pred[:, i].any():
Pred.append(1)
else:
Pred.append(0)
acc = np.mean(np.array(Pred))
kappa = 0. # None
else:
_pred = model.predict(data['x_test'],
batch_size=self.batch_size,
verbose=self.verbose)
pred_list.append(np.squeeze(np.argmax(_pred, axis=1)))
pred = self.ensemble(pred_list, self.weight_list[subject - 1])
acc_list.append(np.mean(pred == np.squeeze(data['y_test'])))
kappa_list.append(computeKappa(pred, data['y_test']))
self._readed = False
avg_acc = np.mean(acc_list)
avg_kappa = np.mean(kappa_list)
with open(self.resavepath, 'w+') as f:
sys.stdout = f
print(('{0:s} {1:d}-fold Ensemble({2:s}) ' + self.validation_name +
' Accuracy (kappa)').format(self.modelstr, self.kFold,
self.ename))
for i in range(len(self.subs)):
print('Subject {0:0>2d}: {1:.2%} ({2:.4f})'.format(
self.subs[i], acc_list[i], kappa_list[i]))
print('Average : {0:.2%} ({1:.4f})'.format(avg_acc, avg_kappa))
sys.stdout = _console
f.seek(0, 0)
for line in f.readlines():
print(line)
f.close()
acc_list.append(avg_acc)
kappa_list.append(avg_kappa)
return acc_list, kappa_list
filepath = os.path.join('data', 'A', 'Test', 'A03E' + '.mat')
vis = visualize(
model,
vis_data_file=filepath,
cropping=cropping,
# step=25,
beg=beg,
end=end,
srate=srate)
data = vis._read_data(srate)
loss, acc = model.evaluate(data['x'], data['y'], batch_size=10, verbose=2)
_pred = model.predict(data['x'], batch_size=10, verbose=2)
pred = np.argmax(_pred, axis=1)
kappa = computeKappa(pred, data['y'])
print('loss: %.4f\tacc: %.4f\tkappa: %.4f' % (loss, acc, kappa))
vis.kernel('tfconv')
vis.line_kernel('tfconv', [1, 6, 7, 10, 11, 15])
# vis.fft_kernel('tfconv')
# vis.fft_output('tfconv')
# vis.fs_fft_output('tfconv')
# vis.class_fft_output('tfconv')
# vis.depthwise_kernel('sconv')
# vis.topo_kernel('sconv')
# vis.fs_class_topo_kernel('sconv')
# vis.fs_class_fft_output('tfconv')
# vis.fs_class_freq_topo_kernel('sconv')
# vis.kernel('fs')
# vis.line_kernel('fs')