5606f8252aafb57777d6e3b9154ab2256aabd72b,train_decaying_keep_prob.py,,keep_prob_decay,#Any#Any#Any#Any#Any#Any#Any#,37

Before Change

                        [validation_accuracy] +
                        [0.0 for i in range(1, num_updates)])

                accumulator = tf.cond(
                    tf.equal(trigger, 1), reset_accumulator,
                    lambda: accumulator_op)

                accumulated = tf.cond(
                    tf.equal(trigger, 1), lambda: tf.assign(accumulated, 1),
                    lambda: accumulated_op)

            // status variable
            keep_prob = tf.assign(
                keep_prob,

After Change

                    lambda: tf.cast(tf.mod(accumulated, num_updates_tensor), tf.int32))

                // execute only after position update
                with tf.control_dependencies([position]):

                    def reset_accumulator():
                        set past validation accuracies to 0 and place actual
                        validation accuracy in position 0
                        return tf.scatter_update(
                            accumulator, [i for i in range(num_updates)],
                            [validation_accuracy] +
                            [0.0 for i in range(1, num_updates)])

                    // update accumulator
                    // if trigger: reset_acculator, else accumulator[position] = va
                    accumulator = tf.cond(
                        tf.equal(trigger, 1), reset_accumulator,
                        lambda: tf.scatter_update(accumulator, position, validation_accuracy)
                    )
                    // update accumulated (for current prob)
                    // if trigger; accumulated = 1, else accumulated +=1
                    accumulated = tf.cond(
                        tf.equal(trigger, 1),
                        lambda: tf.assign(accumulated, 1),
                        lambda: tf.assign_add(accumulated, 1))

        return keep_prob


def train():

Instances