在Keras使用tf.metrics？

Question

I'm especially interested in specificity_at_sensitivity . Looking through the Keras docs :

from keras import metrics

model.compile(loss='mean_squared_error',
              optimizer='sgd',
              metrics=[metrics.mae, metrics.categorical_accuracy])

But it looks like the metrics list must have functions of arity 2, accepting (y_true, y_pred) and returning a single tensor value.

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EDIT: Currently here is how I do things:

from sklearn.metrics import confusion_matrix

predictions = model.predict(x_test)
y_test = np.argmax(y_test, axis=-1)
predictions = np.argmax(predictions, axis=-1)
c = confusion_matrix(y_test, predictions)
print('Confusion matrix:\n', c)
print('sensitivity', c[0, 0] / (c[0, 1] + c[0, 0]))
print('specificity', c[1, 1] / (c[1, 1] + c[1, 0]))

The disadvantage of this approach, is I only get the output I care about when training has finished. Would prefer to get metrics every 10 epochs or so.

Answer 1

I've found a related issue on github , and it seems that tf.metrics are still not supported by Keras models. However, in case you are very interested in using tf.metrics.specificity_at_sensitivity , I would suggest the following workaround (inspired by BogdanRuzh's solution):

def specificity_at_sensitivity(sensitivity, **kwargs):
    def metric(labels, predictions):
        # any tensorflow metric
        value, update_op = tf.metrics.specificity_at_sensitivity(labels, predictions, sensitivity, **kwargs)

        # find all variables created for this metric
        metric_vars = [i for i in tf.local_variables() if 'specificity_at_sensitivity' in i.name.split('/')[2]]

        # Add metric variables to GLOBAL_VARIABLES collection.
        # They will be initialized for new session.
        for v in metric_vars:
            tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)

        # force to update metric values
        with tf.control_dependencies([update_op]):
            value = tf.identity(value)
            return value
    return metric


model.compile(loss='mean_squared_error',
              optimizer='sgd',
              metrics=[metrics.mae,
                       metrics.categorical_accuracy,
                       specificity_at_sensitivity(0.5)])

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UPDATE:

You can use model.evaluate to retrieve the metrics after training.

Answer 2

I don't think there is a strict limit to only two incoming arguments, in metrics.py the function is just three incoming arguments, but k selects the default value of 5.

def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
    return K.mean(K.in_top_k(y_pred, K.cast(K.max(y_true, axis=-1), 'int32'), k), axis=-1)