def decode_predictions(preds, classes, top):
if not classes:
print("Warning! you didn't pass your own set of classes for the model therefore imagenet classes are used")
return decode_imagenet_predictions(preds, top)
if len(preds.shape) != 2 or preds.shape[1] != len(classes):
raise ValueError('you need to provide same number of classes as model prediction output ' + \
'model returns %s predictions, while there are %s classes' % (
preds.shape[1], len(classes)))
results = []
for pred in preds:
top_indices = pred.argsort()[-top:][::-1]
result = [("", classes[i], pred[i]) for i in top_indices]
results.append(result)
return results
def decode_predictions(preds, classes, top):
if not classes:
print("Warning! you didn't pass your own set of classes for the model therefore imagenet classes are used")
return decode_imagenet_predictions(preds, top)
if len(preds.shape) != 2 or preds.shape[1] != len(classes):
raise ValueError('you need to provide same number of classes as model prediction output ' + \
'model returns %s predictions, while there are %s classes' % (
preds.shape[1], len(classes)))
results = []
for pred in preds:
top_indices = pred.argsort()[-top:][::-1]
result = [("", classes[i], pred[i]) for i in top_indices]
results.append(result)
return results