35999e65a83c8b9f67b833623a5b9ce47af7cc71,luminoth/utils/training.py,,get_learning_rate,#Any#Any#,16

Before Change

        raise ValueError("Invalid learning_rate method "{}"".format(method))

    if method == "piecewise_constant":
        learning_rate = tf.train.piecewise_constant(
            global_step, boundaries=[
                tf.cast(train_config.learning_rate_decay, tf.int64), ],
            values=[
                train_config.initial_learning_rate,
                train_config.initial_learning_rate * 0.1
            ], name="learning_rate_piecewise_constant"
        )

    elif method == "exponential_decay":
        learning_rate = tf.train.exponential_decay(
            learning_rate=train_config.initial_learning_rate,

After Change

        lr_config["global_step"] = global_step

    // boundaries, when used, must be the same type as global_step (int64).
    if "boundaries" in lr_config:
        lr_config["boundaries"] = [
            tf.cast(b, tf.int64) for b in lr_config["boundaries"]
        ]

    decay_function = LEARNING_RATE_DECAY_METHODS[decay_method]
    learning_rate = decay_function(
        **lr_config
    )

Instances