This allows a user to read data from a parquet file.

parquet_dataset(filenames, columns, output_types)

Arguments

filenames

A 0-D or 1-D tf.string tensor containing one or more filenames.

columns

A 0-D or 1-D tf.int32 tensor containing the columns to extract.

output_types

A tuple of tf.DType objects representing the types of the columns returned.

Examples

if (FALSE) { dtypes <- tf$python$framework$dtypes output_types <- reticulate::tuple( dtypes$bool, dtypes$int32, dtypes$int64, dtypes$float32, dtypes$float64) dataset <- parquet_dataset( filenames = list("testdata/parquet_cpp_example.parquet"), columns = list(0, 1, 2, 4, 5), output_types = output_types) %>% dataset_repeat(2) sess <- tf$Session() iterator <- make_iterator_one_shot(dataset) next_batch <- iterator_get_next(iterator) until_out_of_range({ batch <- sess$run(next_batch) print(batch) }) }