def classify(file_path):
global label_packed
image = Image.open(file_path)
image = image.resize((30,30))
image = numpy.expand_dims(image, axis=0)
image = numpy.array(image)
pred = model.predict_classes(image)[0]
sign = classes[pred+1]
print(sign)
label.configure(foreground='#011638', text=sign)
Change the:
model.predict_classes()
to
model.predict()
and it'll return a probability. Then you should define a threshold to consider prediction probabilities to one of the classes. Remember that threshold is up to you. You can be strict about that or not. However, I suppose it to be 0.5:
pred_class = np.where(model.predict(example) > 0.5, 1, 0)
Then, you'll get predicted classes.
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