how to use top_k_categorical_accuracy metric?

[cxy...@gmail.com]

Hi all,

From the Keras documentations, there's a metric called "top_k_categorical_accuracy", I'm having trouble knowing how to actually use that in the code, because almost everywhere is using the example of

model.compile (..., metrics=['accuracy'])

so i tried   model.compile(..., metrics=['top_k_categorical_accuracy']) but it doesn't work, tried a lot of other stuff and nothing worked either.

in addition, how do I change the k from the default of 5 to, say, 3?

Thanks in advance!

[Chen-Ping Yu]

it's for tensorflow backend by the way.

[l...@lqu.me]

I had the same problem. You need to import it.

 from keras.metrics import top_k_categorical_accuracy

For top 3, create a new function with different default.

 inTop3 = lambda x, y: top_k_categorical_accuracy(x, y, k=3)

 model.compile(..., metrics=[inTop3])

[Daπid]

On 11 Jan 2017 23:11, <l...@lqu.me> wrote:

I had the same problem. You need to import it.

 from keras.metrics import top_k_categorical_accuracy

For top 3, create a new function with different default.

 inTop3 = lambda x, y: top_k_categorical_accuracy(x, y, k=3)

Or use functools.partial

 model.compile(..., metrics=[inTop3])

On Saturday, November 5, 2016 at 12:26:46 PM UTC-7, Chen-Ping Yu wrote:

Hi all,

From the Keras documentations, there's a metric called "top_k_categorical_accuracy", I'm having trouble knowing how to actually use that in the code, because almost everywhere is using the example of

model.compile (..., metrics=['accuracy'])

so i tried   model.compile(..., metrics=['top_k_categorical_accuracy']) but it doesn't work, tried a lot of other stuff and nothing worked either.

in addition, how do I change the k from the default of 5 to, say, 3?

Thanks in advance!

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[ZuZu]

For top-1 (which is the same as 'accuracy' metric, use:

def top_1_categorical_accuracy(y_true, y_pred):

    return top_k_categorical_accuracy(y_true, y_pred, k=1) 

and then something like...

model.compile(optimizer='adadelta', 

              loss='categorical_crossentropy', 

              metrics=['accuracy', top_1_categorical_accuracy])

[f201...@goa.bits-pilani.ac.in]

saved so much effort! thank you so much!

[marco.mo...@i-m3d.com]

For me the solution using a lambda was not working. It gave me a weird error saying that "<lambda> is not a valid scope". To me this worked: https://stackoverflow.com/questions/46858016/keras-custom-loss-function-to-pass-arguments-other-than-y-true-and-y-pred