LostTech.TensorFlow : API Documentation

Type BaselineEstimator

Namespace tensorflow_estimator.python.estimator.api.estimator

Parent Estimator

Interfaces IBaselineEstimator

Public static methods

BaselineEstimator NewDyn(object head, object model_dir, ImplicitContainer<T> optimizer, object config)

Applies cosine decay to the learning rate.

See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983

When training a model, it is often recommended to lower the learning rate as the training progresses. This schedule applies a cosine decay function to an optimizer step, given a provided initial learning rate. It requires a `step` value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The schedule a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as: Example usage: You can pass this schedule directly into a tf.keras.optimizers.Optimizer as the learning rate. The learning rate schedule is also serializable and deserializable using tf.keras.optimizers.schedules.serialize and tf.keras.optimizers.schedules.deserialize.
A 1-arg callable learning rate schedule that takes the current optimizer step and outputs the decayed learning rate, a scalar `Tensor` of the same type as `initial_learning_rate`.
Show Example
def decayed_learning_rate(step):
              step = min(step, decay_steps)
              cosine_decay = 0.5 * (1 + cos(pi * step / decay_steps))
              decayed = (1 - alpha) * cosine_decay + alpha
              return initial_learning_rate * decayed 

Public properties

object config get;

object config_dyn get;

object model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object params get;

object params_dyn get;

object PythonObject get;