def adapt_params(self,
distance_type='eucos',
tail_range=range(0, 40),
alpha_range=range(0, 10)):
adaption = np.zeros((len(alpha_range) + 1, len(tail_range) + 1))
for image in tqdm(self.x_data):
for alpha in alpha_range:
for tail in tail_range:
self.create_weibull(distance_type,
tailsize=tail,
overwrite=False)
if not image.shape == self.input_shape:
image = image.reshape(self.input_shape)
imgarr = {
'scores': self.compute_score(image),
self.observations[0]: self.compute_feature(image)
}
openmax, softmax = recalibrate_scores(
self.weibull_model,
range(len(self.CLASSES)),
imgarr,
self.observations[0],
distance_type=distance_type,
classes=len(self.CLASSES),
alpharank=alpha)
if np.argmax(openmax) == np.argmax(softmax):
adaption[alpha][tail] += 1
return adaption
def predict(self, image, distance_type='eucos', alpha=10):
if self.weibull_model is None:
self.create_weibull(distance_type, self.overwrite)
if not image.shape == self.input_shape:
image = image.reshape(self.input_shape)
imgarr = {
'scores': self.compute_score(image),
self.observations[0]: self.compute_feature(image)
}
openmax, softmax = recalibrate_scores(self.weibull_model,
range(len(self.CLASSES)),
imgarr,
self.observations[0],
distance_type=distance_type,
classes=len(self.CLASSES),
alpharank=alpha)
return openmax, softmax
def compute_openmax(model, imagearr):
mean = np.load('mean.npy')
distance = np.load('distance.npy')
#Use loop to find the good parameters
#alpharank_list = [1,2,3,4,5,5,6,7,8,9,10]
#tail_list = list(range(0,21))
alpharank_list = [10]
#tail_list = [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
tail_list = [5]
total = 0
for alpha in alpharank_list:
weibull_model = {}
openmax = None
softmax = None
for tail in tail_list:
#print ('Alpha ',alpha,' Tail ',tail)
#print ('++++++++++++++++++++++++++++')
weibull_model = build_weibull(mean, distance, tail)
openmax, softmax = recalibrate_scores(weibull_model,
label,
imagearr,
alpharank=alpha)
#print ('Openmax: ',np.argmax(openmax))
#print ('Softmax: ',np.argmax(softmax))
#print ('opemax lenght',openmax.shape)
#print ('openmax',np.argmax(openmax))
#print ('openmax',openmax)
#print ('softmax',softmax.shape)
#print ('softmax',np.argmax(softmax))
#if np.argmax(openmax) == np.argmax(softmax):
#if np.argmax(openmax) == 0 and np.argmax(softmax) == 0:
#print ('########## Parameters found ############')
#print ('Alpha ',alpha,' Tail ',tail)
#print ('########## Parameters found ############')
# total += 1
#print ('----------------------------')
return np.argmax(softmax), np.argmax(openmax)
def adapt_tailsize(self,
distance_type='eucos',
adaption_range=range(1, 40)):
tails = np.zeros(len(adaption_range) + 1)
for image in self.x_data:
for i in adaption_range:
self.create_weibull(distance_type, tailsize=i, overwrite=False)
if not image.shape == self.input_shape:
image = image.reshape(self.input_shape)
imgarr = {
'scores': self.compute_score(image),
self.observations[0]: self.compute_feature(image)
}
openmax, softmax = recalibrate_scores(
self.weibull_model,
range(len(self.CLASSES)),
imgarr,
self.observations[0],
distance_type=distance_type,
classes=len(self.CLASSES))
if np.argmax(openmax) == np.argmax(softmax):
tails[i] += 1
return tails