tfrecord_dataset: A dataset comprising records from one or more TFRecord files.

In tfdatasets: Interface to 'TensorFlow' Datasets

A dataset comprising records from one or more TFRecord files.

Description

A dataset comprising records from one or more TFRecord files.

Usage

tfrecord_dataset(
  filenames,
  compression_type = NULL,
  buffer_size = NULL,
  num_parallel_reads = NULL
)

Arguments

filenames	String(s) specifying one or more filenames
compression_type	A string, one of: NULL (no compression), "ZLIB", or "GZIP".
buffer_size	An integer representing the number of bytes in the read buffer. (0 means no buffering).
num_parallel_reads	An integer representing the number of files to read in parallel. Defaults to reading files sequentially.

Details

If the dataset encodes a set of TFExample instances, then they can be decoded into named records using the dataset_map() function (see example below).

Examples

## Not run: 

# Creates a dataset that reads all of the examples from two files, and extracts
# the image and label features.
filenames <- c("/var/data/file1.tfrecord", "/var/data/file2.tfrecord")
dataset <- tfrecord_dataset(filenames) %>%
  dataset_map(function(example_proto) {
    features <- list(
      image = tf$FixedLenFeature(shape(), tf$string, default_value = ""),
      label = tf$FixedLenFeature(shape(), tf$int32, default_value = 0L)
    )
    tf$parse_single_example(example_proto, features)
  })

## End(Not run)

tfdatasets documentation built on June 30, 2022, 1:04 a.m.