e605d5b1621d118435e23a15bedc31754b9a2b3b,src/model_fn.py,,model_fn,#Any#Any#Any#,81

Before Change

                logits,sequence_length,label,length)

            //Create homogeneity among the return values
            global_step = tf.convert_to_tensor(
                tf.train.get_or_create_global_step())
            global_step = tf.cast(global_step, tf.float32)
            sequence_error = tf.cast(sequence_error, tf.float32)

            //Get the correct format to pass to estimator spec
            result = tf.convert_to_tensor([(tf.stack([global_step,
                                                  loss, 
                                                  label_error, 
                                                  sequence_error], axis=0))])

            return tf.estimator.EstimatorSpec(mode=mode, loss=loss, 
                                      predictions=result, 
                                      train_op=None)

After Change

            return tf.estimator.EstimatorSpec(mode=mode, 
                                              loss=loss, 
                                              eval_metric_ops=
                                              {"label_error":
                                               metric_fn(label_error),
                                               "sequence_error":
                                               seq_err_metric_fn(sequence_error)},
                                              train_op=None)

def label_err_metric_fn(label_error):

Instances