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tf.compat.v1.data.FixedLengthRecordDataset

A Dataset of fixed-length records from one or more binary files.

Inherits From: Dataset, Dataset

filenames A tf.string tensor or tf.data.Dataset containing one or more filenames.
record_bytes A tf.int64 scalar representing the number of bytes in each record.
header_bytes (Optional.) A tf.int64 scalar representing the number of bytes to skip at the start of a file.
footer_bytes (Optional.) A tf.int64 scalar representing the number of bytes to ignore at the end of a file.
buffer_size (Optional.) A tf.int64 scalar representing the number of bytes to buffer when reading.
compression_type (Optional.) A tf.string scalar evaluating to one of "" (no compression), "ZLIB", or "GZIP".
num_parallel_reads (Optional.) A tf.int64 scalar representing the number of files to read in parallel. If greater than one, the records of files read in parallel are outputted in an interleaved order. If your input pipeline is I/O bottlenecked, consider setting this parameter to a value greater than one to parallelize the I/O. If None, files will be read sequentially.

element_spec The type specification of an element of this dataset.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset.element_spec
TensorSpec(shape=(), dtype=tf.int32, name=None)

For more information, read this guide.

output_classes Returns the class of each component of an element of this dataset. (deprecated)

output_shapes Returns the shape of each component of an element of this dataset. (deprecated)
output_types Returns the type of each component of an element of this dataset. (deprecated)