---
title: Writing your own callbacks
authors: Rick Chao, Francois Chollet
date-created: 2019/03/20
last-modified: 2023/06/25
description: Complete guide to writing new Keras callbacks.
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Writing your own callbacks}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
A callback is a powerful tool to customize the behavior of a Keras model during
training, evaluation, or inference. Examples include `keras.callbacks.TensorBoard`
to visualize training progress and results with TensorBoard, or
`keras.callbacks.ModelCheckpoint` to periodically save your model during training.
In this guide, you will learn what a Keras callback is, what it can do, and how you can
build your own. We provide a few demos of simple callback applications to get you
started.
## Setup
``` r
library(keras3)
```
## Keras callbacks overview
All callbacks subclass the `keras.callbacks.Callback` class, and
override a set of methods called at various stages of training, testing, and
predicting. Callbacks are useful to get a view on internal states and statistics of
the model during training.
You can pass a list of callbacks (as the keyword argument `callbacks`) to the following
model methods:
- `fit()`
- `evaluate()`
- `predict()`
## An overview of callback methods
### Global methods
#### `on_(train|test|predict)_begin(logs = NULL)`
Called at the beginning of `fit`/`evaluate`/`predict`.
#### `on_(train|test|predict)_end(logs = NULL)`
Called at the end of `fit`/`evaluate`/`predict`.
### Batch-level methods for training/testing/predicting
#### `on_(train|test|predict)_batch_begin(batch, logs = NULL)`
Called right before processing a batch during training/testing/predicting.
#### `on_(train|test|predict)_batch_end(batch, logs = NULL)`
Called at the end of training/testing/predicting a batch. Within this method, `logs` is
a named list containing the metrics results.
### Epoch-level methods (training only)
#### `on_epoch_begin(epoch, logs = NULL)`
Called at the beginning of an epoch during training.
#### `on_epoch_end(epoch, logs = NULL)`
Called at the end of an epoch during training.
## A basic example
Let's take a look at a concrete example. To get started, let's import tensorflow and
define a simple Sequential Keras model:
``` r
# Define the Keras model to add callbacks to
get_model <- function() {
model <- keras_model_sequential()
model |> layer_dense(units = 1)
model |> compile(
optimizer = optimizer_rmsprop(learning_rate = 0.1),
loss = "mean_squared_error",
metrics = "mean_absolute_error"
)
model
}
```
Then, load the MNIST data for training and testing from Keras datasets API:
``` r
# Load example MNIST data and pre-process it
mnist <- dataset_mnist()
flatten_and_rescale <- function(x) {
x <- array_reshape(x, c(-1, 784))
x <- x / 255
x
}
mnist$train$x <- flatten_and_rescale(mnist$train$x)
mnist$test$x <- flatten_and_rescale(mnist$test$x)
# limit to 1000 samples
n <- 1000
mnist$train$x <- mnist$train$x[1:n,]
mnist$train$y <- mnist$train$y[1:n]
mnist$test$x <- mnist$test$x[1:n,]
mnist$test$y <- mnist$test$y[1:n]
```
Now, define a simple custom callback that logs:
- When `fit`/`evaluate`/`predict` starts & ends
- When each epoch starts & ends
- When each training batch starts & ends
- When each evaluation (test) batch starts & ends
- When each inference (prediction) batch starts & ends
``` r
show <- function(msg, logs) {
cat(glue::glue(msg, .envir = parent.frame()),
"got logs: ", sep = "; ")
str(logs); cat("\n")
}
callback_custom <- Callback(
"CustomCallback",
on_train_begin = \(logs = NULL) show("Starting training", logs),
on_epoch_begin = \(epoch, logs = NULL) show("Start epoch {epoch} of training", logs),
on_train_batch_begin = \(batch, logs = NULL) show("...Training: start of batch {batch}", logs),
on_train_batch_end = \(batch, logs = NULL) show("...Training: end of batch {batch}", logs),
on_epoch_end = \(epoch, logs = NULL) show("End epoch {epoch} of training", logs),
on_train_end = \(logs = NULL) show("Stop training", logs),
on_test_begin = \(logs = NULL) show("Start testing", logs),
on_test_batch_begin = \(batch, logs = NULL) show("...Evaluating: start of batch {batch}", logs),
on_test_batch_end = \(batch, logs = NULL) show("...Evaluating: end of batch {batch}", logs),
on_test_end = \(logs = NULL) show("Stop testing", logs),
on_predict_begin = \(logs = NULL) show("Start predicting", logs),
on_predict_end = \(logs = NULL) show("Stop predicting", logs),
on_predict_batch_begin = \(batch, logs = NULL) show("...Predicting: start of batch {batch}", logs),
on_predict_batch_end = \(batch, logs = NULL) show("...Predicting: end of batch {batch}", logs),
)
```
Let's try it out:
``` r
model <- get_model()
model |> fit(
mnist$train$x, mnist$train$y,
batch_size = 128,
epochs = 2,
verbose = 0,
validation_split = 0.5,
callbacks = list(callback_custom())
)
```
```
## Starting training; got logs: Named list()
##
## Start epoch 1 of training; got logs: Named list()
##
## ...Training: start of batch 1; got logs: Named list()
##
## ...Training: end of batch 1; got logs: List of 2
## $ loss : num 25.9
## $ mean_absolute_error: num 4.19
##
## ...Training: start of batch 2; got logs: Named list()
##
## ...Training: end of batch 2; got logs: List of 2
## $ loss : num 433
## $ mean_absolute_error: num 15.5
##
## ...Training: start of batch 3; got logs: Named list()
##
## ...Training: end of batch 3; got logs: List of 2
## $ loss : num 297
## $ mean_absolute_error: num 11.8
##
## ...Training: start of batch 4; got logs: Named list()
##
## ...Training: end of batch 4; got logs: List of 2
## $ loss : num 231
## $ mean_absolute_error: num 9.68
##
## Start testing; got logs: Named list()
##
## ...Evaluating: start of batch 1; got logs: Named list()
##
## ...Evaluating: end of batch 1; got logs: List of 2
## $ loss : num 8.1
## $ mean_absolute_error: num 2.3
##
## ...Evaluating: start of batch 2; got logs: Named list()
##
## ...Evaluating: end of batch 2; got logs: List of 2
## $ loss : num 7.58
## $ mean_absolute_error: num 2.23
##
## ...Evaluating: start of batch 3; got logs: Named list()
##
## ...Evaluating: end of batch 3; got logs: List of 2
## $ loss : num 7.38
## $ mean_absolute_error: num 2.21
##
## ...Evaluating: start of batch 4; got logs: Named list()
##
## ...Evaluating: end of batch 4; got logs: List of 2
## $ loss : num 7.3
## $ mean_absolute_error: num 2.21
##
## Stop testing; got logs: List of 2
## $ loss : num 7.3
## $ mean_absolute_error: num 2.21
##
## End epoch 1 of training; got logs: List of 4
## $ loss : num 231
## $ mean_absolute_error : num 9.68
## $ val_loss : num 7.3
## $ val_mean_absolute_error: num 2.21
##
## Start epoch 2 of training; got logs: Named list()
##
## ...Training: start of batch 1; got logs: Named list()
##
## ...Training: end of batch 1; got logs: List of 2
## $ loss : num 7.44
## $ mean_absolute_error: num 2.27
##
## ...Training: start of batch 2; got logs: Named list()
##
## ...Training: end of batch 2; got logs: List of 2
## $ loss : num 6.81
## $ mean_absolute_error: num 2.16
##
## ...Training: start of batch 3; got logs: Named list()
##
## ...Training: end of batch 3; got logs: List of 2
## $ loss : num 6.12
## $ mean_absolute_error: num 2.06
##
## ...Training: start of batch 4; got logs: Named list()
##
## ...Training: end of batch 4; got logs: List of 2
## $ loss : num 6.08
## $ mean_absolute_error: num 2.04
##
## Start testing; got logs: Named list()
##
## ...Evaluating: start of batch 1; got logs: Named list()
##
## ...Evaluating: end of batch 1; got logs: List of 2
## $ loss : num 5.54
## $ mean_absolute_error: num 1.92
##
## ...Evaluating: start of batch 2; got logs: Named list()
##
## ...Evaluating: end of batch 2; got logs: List of 2
## $ loss : num 5.31
## $ mean_absolute_error: num 1.87
##
## ...Evaluating: start of batch 3; got logs: Named list()
##
## ...Evaluating: end of batch 3; got logs: List of 2
## $ loss : num 5.11
## $ mean_absolute_error: num 1.8
##
## ...Evaluating: start of batch 4; got logs: Named list()
##
## ...Evaluating: end of batch 4; got logs: List of 2
## $ loss : num 5.15
## $ mean_absolute_error: num 1.82
##
## Stop testing; got logs: List of 2
## $ loss : num 5.15
## $ mean_absolute_error: num 1.82
##
## End epoch 2 of training; got logs: List of 4
## $ loss : num 6.08
## $ mean_absolute_error : num 2.04
## $ val_loss : num 5.15
## $ val_mean_absolute_error: num 1.82
##
## Stop training; got logs: List of 4
## $ loss : num 6.08
## $ mean_absolute_error : num 2.04
## $ val_loss : num 5.15
## $ val_mean_absolute_error: num 1.82
```
``` r
res <- model |> evaluate(
mnist$test$x, mnist$test$y,
batch_size = 128, verbose = 0,
callbacks = list(callback_custom())
)
```
```
## Start testing; got logs: Named list()
##
## ...Evaluating: start of batch 1; got logs: Named list()
##
## ...Evaluating: end of batch 1; got logs: List of 2
## $ loss : num 5.2
## $ mean_absolute_error: num 1.84
##
## ...Evaluating: start of batch 2; got logs: Named list()
##
## ...Evaluating: end of batch 2; got logs: List of 2
## $ loss : num 4.62
## $ mean_absolute_error: num 1.73
##
## ...Evaluating: start of batch 3; got logs: Named list()
##
## ...Evaluating: end of batch 3; got logs: List of 2
## $ loss : num 4.61
## $ mean_absolute_error: num 1.74
##
## ...Evaluating: start of batch 4; got logs: Named list()
##
## ...Evaluating: end of batch 4; got logs: List of 2
## $ loss : num 4.65
## $ mean_absolute_error: num 1.75
##
## ...Evaluating: start of batch 5; got logs: Named list()
##
## ...Evaluating: end of batch 5; got logs: List of 2
## $ loss : num 4.84
## $ mean_absolute_error: num 1.77
##
## ...Evaluating: start of batch 6; got logs: Named list()
##
## ...Evaluating: end of batch 6; got logs: List of 2
## $ loss : num 4.76
## $ mean_absolute_error: num 1.76
##
## ...Evaluating: start of batch 7; got logs: Named list()
##
## ...Evaluating: end of batch 7; got logs: List of 2
## $ loss : num 4.74
## $ mean_absolute_error: num 1.76
##
## ...Evaluating: start of batch 8; got logs: Named list()
##
## ...Evaluating: end of batch 8; got logs: List of 2
## $ loss : num 4.67
## $ mean_absolute_error: num 1.75
##
## Stop testing; got logs: List of 2
## $ loss : num 4.67
## $ mean_absolute_error: num 1.75
```
``` r
res <- model |> predict(
mnist$test$x,
batch_size = 128, verbose = 0,
callbacks = list(callback_custom())
)
```
```
## Start predicting; got logs: Named list()
##
## ...Predicting: start of batch 1; got logs: Named list()
##
## ...Predicting: end of batch 1; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 2; got logs: Named list()
##
## ...Predicting: end of batch 2; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 3; got logs: Named list()
##
## ...Predicting: end of batch 3; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 4; got logs: Named list()
##
## ...Predicting: end of batch 4; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 5; got logs: Named list()
##
## ...Predicting: end of batch 5; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 6; got logs: Named list()
##
## ...Predicting: end of batch 6; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 7; got logs: Named list()
##
## ...Predicting: end of batch 7; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(128, 1), dtype=float32, numpy=…>
##
## ...Predicting: start of batch 8; got logs: Named list()
##
## ...Predicting: end of batch 8; got logs: List of 1
## $ outputs:<tf.Tensor: shape=(104, 1), dtype=float32, numpy=…>
##
## Stop predicting; got logs: Named list()
```
### Usage of `logs` list
The `logs` named list contains the loss value, and all the metrics at the end of a batch or
epoch. Example includes the loss and mean absolute error.
``` r
callback_print_loss_and_mae <- Callback(
"LossAndErrorPrintingCallback",
on_train_batch_end = function(batch, logs = NULL)
cat(sprintf("Up to batch %i, the average loss is %7.2f.\n",
batch, logs$loss)),
on_test_batch_end = function(batch, logs = NULL)
cat(sprintf("Up to batch %i, the average loss is %7.2f.\n",
batch, logs$loss)),
on_epoch_end = function(epoch, logs = NULL)
cat(sprintf(
"The average loss for epoch %2i is %9.2f and mean absolute error is %7.2f.\n",
epoch, logs$loss, logs$mean_absolute_error
))
)
model <- get_model()
model |> fit(
mnist$train$x, mnist$train$y,
epochs = 2, verbose = 0, batch_size = 128,
callbacks = list(callback_print_loss_and_mae())
)
```
```
## Up to batch 1, the average loss is 25.12.
## Up to batch 2, the average loss is 398.92.
## Up to batch 3, the average loss is 274.04.
## Up to batch 4, the average loss is 208.32.
## Up to batch 5, the average loss is 168.15.
## Up to batch 6, the average loss is 141.31.
## Up to batch 7, the average loss is 122.19.
## Up to batch 8, the average loss is 110.05.
## The average loss for epoch 1 is 110.05 and mean absolute error is 5.79.
## Up to batch 1, the average loss is 4.71.
## Up to batch 2, the average loss is 4.74.
## Up to batch 3, the average loss is 4.81.
## Up to batch 4, the average loss is 5.07.
## Up to batch 5, the average loss is 5.08.
## Up to batch 6, the average loss is 5.09.
## Up to batch 7, the average loss is 5.19.
## Up to batch 8, the average loss is 5.51.
## The average loss for epoch 2 is 5.51 and mean absolute error is 1.90.
```
``` r
res = model |> evaluate(
mnist$test$x, mnist$test$y,
verbose = 0, batch_size = 128,
callbacks = list(callback_print_loss_and_mae())
)
```
```
## Up to batch 1, the average loss is 15.86.
## Up to batch 2, the average loss is 16.13.
## Up to batch 3, the average loss is 16.02.
## Up to batch 4, the average loss is 16.11.
## Up to batch 5, the average loss is 16.23.
## Up to batch 6, the average loss is 16.68.
## Up to batch 7, the average loss is 16.61.
## Up to batch 8, the average loss is 16.54.
```
For more information about callbacks, you can check out the [Keras callback API documentation](https://keras3.posit.co/reference/index.html#callbacks).
## Usage of `self$model` attribute
In addition to receiving log information when one of their methods is called,
callbacks have access to the model associated with the current round of
training/evaluation/inference: `self$model`.
Here are of few of the things you can do with `self$model` in a callback:
- Set `self$model$stop_training <- TRUE` to immediately interrupt training.
- Mutate hyperparameters of the optimizer (available as `self$model$optimizer`),
such as `self$model$optimizer$learning_rate`.
- Save the model at period intervals.
- Record the output of `model |> predict()` on a few test samples at the end of each
epoch, to use as a sanity check during training.
- Extract visualizations of intermediate features at the end of each epoch, to monitor
what the model is learning over time.
- etc.
Let's see this in action in a couple of examples.
## Examples of Keras callback applications
### Early stopping at minimum loss
This first example shows the creation of a `Callback` that stops training when the
minimum of loss has been reached, by setting the attribute `self$model$stop_training`
(boolean). Optionally, you can provide an argument `patience` to specify how many
epochs we should wait before stopping after having reached a local minimum.
`callback_early_stopping()` provides a more complete and general implementation.
``` r
callback_early_stopping_at_min_loss <- Callback(
"EarlyStoppingAtMinLoss",
`__doc__` =
"Stop training when the loss is at its min, i.e. the loss stops decreasing.
Arguments:
patience: Number of epochs to wait after min has been hit. After this
number of no improvement, training stops.
",
initialize = function(patience = 0) {
super$initialize()
self$patience <- patience
# best_weights to store the weights at which the minimum loss occurs.
self$best_weights <- NULL
},
on_train_begin = function(logs = NULL) {
# The number of epoch it has waited when loss is no longer minimum.
self$wait <- 0
# The epoch the training stops at.
self$stopped_epoch <- 0
# Initialize the best as infinity.
self$best <- Inf
},
on_epoch_end = function(epoch, logs = NULL) {
current <- logs$loss
if (current < self$best) {
self$best <- current
self$wait <- 0L
# Record the best weights if current results is better (less).
self$best_weights <- get_weights(self$model)
} else {
add(self$wait) <- 1L
if (self$wait >= self$patience) {
self$stopped_epoch <- epoch
self$model$stop_training <- TRUE
cat("Restoring model weights from the end of the best epoch.\n")
model$set_weights(self$best_weights)
}
}
},
on_train_end = function(logs = NULL)
if (self$stopped_epoch > 0)
cat(sprintf("Epoch %05d: early stopping\n", self$stopped_epoch + 1))
)
`add<-` <- `+`
model <- get_model()
model |> fit(
mnist$train$x,
mnist$train$y,
epochs = 30,
batch_size = 64,
verbose = 0,
callbacks = list(callback_print_loss_and_mae(),
callback_early_stopping_at_min_loss())
)
```
```
## Up to batch 1, the average loss is 30.54.
## Up to batch 2, the average loss is 513.27.
## Up to batch 3, the average loss is 352.60.
## Up to batch 4, the average loss is 266.37.
## Up to batch 5, the average loss is 214.68.
## Up to batch 6, the average loss is 179.97.
## Up to batch 7, the average loss is 155.06.
## Up to batch 8, the average loss is 136.59.
## Up to batch 9, the average loss is 121.96.
## Up to batch 10, the average loss is 110.28.
## Up to batch 11, the average loss is 100.72.
## Up to batch 12, the average loss is 92.71.
## Up to batch 13, the average loss is 85.95.
## Up to batch 14, the average loss is 80.21.
## Up to batch 15, the average loss is 75.17.
## Up to batch 16, the average loss is 72.48.
## The average loss for epoch 1 is 72.48 and mean absolute error is 4.08.
## Up to batch 1, the average loss is 7.98.
## Up to batch 2, the average loss is 9.92.
## Up to batch 3, the average loss is 12.88.
## Up to batch 4, the average loss is 16.61.
## Up to batch 5, the average loss is 20.49.
## Up to batch 6, the average loss is 26.14.
## Up to batch 7, the average loss is 30.44.
## Up to batch 8, the average loss is 33.76.
## Up to batch 9, the average loss is 36.32.
## Up to batch 10, the average loss is 35.26.
## Up to batch 11, the average loss is 34.22.
## Up to batch 12, the average loss is 33.53.
## Up to batch 13, the average loss is 32.84.
## Up to batch 14, the average loss is 31.80.
## Up to batch 15, the average loss is 31.39.
## Up to batch 16, the average loss is 31.45.
## The average loss for epoch 2 is 31.45 and mean absolute error is 4.82.
## Up to batch 1, the average loss is 39.60.
## Up to batch 2, the average loss is 41.95.
## Up to batch 3, the average loss is 41.29.
## Up to batch 4, the average loss is 36.77.
## Up to batch 5, the average loss is 32.08.
## Up to batch 6, the average loss is 28.17.
## Up to batch 7, the average loss is 25.33.
## Up to batch 8, the average loss is 23.56.
## Up to batch 9, the average loss is 22.28.
## Up to batch 10, the average loss is 21.22.
## Up to batch 11, the average loss is 20.87.
## Up to batch 12, the average loss is 22.25.
## Up to batch 13, the average loss is 25.08.
## Up to batch 14, the average loss is 27.87.
## Up to batch 15, the average loss is 31.72.
## Up to batch 16, the average loss is 33.21.
## The average loss for epoch 3 is 33.21 and mean absolute error is 4.79.
## Restoring model weights from the end of the best epoch.
## Epoch 00004: early stopping
```
### Learning rate scheduling
In this example, we show how a custom Callback can be used to dynamically change the
learning rate of the optimizer during the course of training.
See `keras$callbacks$LearningRateScheduler` for a more general implementations (in RStudio, press F1 while the cursor is over `LearningRateScheduler` and a browser will open to [this page](https://www.tensorflow.org/versions/r2.5/api_docs/python/tf/keras/callbacks/LearningRateScheduler)).
``` r
callback_custom_learning_rate_scheduler <- Callback(
"CustomLearningRateScheduler",
`__doc__` =
"Learning rate scheduler which sets the learning rate according to schedule.
Arguments:
schedule: a function that takes an epoch index
(integer, indexed from 0) and current learning rate
as inputs and returns a new learning rate as output (float).
",
initialize = function(schedule) {
super$initialize()
self$schedule <- schedule
},
on_epoch_begin = function(epoch, logs = NULL) {
## When in doubt about what types of objects are in scope (e.g., self$model)
## use a debugger to interact with the actual objects at the console!
# browser()
if (!"learning_rate" %in% names(self$model$optimizer))
stop('Optimizer must have a "learning_rate" attribute.')
# # Get the current learning rate from model's optimizer.
# use as.numeric() to convert the keras variablea to an R numeric
lr <- as.numeric(self$model$optimizer$learning_rate)
# # Call schedule function to get the scheduled learning rate.
scheduled_lr <- self$schedule(epoch, lr)
# # Set the value back to the optimizer before this epoch starts
optimizer <- self$model$optimizer
optimizer$learning_rate <- scheduled_lr
cat(sprintf("\nEpoch %03d: Learning rate is %6.4f.\n", epoch, scheduled_lr))
}
)
LR_SCHEDULE <- tibble::tribble(
~start_epoch, ~learning_rate,
0, 0.1,
3, 0.05,
6, 0.01,
9, 0.005,
12, 0.001,
)
last <- function(x) x[length(x)]
lr_schedule <- function(epoch, learning_rate) {
"Helper function to retrieve the scheduled learning rate based on epoch."
with(LR_SCHEDULE, learning_rate[last(which(epoch >= start_epoch))])
}
model <- get_model()
model |> fit(
mnist$train$x,
mnist$train$y,
epochs = 14,
batch_size = 64,
verbose = 0,
callbacks = list(
callback_print_loss_and_mae(),
callback_custom_learning_rate_scheduler(lr_schedule)
)
)
```
```
##
## Epoch 001: Learning rate is 0.1000.
## Up to batch 1, the average loss is 29.36.
## Up to batch 2, the average loss is 513.95.
## Up to batch 3, the average loss is 352.70.
## Up to batch 4, the average loss is 266.46.
## Up to batch 5, the average loss is 214.73.
## Up to batch 6, the average loss is 180.00.
## Up to batch 7, the average loss is 155.05.
## Up to batch 8, the average loss is 136.64.
## Up to batch 9, the average loss is 121.97.
## Up to batch 10, the average loss is 110.30.
## Up to batch 11, the average loss is 100.76.
## Up to batch 12, the average loss is 92.74.
## Up to batch 13, the average loss is 85.95.
## Up to batch 14, the average loss is 80.18.
## Up to batch 15, the average loss is 75.11.
## Up to batch 16, the average loss is 72.38.
## The average loss for epoch 1 is 72.38 and mean absolute error is 4.04.
##
## Epoch 002: Learning rate is 0.1000.
## Up to batch 1, the average loss is 6.95.
## Up to batch 2, the average loss is 8.71.
## Up to batch 3, the average loss is 11.42.
## Up to batch 4, the average loss is 15.15.
## Up to batch 5, the average loss is 19.28.
## Up to batch 6, the average loss is 25.54.
## Up to batch 7, the average loss is 30.38.
## Up to batch 8, the average loss is 33.95.
## Up to batch 9, the average loss is 36.58.
## Up to batch 10, the average loss is 35.46.
## Up to batch 11, the average loss is 34.34.
## Up to batch 12, the average loss is 33.51.
## Up to batch 13, the average loss is 32.67.
## Up to batch 14, the average loss is 31.54.
## Up to batch 15, the average loss is 31.05.
## Up to batch 16, the average loss is 31.09.
## The average loss for epoch 2 is 31.09 and mean absolute error is 4.77.
##
## Epoch 003: Learning rate is 0.0500.
## Up to batch 1, the average loss is 40.40.
## Up to batch 2, the average loss is 22.33.
## Up to batch 3, the average loss is 16.18.
## Up to batch 4, the average loss is 13.09.
## Up to batch 5, the average loss is 11.48.
## Up to batch 6, the average loss is 10.21.
## Up to batch 7, the average loss is 9.22.
## Up to batch 8, the average loss is 8.70.
## Up to batch 9, the average loss is 8.16.
## Up to batch 10, the average loss is 7.80.
## Up to batch 11, the average loss is 7.50.
## Up to batch 12, the average loss is 7.17.
## Up to batch 13, the average loss is 6.89.
## Up to batch 14, the average loss is 6.70.
## Up to batch 15, the average loss is 6.52.
## Up to batch 16, the average loss is 6.54.
## The average loss for epoch 3 is 6.54 and mean absolute error is 1.93.
##
## Epoch 004: Learning rate is 0.0500.
## Up to batch 1, the average loss is 8.74.
## Up to batch 2, the average loss is 8.34.
## Up to batch 3, the average loss is 9.09.
## Up to batch 4, the average loss is 9.72.
## Up to batch 5, the average loss is 10.48.
## Up to batch 6, the average loss is 11.69.
## Up to batch 7, the average loss is 11.83.
## Up to batch 8, the average loss is 11.56.
## Up to batch 9, the average loss is 11.24.
## Up to batch 10, the average loss is 10.84.
## Up to batch 11, the average loss is 10.66.
## Up to batch 12, the average loss is 10.44.
## Up to batch 13, the average loss is 10.21.
## Up to batch 14, the average loss is 10.06.
## Up to batch 15, the average loss is 10.00.
## Up to batch 16, the average loss is 10.20.
## The average loss for epoch 4 is 10.20 and mean absolute error is 2.71.
##
## Epoch 005: Learning rate is 0.0500.
## Up to batch 1, the average loss is 17.26.
## Up to batch 2, the average loss is 14.09.
## Up to batch 3, the average loss is 12.67.
## Up to batch 4, the average loss is 11.44.
## Up to batch 5, the average loss is 10.54.
## Up to batch 6, the average loss is 10.10.
## Up to batch 7, the average loss is 9.53.
## Up to batch 8, the average loss is 9.17.
## Up to batch 9, the average loss is 8.78.
## Up to batch 10, the average loss is 8.49.
## Up to batch 11, the average loss is 8.50.
## Up to batch 12, the average loss is 8.59.
## Up to batch 13, the average loss is 8.68.
## Up to batch 14, the average loss is 8.86.
## Up to batch 15, the average loss is 9.17.
## Up to batch 16, the average loss is 9.53.
## The average loss for epoch 5 is 9.53 and mean absolute error is 2.58.
##
## Epoch 006: Learning rate is 0.0100.
## Up to batch 1, the average loss is 17.04.
## Up to batch 2, the average loss is 14.85.
## Up to batch 3, the average loss is 11.53.
## Up to batch 4, the average loss is 9.65.
## Up to batch 5, the average loss is 8.44.
## Up to batch 6, the average loss is 7.50.
## Up to batch 7, the average loss is 6.74.
## Up to batch 8, the average loss is 6.56.
## Up to batch 9, the average loss is 6.18.
## Up to batch 10, the average loss is 5.87.
## Up to batch 11, the average loss is 5.63.
## Up to batch 12, the average loss is 5.45.
## Up to batch 13, the average loss is 5.23.
## Up to batch 14, the average loss is 5.12.
## Up to batch 15, the average loss is 4.96.
## Up to batch 16, the average loss is 4.91.
## The average loss for epoch 6 is 4.91 and mean absolute error is 1.67.
##
## Epoch 007: Learning rate is 0.0100.
## Up to batch 1, the average loss is 3.65.
## Up to batch 2, the average loss is 3.04.
## Up to batch 3, the average loss is 2.88.
## Up to batch 4, the average loss is 2.85.
## Up to batch 5, the average loss is 2.88.
## Up to batch 6, the average loss is 2.81.
## Up to batch 7, the average loss is 2.70.
## Up to batch 8, the average loss is 2.96.
## Up to batch 9, the average loss is 2.96.
## Up to batch 10, the average loss is 2.93.
## Up to batch 11, the average loss is 2.95.
## Up to batch 12, the average loss is 2.98.
## Up to batch 13, the average loss is 2.97.
## Up to batch 14, the average loss is 3.01.
## Up to batch 15, the average loss is 3.00.
## Up to batch 16, the average loss is 3.05.
## The average loss for epoch 7 is 3.05 and mean absolute error is 1.34.
##
## Epoch 008: Learning rate is 0.0100.
## Up to batch 1, the average loss is 3.69.
## Up to batch 2, the average loss is 3.21.
## Up to batch 3, the average loss is 3.00.
## Up to batch 4, the average loss is 2.91.
## Up to batch 5, the average loss is 2.94.
## Up to batch 6, the average loss is 2.85.
## Up to batch 7, the average loss is 2.72.
## Up to batch 8, the average loss is 2.95.
## Up to batch 9, the average loss is 2.97.
## Up to batch 10, the average loss is 2.93.
## Up to batch 11, the average loss is 2.96.
## Up to batch 12, the average loss is 2.98.
## Up to batch 13, the average loss is 2.99.
## Up to batch 14, the average loss is 3.05.
## Up to batch 15, the average loss is 3.08.
## Up to batch 16, the average loss is 3.14.
## The average loss for epoch 8 is 3.14 and mean absolute error is 1.36.
##
## Epoch 009: Learning rate is 0.0050.
## Up to batch 1, the average loss is 3.71.
## Up to batch 2, the average loss is 2.93.
## Up to batch 3, the average loss is 2.76.
## Up to batch 4, the average loss is 2.70.
## Up to batch 5, the average loss is 2.76.
## Up to batch 6, the average loss is 2.69.
## Up to batch 7, the average loss is 2.57.
## Up to batch 8, the average loss is 2.79.
## Up to batch 9, the average loss is 2.80.
## Up to batch 10, the average loss is 2.77.
## Up to batch 11, the average loss is 2.79.
## Up to batch 12, the average loss is 2.80.
## Up to batch 13, the average loss is 2.78.
## Up to batch 14, the average loss is 2.81.
## Up to batch 15, the average loss is 2.80.
## Up to batch 16, the average loss is 2.83.
## The average loss for epoch 9 is 2.83 and mean absolute error is 1.28.
##
## Epoch 010: Learning rate is 0.0050.
## Up to batch 1, the average loss is 3.02.
## Up to batch 2, the average loss is 2.69.
## Up to batch 3, the average loss is 2.58.
## Up to batch 4, the average loss is 2.57.
## Up to batch 5, the average loss is 2.65.
## Up to batch 6, the average loss is 2.60.
## Up to batch 7, the average loss is 2.48.
## Up to batch 8, the average loss is 2.72.
## Up to batch 9, the average loss is 2.74.
## Up to batch 10, the average loss is 2.71.
## Up to batch 11, the average loss is 2.74.
## Up to batch 12, the average loss is 2.75.
## Up to batch 13, the average loss is 2.74.
## Up to batch 14, the average loss is 2.77.
## Up to batch 15, the average loss is 2.77.
## Up to batch 16, the average loss is 2.80.
## The average loss for epoch 10 is 2.80 and mean absolute error is 1.27.
##
## Epoch 011: Learning rate is 0.0050.
## Up to batch 1, the average loss is 3.01.
## Up to batch 2, the average loss is 2.69.
## Up to batch 3, the average loss is 2.58.
## Up to batch 4, the average loss is 2.56.
## Up to batch 5, the average loss is 2.63.
## Up to batch 6, the average loss is 2.58.
## Up to batch 7, the average loss is 2.47.
## Up to batch 8, the average loss is 2.70.
## Up to batch 9, the average loss is 2.72.
## Up to batch 10, the average loss is 2.69.
## Up to batch 11, the average loss is 2.71.
## Up to batch 12, the average loss is 2.72.
## Up to batch 13, the average loss is 2.71.
## Up to batch 14, the average loss is 2.75.
## Up to batch 15, the average loss is 2.74.
## Up to batch 16, the average loss is 2.77.
## The average loss for epoch 11 is 2.77 and mean absolute error is 1.27.
##
## Epoch 012: Learning rate is 0.0010.
## Up to batch 1, the average loss is 2.96.
## Up to batch 2, the average loss is 2.53.
## Up to batch 3, the average loss is 2.47.
## Up to batch 4, the average loss is 2.46.
## Up to batch 5, the average loss is 2.54.
## Up to batch 6, the average loss is 2.48.
## Up to batch 7, the average loss is 2.39.
## Up to batch 8, the average loss is 2.60.
## Up to batch 9, the average loss is 2.62.
## Up to batch 10, the average loss is 2.59.
## Up to batch 11, the average loss is 2.61.
## Up to batch 12, the average loss is 2.62.
## Up to batch 13, the average loss is 2.60.
## Up to batch 14, the average loss is 2.64.
## Up to batch 15, the average loss is 2.62.
## Up to batch 16, the average loss is 2.64.
## The average loss for epoch 12 is 2.64 and mean absolute error is 1.24.
##
## Epoch 013: Learning rate is 0.0010.
## Up to batch 1, the average loss is 2.82.
## Up to batch 2, the average loss is 2.46.
## Up to batch 3, the average loss is 2.42.
## Up to batch 4, the average loss is 2.42.
## Up to batch 5, the average loss is 2.50.
## Up to batch 6, the average loss is 2.45.
## Up to batch 7, the average loss is 2.36.
## Up to batch 8, the average loss is 2.57.
## Up to batch 9, the average loss is 2.59.
## Up to batch 10, the average loss is 2.57.
## Up to batch 11, the average loss is 2.59.
## Up to batch 12, the average loss is 2.60.
## Up to batch 13, the average loss is 2.59.
## Up to batch 14, the average loss is 2.62.
## Up to batch 15, the average loss is 2.61.
## Up to batch 16, the average loss is 2.63.
## The average loss for epoch 13 is 2.63 and mean absolute error is 1.23.
##
## Epoch 014: Learning rate is 0.0010.
## Up to batch 1, the average loss is 2.79.
## Up to batch 2, the average loss is 2.44.
## Up to batch 3, the average loss is 2.40.
## Up to batch 4, the average loss is 2.41.
## Up to batch 5, the average loss is 2.49.
## Up to batch 6, the average loss is 2.44.
## Up to batch 7, the average loss is 2.34.
## Up to batch 8, the average loss is 2.56.
## Up to batch 9, the average loss is 2.58.
## Up to batch 10, the average loss is 2.56.
## Up to batch 11, the average loss is 2.58.
## Up to batch 12, the average loss is 2.59.
## Up to batch 13, the average loss is 2.58.
## Up to batch 14, the average loss is 2.61.
## Up to batch 15, the average loss is 2.60.
## Up to batch 16, the average loss is 2.62.
## The average loss for epoch 14 is 2.62 and mean absolute error is 1.23.
```
### Built-in Keras callbacks
Be sure to check out the existing Keras callbacks by
reading the [API docs](https://keras3.posit.co/reference/index.html#callbacks).
Applications include logging to CSV, saving
the model, visualizing metrics in TensorBoard, and a lot more!