I am using this function to predict the output of never seen images

def predictor(img, model):
    image = cv2.imread(img)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (224, 224))
    image = np.array(image, dtype = 'float32')/255.0
    plt.imshow(image)
    image = image.reshape(1, 224,224,3)
    
    clas = model.predict(image).argmax()
    name = dict_class[clas]
    print('The given image is of \nClass: {0} \nSpecies: {1}'.format(clas, name))

how to change it, if I want the top 2(or k) accuracy i.e

70% chance its dog

15% its a bear

CodePudding user response：

If you are using TensorFlow Keras and probably doing multi-class classification, then the output of model.predict() is a tensor representing either the logits or already the probabilities (softmax on top of logits).

I am taking this example from here and slightly modifying it : https://www.tensorflow.org/api_docs/python/tf/math/top_k.

#See the softmax, probabilities add up to 1
network_predictions = [0.7,0.2,0.05,0.05]
prediction_probabilities = tf.math.top_k(network_predictions, k=2)
top_2_scores = prediction_probabilities.values.numpy()
dict_class_entries = prediction_probabilities.indices.numpy()

And here in dict_class_entries you have then the indices (sorted ascendingly) in accordance with the probabilities. (i.e. dict_class_entries[0] = 0 (corresponds to 0.7) and top_2_scores[0] = 0.7 etc.).

You just need to replace network_probabilities with model.predict(image).

Notice I removed the argmax() in order to send an array of probabilities instead of the index of the max score/probability position (that is, argmax()).