"""Optimizers are used to update weight parameters in a neural network.
The learning rate defines what stepsizes are taken during one iteration of training.
This file contains functions to return standard or custom optimizers.
"""
import tensorflow as tf
[docs]def adam(learning_rate: float):
"""Keras' adam optimizer with a specified learning rate."""
return tf.keras.optimizers.Adam(learning_rate=learning_rate)
[docs]def rmsprop(learning_rate: float):
"""Keras' rmsprop optimizer with a specified learning rate."""
return tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
[docs]def amsgrad(learning_rate: float):
"""Keras' amsgrad optimizer with a specified learning rate."""
return tf.keras.optimizers.Adam(learning_rate=learning_rate, amsgrad=True)