How to calculate top5 accuracy in keras?

问题:

I want to calculate top5 in imagenet2012 dataset, but i don't know how to do it in keras. fit function just can calculate top 1 accuracy.

回答1:

If you are just after the topK you could always call tensorflow directly (you don't say which backend you are using).

from keras import backend as K import tensorflow as tf  top_values, top_indices = K.get_session().run(tf.nn.top_k(_pred_test, k=5)) 

If you want an accuracy metric you can add it to your model 'top_k_categorical_accuracy'.

model.compile('adam', 'categorical_crossentropy', ['accuracy', 'top_k_categorical_accuracy'])  history = model.fit(X_train, y_train, nb_epoch=3, validation_split=0.2)  Train on 31367 samples, validate on 7842 samples Epoch 1/3 31367/31367 [==============================] - 6s - loss: 0.0818 - acc: 0.9765 - top_k_categorical_accuracy: 0.9996 -  ... 

The default k for this metric is 5 but if you wanted to change that to say 3 you would set up your model like this:

top3_acc = functools.partial(keras.metrics.top_k_categorical_accuracy, k=3)  top3_acc.__name__ = 'top3_acc'  model.compile('adam', 'categorical_crossentropy', ['accuracy', top3_acc]) 

回答2:

Is this what you are looking for : top_k_categorical_crossentropy?

You can select the metric you want but also easily create you own custom metric.

回答3:

Frank Wilson's answer is probably the more official answer, but just you can also calculate it like this.

top1 = 0.0 top5 = 0.0     class_probs = model.predict(x) for i, l in enumerate(labels):     class_prob = class_probs[i]     top_values = (-class_prob).argsort()[:5]     if top_values[0] == l:         top1 += 1.0     if np.isin(np.array([l]), top_values):         top5 += 1.0  print("top1 acc", top1/len(labels)) print("top1 acc", top5/len(labels)) 

文章来源: How to calculate top5 accuracy in keras?