def test_ohc_simple_float(self):
Y = np.array([[1, 0], [0, 1], [1, 0]])
Y_ = np.array([[0.8, 0.2], [0.4, 0.6], [0.55, 0.45]])
acc = ohc.accuracy(Y, Y_)
self.assertEqual(acc, 1)
root = Settings['data_root']
model_root = join(root, 'good_models')
#filepath = join(model_root, 'stacknet64x64_84acc.h5')
filepath = join(root, 'stacknet_64x64_model.h5')
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint, TerminateOnNaN()]
if isfile(filepath):
model = load_model(filepath)
else:
raise Exception("Could not find model!")
model.summary()
#sampler = ReId.DataSampler(root,64,64)
sampler = MOT16Sampler(root, (64, 64))
X, Y = sampler.get_named_batch('MOT16-02', 1000, 4000)
X = preprocess_input(X.astype('float64'))
Y_ = model.predict(X)
#Y_clipped = (Y_[:,0] > 0.5) * 1
#Yclipped = (Y[:,0] > 0.5) * 1
#accuracy = np.sum( (Y_clipped == Yclipped) * 1) / len (Yclipped)
accuracy = ohc.accuracy(Y, Y_)
print("accuracy : \t", accuracy)
def test_ohc_simple_half(self):
Y = np.array([[1, 0], [0, 1], [1, 0], [0, 1]])
Y_ = np.array([[0, 1], [1, 0], [1, 0], [0, 1]])
acc = ohc.accuracy(Y, Y_)
self.assertEqual(acc, 0.5)