237d138e6af68e7aed794c4797f07eab2fad4261,scripts/tf_cnn_benchmarks/models/ssd_model.py,SSD300Model,get_learning_rate,#SSD300Model#Any#Any#,196

Before Change

  def get_learning_rate(self, global_step, batch_size):
    boundaries = [160000, 200000]
    learning_rates = [1e-3, 1e-4, 1e-5]
    return tf.train.piecewise_constant(global_step, boundaries, learning_rates)

  def _collect_backbone_vars(self):
    backbone_vars = tf.get_collection(

After Change

    return cnn.top_layer

  def get_learning_rate(self, global_step, batch_size):
    rescaled_lr = self.get_scaled_base_learning_rate(batch_size)
    // Defined in MLPerf reference model
    boundaries = [160000, 200000]
    boundaries = [b * self.base_lr_batch_size // batch_size for b in boundaries]
    decays = [1, 0.1, 0.01]
    learning_rates = [rescaled_lr * d for d in decays]
    lr = tf.train.piecewise_constant(global_step, boundaries, learning_rates)
    warmup_steps = int(118287 / batch_size * 5)
    warmup_lr = (
        rescaled_lr * tf.cast(global_step, tf.float32) / tf.cast(
            warmup_steps, tf.float32))
    return tf.cond(global_step < warmup_steps, lambda: warmup_lr, lambda: lr)

  def get_scaled_base_learning_rate(self, batch_size):
    Calculates base learning rate for creating lr schedule.

Instances