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An example to check the AUC score on a validation set for each 10 epochs.
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| """ | |
| An example to check the AUC score on a validation set for each 10 epochs. | |
| I hope it will be helpful for optimizing number of epochs. | |
| """ | |
| # -*- coding: utf-8 -*- | |
| import logging | |
| 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) | |
| logging.info("interval evaluation - epoch: {:d} - score: {:.6f}".format(epoch, score)) | |
| # (snip) | |
| if __name__ == '__main__': | |
| l.basicConfig(format='%(asctime)s %(message)s', level=l.INFO) | |
| X_train, y_train, X_test, y_test = load_data() | |
| ival = IntervalEvaluation(validation_data=(X_test, y_test), interval=10) | |
| clf = keras_model(input_size=X_train.shape[1]) | |
| clf.fit(X_train, y_train, nb_epoch=100, batch_size=128, verbose=0, callbacks=[ival]) |
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