tfdatasets
Create efficient and fast data loading pipelines
Creating Datasets
| Function(s) | Description |
|---|---|
| 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. |
| sql_record_spec() sql_dataset() sqlite_dataset() | 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. |
| sparse_tensor_slices_dataset() | Splits each rank-N tf$SparseTensor in this dataset row-wise. |
| fixed_length_record_dataset() | A dataset of fixed-length records from one or more binary files. |
| file_list_dataset() | A dataset of all files matching a pattern |
| range_dataset() | Creates a dataset of a step-separated range of values. |
| read_files() | Read a dataset from a set of files |
| delim_record_spec() csv_record_spec() tsv_record_spec() | Specification for reading a record from a text file with delimited values |
| make_csv_dataset() | Reads CSV files into a batched dataset |
Transforming Datasets
| Function(s) | Description |
|---|---|
| dataset_map() | Map a function across a dataset. |
| dataset_map_and_batch() | Fused implementation of dataset_map() and dataset_batch() |
| dataset_prepare() | Prepare a dataset for analysis |
| dataset_skip() | Creates a dataset that skips count elements from this dataset |
| dataset_filter() | Filter a dataset by a predicate |
| 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_prefetch() | Creates a Dataset that prefetches elements from this dataset. |
| dataset_batch() | Combines consecutive elements of this dataset into batches. |
| dataset_repeat() | Repeats a dataset count times. |
| dataset_cache() | Caches the elements in this dataset. |
| dataset_take() | Creates a dataset with at most count elements from this dataset |
| dataset_flat_map() | Maps map_func across this dataset and flattens the result. |
| dataset_padded_batch() | Combines consecutive elements of this dataset into padded batches. |
| dataset_decode_delim() | Transform a dataset with delimted text lines into a dataset with named |
| columns | |
| dataset_concatenate() | Creates a dataset by concatenating given dataset with this dataset. |
| dataset_interleave() | Maps map_func across this dataset, and interleaves the results |
| dataset_prefetch_to_device() | A transformation that prefetches dataset values to the given device |
| dataset_window() | Combines input elements into a dataset of windows. |
| dataset_collect() | Collects a dataset |
| zip_datasets() | Creates a dataset by zipping together the given datasets. |
| sample_from_datasets() | Samples elements at random from the datasets in datasets. |
| with_dataset() | Execute code that traverses a dataset |
Dataset Properites
| Function(s) | Description |
|---|---|
| output_types() output_shapes() | Output types and shapes |
| output_types() output_shapes() | Output types and shapes |
Dataset Iterators
| Function(s) | Description |
|---|---|
| input_fn.tf_dataset() | Construct a tfestimators input function from a dataset |
| make_iterator_one_shot() make_iterator_initializable() make_iterator_from_structure() make_iterator_from_string_handle() | Creates an iterator for enumerating the elements of this dataset. |
| iterator_get_next() | Get next element from iterator |
| iterator_initializer() | An operation that should be run to initialize this iterator. |
| iterator_string_handle() | String-valued tensor that represents this iterator |
| iterator_make_initializer() | Create an operation that can be run to initialize this iterator |
| until_out_of_range() out_of_range_handler() | Execute code that traverses a dataset until an out of range condition occurs |
| next_batch() | Tensor(s) for retrieving the next batch from a dataset |
Feature Spec API
| Function(s) | Description |
|---|---|
| feature_spec() | Creates a feature specification. |
| dense_features() | Dense Features |
| dataset_use_spec() | Transform the dataset using the provided spec. |
| fit( |
Fits a feature specification. |
| scaler | List of pre-made scalers |
| scaler_min_max() | Creates an instance of a min max scaler |
| scaler_standard() | Creates an instance of a standard scaler |
| step_bucketized_column() | Creates bucketized columns |
| step_categorical_column_with_hash_bucket() | Creates a categorical column with hash buckets specification |
| step_categorical_column_with_identity() | Create a categorical column with identity |
| step_categorical_column_with_vocabulary_file() | Creates a categorical column with vocabulary file |
| step_categorical_column_with_vocabulary_list() | Creates a categorical column specification |
| step_crossed_column() | Creates crosses of categorical columns |
| step_embedding_column() | Creates embeddings columns |
| step_indicator_column() | Creates Indicator Columns |
| step_numeric_column() | Creates a numeric column specification |
| step_remove_column() | Creates a step that can remove columns |
| step_shared_embeddings_column() | Creates shared embeddings for categorical columns |
| steps | Steps for feature columns specification. |
| all_nominal() | Find all nominal variables. |
| all_numeric() | Speciy all numeric variables. |
| has_type() | Identify the type of the variable. |
| cur_info_env | Selectors |
| layer_input_from_dataset() | Creates a list of inputs from a dataset |
Data
| Function(s) | Description |
|---|---|
| hearts | Heart Disease Data Set |