CODEWITHSUNDEEP
pythontensorflowkeras
Loic L.
Loic L.

Reputation: 407

How to set keras custom metrics to only be called at the end of epoch?

I am trying to use a custom metrics for my neural network and this metric should only be evaluated at the end of the epoch. The problem that I encounter is that the metrics is evaluated at each batch which is not the behaviour wanted. Note that I am working with generators and fit_generator with keras.

validation_data are loaded with a generator that implements keras.utils.Sequence

class DataGenerator(keras.utils.Sequence): 
   def __init__(self, inputs, labels, batch_size):
    self.inputs = inputs
    self.labels = labels
    self.batch_size = batch_size

   def __getitem__(self, index):
    #some processing done here
    return batch_inputs, batch_labels

   def __len__(self):
    return int(np.floor(len(self.inputs) / self.batch_size))

I tried to implement what the keras documentation suggests but I did not find any info to specify the metric should only used at the end of epoch.

def auc_roc(y_true, y_pred):
   auc, up_opt = tf.metrics.auc(y_true, y_pred)
   K.get_session().run(tf.local_variables_initializer())
   with tf.control_dependencies([up_opt]):
       auc = tf.identity(auc)
   return auc

So right now the auc_roc is called after each batch instead of a single call at the end of the epoch.

Upvotes: 5

Views: 2648

Answers (1)

ixeption
ixeption

Reputation: 2060

from sklearn.metrics import roc_auc_score
from keras.callbacks import Callback

class IntervalEvaluation(Callback):
    def __init__(self, validation_data=(), interval=10):
        super(Callback, self).__init__()

        self.interval = interval
        self.X_val, self.y_val = validation_data

    def on_epoch_end(self, epoch, logs={}):
        if epoch % self.interval == 0:
            y_pred = self.model.predict_proba(self.X_val, verbose=0)
            score = roc_auc_score(self.y_val, y_pred)
            print("interval evaluation - epoch: {:d} - score: {:.6f}".format(epoch, score))

Usage:

ival = IntervalEvaluation(validation_data=(x_test2, y_test2), interval=1)

More Info: http://digital-thinking.de/keras-three-ways-to-use-custom-validation-metrics-in-keras/

Upvotes: 3

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