def get_per_class_accuracy(labels, preds):
names = []
accs = []
dico = get_dico()
inv_dico = invert_dico(dico)
for value in set(labels):
s = 0
n = 0
for i in range(len(labels)):
if (labels[i] == value):
n = n + 1
if (preds[i] == value):
s = s + 1
names.append(inv_dico.get(value))
accs.append(s / n * 100, )
return accs, names
def get_pred(model, image_path, is_decaf6=False, top_k=1,bagging=DEFAULT_BAGGING,preprocessing=DEFAULT_PREPROCESSING):
target_size = (224, 224)
if is_decaf6:
target_size = (227, 227)
img = load_img(image_path, target_size=target_size)
x = img_to_array(img)
if bagging:
pred = _bagging_predict(x, model,preprocessing=preprocessing)
else:
x = _preprocess_input(x,preprocessing=preprocessing)
pred = model.predict(x[np.newaxis, ...])
dico = get_dico()
inv_dico = invert_dico(dico)
args_sorted = np.argsort(pred)[0][::-1]
preds = [inv_dico.get(a) for a in args_sorted[:top_k]]
pcts = [pred[0][a] for a in args_sorted[:top_k]]
return preds,pcts
