tf.keras.callbacks.History

TensorFlow 1 version

View source on GitHub

Learning rate scheduler.

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.LearningRateScheduler

tf.keras.callbacks.LearningRateScheduler(
    schedule, verbose=0
)

Used in the notebooks

Used in the tutorials
Distributed training with Keras TensorBoard Scalars: Logging training metrics in Keras

At the beginning of every epoch, this callback gets the updated learning rate value from schedule function provided at __init__, with the current epoch and current learning rate, and applies the updated learning rate on the optimizer.

Arguments
`schedule`	a function that takes an epoch index (integer, indexed from 0) and current learning rate (float) as inputs and returns a new learning rate as output (float).
`verbose`	int. 0: quiet, 1: update messages.

Example:

# This function keeps the initial learning rate for the first ten epochs
# and decreases it exponentially after that.
def scheduler(epoch, lr):
  if epoch < 10:
    return lr
  else:
    return lr * tf.math.exp(-0.1)

model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
model.compile(tf.keras.optimizers.SGD(), loss='mse')
round(model.optimizer.lr.numpy(), 5)
0.01

callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
                    epochs=15, callbacks=[callback], verbose=0)
round(model.optimizer.lr.numpy(), 5)
0.00607

Methods

`set_model`

View source

set_model(
    model
)

`set_params`

View source

set_params(
    params
)

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.keras / callbacks / callbacks.History

View aliases

Used in the notebooks

Arguments

Example:

Methods

set_model

set_params

`set_model`

`set_params`