e96e2ae8a6888473093726b925b13012a2f4ce8b,tflearn/helpers/evaluator.py,Evaluator,evaluate,#Evaluator#Any#Any#Any#,62

Before Change

            return prediction

    def evaluate(self, feed_dict, metric="accuracy", batch_size=128):
        raise NotImplementedError

After Change

            return prediction

    def evaluate(self, feed_dict, ops, batch_size=128):
        coord = tf.train.Coordinator()
        inputs = tf.get_collection(tf.GraphKeys.INPUTS)
        // Data Preprocessing
        dprep_dict = []
        dprep_collection = tf.get_collection(tf.GraphKeys.DATA_PREP)
        for i in range(len(inputs)):
            // Support for custom inputs not using dprep/daug
            if len(dprep_collection) > i:
                if dprep_collection[i] is not None:

Instances