b3677ae2936d0457cb8c6213ea83bd89610eb3f0,official/vision/image_classification/optimizer_factory.py,,build_learning_rate,#Any#Any#Any#Any#,339

Before Change

        decay_rate=decay_rate,
        staircase=params.staircase)
  elif decay_type == "piecewise_constant_with_warmup":
    logging.info("Using Piecewise constant decay with warmup. "
                 "Parameters: batch_size: %d, epoch_size: %d, "
                 "warmup_epochs: %d, boundaries: %s, multipliers: %s",
                 batch_size, params.examples_per_epoch,
                 params.warmup_epochs, params.boundaries,
                 params.multipliers)
    lr = learning_rate.PiecewiseConstantDecayWithWarmup(
        batch_size=batch_size,
        epoch_size=params.examples_per_epoch,
        warmup_epochs=params.warmup_epochs,

After Change

        staircase=params.staircase)
  elif decay_type == "stepwise":
    steps_per_epoch = params.examples_per_epoch // batch_size
    boundaries = [boundary * steps_per_epoch for boundary in params.boundaries]
    multipliers = [batch_size * multiplier for multiplier in params.multipliers]
    logging.info("Using stepwise learning rate. Parameters: "
                 "boundaries: %s, values: %s",
                 boundaries, multipliers)
    lr = tf.keras.optimizers.schedules.PiecewiseConstantDecay(
        boundaries=boundaries,
        values=multipliers)
  elif decay_type == "cosine_with_warmup":
    lr = learning_rate.CosineDecayWithWarmup(
        batch_size=batch_size,
        total_steps=train_epochs * train_steps,

Instances