Interface to 'TensorFlow' Datasets


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Documentation for package ‘tfdatasets’ version 1.13.1

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csv_record_spec Specification for reading a record from a text file with delimited values
dataset_batch Combines consecutive elements of this dataset into batches.
dataset_cache Caches the elements in this dataset.
dataset_concatenate Creates a dataset by concatenating given dataset with this dataset.
dataset_decode_delim Transform a dataset with delimted text lines into a dataset with named columns
dataset_filter Filter a dataset by a predicate
dataset_flat_map Maps map_func across this dataset and flattens the result.
dataset_interleave Maps map_func across this dataset, and interleaves the results
dataset_map Map a function across a dataset.
dataset_map_and_batch Fused implementation of dataset_map() and dataset_batch()
dataset_padded_batch Combines consecutive elements of this dataset into padded batches
dataset_prefetch Creates a Dataset that prefetches elements from this dataset.
dataset_prefetch_to_device A transformation that prefetches dataset values to the given 'device'
dataset_prepare Prepare a dataset for analysis
dataset_repeat Repeats a dataset count times.
dataset_shard Creates a dataset that includes only 1 / num_shards of this dataset.
dataset_shuffle Randomly shuffles the elements of this dataset.
dataset_shuffle_and_repeat Shuffles and repeats a dataset returning a new permutation for each epoch.
dataset_skip Creates a dataset that skips count elements from this dataset
dataset_take Creates a dataset with at most count elements from this dataset
delim_record_spec Specification for reading a record from a text file with delimited values
file_list_dataset A dataset of all files matching a pattern
fixed_length_record_dataset A dataset of fixed-length records from one or more binary files.
input_fn Construct a tfestimators input function from a dataset
input_fn.tf_dataset Construct a tfestimators input function from a dataset
iterator_get_next Get next element from iterator
iterator_initializer An operation that should be run to initialize this iterator.
iterator_make_initializer Create an operation that can be run to initialize this iterator
iterator_string_handle String-valued tensor that represents this iterator
make-iterator Creates an iterator for enumerating the elements of this dataset.
make_csv_dataset Reads CSV files into a batched dataset
make_iterator_from_string_handle Creates an iterator for enumerating the elements of this dataset.
make_iterator_from_structure Creates an iterator for enumerating the elements of this dataset.
make_iterator_initializable Creates an iterator for enumerating the elements of this dataset.
make_iterator_one_shot Creates an iterator for enumerating the elements of this dataset.
next_batch Tensor(s) for retreiving the next batch from a dataset
output_shapes Output types and shapes
output_types Output types and shapes
out_of_range_handler Execute code that traverses a dataset until an out of range condition occurs
range_dataset Creates a dataset of a step-separated range of values.
read_files Read a dataset from a set of files
sample_from_datasets Samples elements at random from the datasets in 'datasets'.
sparse_tensor_slices_dataset Splits each rank-N 'tf$SparseTensor' in this dataset row-wise.
sqlite_dataset A dataset consisting of the results from a SQL query
sql_dataset A dataset consisting of the results from a SQL query
sql_record_spec A dataset consisting of the results from a SQL query
tensors_dataset Creates a dataset with a single element, comprising the given tensors.
tensor_slices_dataset Creates a dataset whose elements are slices of the given tensors.
text_line_dataset A dataset comprising lines from one or more text files.
tfrecord_dataset A dataset comprising records from one or more TFRecord files.
tsv_record_spec Specification for reading a record from a text file with delimited values
until_out_of_range Execute code that traverses a dataset until an out of range condition occurs
with_dataset Execute code that traverses a dataset
zip_datasets Creates a dataset by zipping together the given datasets.