Package 'tfevents'

Description

Coerce an object to a event.

Usage

as_event(x, step, wall_time, ...)

Arguments

Value

A event vctr with class <tfevents_event>.

Extending as_event

as_event is an S3 generic and you can implement method for your own class. We don't export the event constructor though, so you should implement it in terms of other as_event methods.

Examples

as_event(list(hello = 1), step = 1, wall_time = 1)

Description

Collects all events of a kind in a single data.frame ready for analysis.

Usage

collect_events(
  logdir = get_default_logdir(),
  n = NULL,
  type = c("any", "summary", "scalar")
)

events_logdir(logdir = get_default_logdir())

Arguments

Value

A tibble with the collected events.

Functions

• events_logdir(): Creates a connection to a logdir that can be reused to read further events later.

Examples

temp <- tempfile()
with_logdir(temp, {
  for(i in 1:5) {
    log_event(my_log = runif(1))
  }
})
# collect all events in files, including file description events
collect_events(temp)
# collect summaries in the logdir
collect_events(temp, type = "summary")
# collect only scalar events
collect_events(temp, type = "scalar")

Description

log_event() has a notion of default logdir, so you don't need to specify it at every call. These functions allow you to query and the current logdir.

Usage

get_default_logdir()

set_default_logdir(logdir = "logs")

with_logdir(logdir, code)

local_logdir(logdir, .env = parent.frame())

Arguments

Value

The logdir for get_default_logdir() otherwise invisibly returns NULL

Functions

• set_default_logdir(): Modifies the default logdir.

• with_logdir(): Temporarily modify the default logdir.

• local_logdir(): Temporarily modify thedefault logdir.

Examples

temp <- tempfile()
get_default_logdir()
with_logdir(temp, {
 print(get_default_logdir())
})

Description

Global step counters

Usage

get_global_step(increment = TRUE)

set_global_step(step)

Arguments

Details

tfevents tracks and automatically increased the step counter whenever log_event() is called. Note that, it maintains a separate step counter for each root logdir, thus if you change the logdir using set_default_logdir() or with_logdir(), a different step counter will be used.

Value

The global step value for the default logdir, when get_global_step, otherwise returns NULL invisibly.

Functions

• set_global_step(): Set the global step.

Examples

temp <- tempfile()
with_logdir(temp, {
  print(get_global_step())
  set_global_step(100)
  print(get_global_step())
})
print(get_global_step())

Description

Hparam object are used to describe names and domains of hyperparameters so TensorBoard UI can show additional information about them.

Usage

hparams_hparam(name, domain = NA, display_name = name, description = name)

Arguments

Value

A hparams_hparam object.

Note

A list of hparam values can be passed to log_hparams_config() so you define the hyperparameters that are tracked by the experiment.

Examples

hparams_hparam("optimizer", domain = c("adam", "sgd"))
hparams_hparam("num_units", domain = c(128, 512, 1024))
hparams_hparam("use_cnn", domain = c(TRUE, FALSE))
hparams_hparam("dropout", domain = c(min_value = 0, max_value = 0.5))

Description

Metric objects are passed to log_hparams_config() in order to define the collection of scalars that will be displayed in the HParams tab in TensorBoard.

Usage

hparams_metric(
  tag,
  group = NA,
  display_name = tag,
  description = tag,
  dataset_type = NA
)

Arguments

Value

A hparams_metric object.

Examples

hparams_metric("loss", group = "train")
hparams_metric("acc")

Description

Log event

Usage

log_event(..., step = get_global_step(increment = TRUE))

Arguments

Value

Invisibly returns the logged data.

Note

log_event() writes events to the default logdir. You can query the default logdir with get_default_logdir() and modify it with set_default_logdir(). You can also use the with_logdir() context switcher to temporarily modify the logdir.

Examples

temp <- tempfile()
with_logdir(temp, {
  log_event(
     train = list(loss = runif(1), acc = runif(1)),
     valid = list(loss = runif(1), acc = runif(1))
  )
})

Description

Log hyperaparameters

Usage

log_hparams(..., trial_id = NA, time_created_secs = get_wall_time())

summary_hparams(..., trial_id = NA, time_created_secs = get_wall_time())

Arguments

Details

This function should only be called once in a logdir and it will record the set of hyperparameters used in that run. Undefined behavior can happen if it's called more than once in a logdir - specially how TensorBoard behaves during visualization.

Value

A hyperparameter summary. USed for the side effects of logging the hyperparameter to the logdir.

Functions

• summary_hparams(): For advanced users only. It's recommended to use the log_hparams() function instead. Creates a hyperparameter summary that can be written with log_event().

See Also

log_hparams_config()

Examples

temp <- tempfile()
with_logdir(temp, {
  log_hparams(optimizer = "adam", num_units = 16)
})

Description

Logs the hyperaparameter configuration for a HyperParameter tuning experiment. It allows you to define the domain for each hyperparameters and what are the metrics that should be shown in the TensorBoard UI, along with configuring their display name and descriptions.

Usage

log_hparams_config(hparams, metrics, time_created_secs = get_wall_time())

summary_hparams_config(hparams, metrics, time_created_secs = get_wall_time())

Arguments

Value

Invisibly returns the HParam conffuration data as a summary object.

Functions

• summary_hparams_config(): For advanced users only. Creates a hyperaparameter configuration summary that can be logged with log_event().

Recommendations

When loging hyperparameter tuning experiments, the log directory organization is:

- root:
 - log_hparams_config(...)
 - run1:
   - log_hparams(...)
   - log_event(...)
 - run2:
   - log_hparams(...)
   - log_event(...)

Ie you should have a root logdir that will only contain the hyperaparameter config log, as created with log_hparams_config(). Then each run in the experiment will have it's own logdir as a child directory of the root logdir.

See Also

log_hparams()

Description

Audio summaries can be played withing the TensorBoard UI.

Usage

summary_audio(audio, ..., metadata = NULL, tag = NA)

## S3 method for class 'array'
summary_audio(audio, ..., sample_rate = 44100, metadata = NULL, tag = NA)

## S3 method for class 'raw'
summary_audio(audio, ..., metadata = NULL, tag = NA)

## S3 method for class 'blob'
summary_audio(audio, ..., metadata = NULL, tag = NA)

Arguments

Value

An audio summary that can be logged with log_event().

Methods (by class)

• summary_audio(array): Creates a summary from a 3D array with dimensions ⁠(batch_size, n_samples, n_channels)⁠. Values must be in the range ⁠[-1, 1]⁠.

• summary_audio(raw): Creates an audio summary from a raw vector containing a WAV encoded audio file.

• summary_audio(blob): Creates an audio summary from a blob (ie list of raw vectors) containing WAV encoded audio files.

See Also

Other summary: summary_histogram(), summary_image(), summary_scalar(), summary_text()

Examples

tmp <- tempfile()
with_logdir(tmp, {
  summary_audio(array(runif(100), dim = c(1,100, 1)))
})

Description

Writes an histogram for later analysis in TensorBoard's Histograms and Distributions tab.

Usage

summary_histogram(data, ..., metadata = NULL, tag = NA)

## S3 method for class 'numeric'
summary_histogram(data, ..., metadata = NULL, tag = NA, buckets = 30)

## S3 method for class 'array'
summary_histogram(data, ..., metadata = NULL, tag = NA, buckets = 30)

Arguments

Value

An histogram summary that can be logged with log_event().

Methods (by class)

• summary_histogram(numeric): Creates an histogram summary for a numeric vector.

• summary_histogram(array): Creates an histogram for array data.

See Also

Other summary: summary_audio(), summary_image(), summary_scalar(), summary_text()

Examples

temp <- tempfile()
with_logdir(temp, {
  for(i in 1:10) {
    log_event(x = summary_histogram(rnorm(10000)))
  }
})

Description

Creates a image summary

Usage

summary_image(img, ..., metadata = NULL, tag = NA)

## S3 method for class 'ggplot'
summary_image(img, ..., width = 480, height = 480, metadata = NULL, tag = NA)

## S3 method for class 'array'
summary_image(img, ..., metadata = NULL, tag = NA)

## S3 method for class 'blob'
summary_image(img, ..., width, height, colorspace, metadata = NULL, tag = NA)

## S3 method for class 'raw'
summary_image(img, ..., width, height, colorspace, metadata = NULL, tag = NA)

Arguments

Value

An image summary that can be logged with log_event().

Methods (by class)

• summary_image(ggplot): Cretes an image summary from a ggplot2 graph object. The ... will be forwarded to grDevices::png().

• summary_image(array): Creates an image from an R array. The array should be numeric, with values between 0 and 1. Dimensions should be ⁠(batch, height, width, channels)⁠.

• summary_image(blob): Creates an image from blob::blob() vctr of PNG encoded images, (eg using png::writePNG()). width, height and colorspace are recycled thus they can be a single scalar or a vector the same size of the images blob.

• summary_image(raw): Creates an image from a png encoded image. Eg, created with png::writePNG(). In this case you need to provide width, height and colorspace arguments.

See Also

Other summary: summary_audio(), summary_histogram(), summary_scalar(), summary_text()

Examples

tmp <- tempfile()
with_logdir(tmp, {
  summary_image(array(runif(100), dim = c(1,10, 10, 1)))
})

Description

Creates a summary metadata that can be passed to multiple summary_ functions.

Usage

summary_metadata(
  plugin_name,
  display_name = NA_character_,
  description = NA_character_,
  ...,
  plugin_content = NA
)

Arguments

Value

A summary_metadata object.

Examples

summary <- summary_scalar(1, metadata = summary_metadata("scalars"))

Description

Scalar event

Usage

summary_scalar(value, ..., metadata = NULL, tag = NA)

Arguments

Value

A ⁠<scalar_event>⁠ object.

See Also

Other summary: summary_audio(), summary_histogram(), summary_image(), summary_text()

Examples

temp <- tempfile()
with_logdir(temp, {
  log_event(loss = summary_scalar(1))
})

Description

Creates a text summary

Usage

summary_text(txt, ..., metadata = NULL, tag = NA)

## S3 method for class 'character'
summary_text(txt, ..., metadata = NULL, tag = NA)

Arguments

Value

A summary that can be logged with log_event().

Methods (by class)

• summary_text(character): Creates a summary from a scalar character vector.

See Also

Other summary: summary_audio(), summary_histogram(), summary_image(), summary_scalar()

Examples

temp <- tempfile()
with_logdir(temp, {
  log_event(
    x = "hello world",
    y = summary_text("hello world")
  )
})

Description

Summaries are complicated objects because they reflect the Protobuf object structure that are serialized in the tfevents records files. This function allows one to easily query vaues from summaries and will dispatch to the correct way to extract images, audio, text, etc from summary values.

Usage

value(x, ...)

## S3 method for class 'tfevents_summary_values'
value(x, ..., as_list = FALSE)

Arguments

Value

Depending on the type of the summary it returns an image, audio, text or scalar.

Methods (by class)

• value(tfevents_summary_values): Acess values from summary_values.

Examples

temp <- tempfile()
with_logdir(temp, {
  for(i in 1:5) {
    log_event(my_log = runif(1))
  }
})

# iterate over all events
summary <- collect_events(temp, n = 1, type = "summary")
value(summary$summary)