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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from typing import Callable
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import tensorflow as tf
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class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
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def __init__(
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self,
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initial_learning_rate: float,
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decay_schedule_fn: Callable,
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warmup_steps: int,
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power: float = 1.0,
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name: str = "",
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):
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super().__init__()
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self.initial_learning_rate = initial_learning_rate
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self.warmup_steps = warmup_steps
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self.power = power
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self.decay_schedule_fn = decay_schedule_fn
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self.name = name
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def __call__(self, step):
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with tf.name_scope(self.name or "WarmUp") as name:
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global_step_float = tf.cast(step, tf.float32)
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warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
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warmup_percent_done = global_step_float / warmup_steps_float
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warmup_learning_rate = self.initial_learning_rate * tf.math.pow(
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warmup_percent_done, self.power
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)
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return tf.cond(
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global_step_float < warmup_steps_float,
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lambda: warmup_learning_rate,
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lambda: self.decay_schedule_fn(step - self.warmup_steps),
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name=name,
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)
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def get_config(self):
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return {
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"initial_learning_rate": self.initial_learning_rate,
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"decay_schedule_fn": self.decay_schedule_fn,
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"warmup_steps": self.warmup_steps,
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"power": self.power,
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"name": self.name,
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}
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