Loading a data set in collaboratory download#

Tf.data.Dataset, the dataset requested, or if split is None, a Public GCS bucket before building it locally. If not set,Ĭache_dir and manual_dir will automatically be deduced from data_dir.ĭict (optional), keyword arguments passed toīool, if True, tfds.load will see if the dataset exists on the

Loading a data set in collaboratory download#

To control where to download and extract the cached data. data_dir will be passed throughĭict (optional) keyword arguments passed to ( tf.data.Dataset, ), the latter containing theĭict (optional), keyword arguments to be passed to the seed, num parallel reads.).īool, if True, tfds.load will return the tuple Tfds.ReadConfig, Additional options to configure the input The structure should match the feature structure, but onlyĬustomized feature keys need to be present. Nested dict of Decoder objects which allow to customize theĭecoding. Tf.data.Dataset will have a dictionary with all the features. If True and the data is already in data_dir,īool, if True, the returned tf.data.Dataset will have aĢ-tuple structure (input, label) according toī_keys.

If False, data is expected to be inĭata_dir. If batch_size=-1, will returnīool, whether to shuffle the input files. Variable length features will be 0-padded. Int, if set, add a batch dimension to examples.

Defaults to the value of theĮnvironment variable TFDS_DATA_DIR, if set, otherwise falls back to See our split APIĪll splits in a Dictĭirectory to read/write data. To use the 'zoo' config and pass to the builder keyword arguments Would use the FooBar dataset passing the keyword arguments a=True andī=3 (for builders with configs, it would be 'foo_bar/zoo/a=True,b=3' For convenience, the name parameter can contain comma-separated Version can be generated, but old versions can be read if they are present ForĮxample, 'movielens/25m-ratings' (for the latest version of The config and version can also be specified

Str, the registered name of the DatasetBuilder (the snake case Warning: calling this function might potentially trigger the download You can pass the return value to tfds.as_numpy. If you'd like NumPy arrays instead of tf.data.Datasets or tf.Tensors, Load the tf.data.Dataset object: ds = builder.as_dataset( Generate the data (when download=True): builder.download_and_prepare(**download_and_prepare_kwargs) Training a neural network on MNIST with Kerasįetch the by name: builder = tfds.builder(name, data_dir=data_dir, **builder_kwargs).Distributed training with Core APIs and DTensor.Multilayer perceptrons for digit recognition with Core APIs.Read_config: Optional = None,īuilder_kwargs: Optional] = None,ĭownload_and_prepare_kwargs: Optional] = None,Īs_dataset_kwargs: Optional] = None, Split: Optional] = None,ĭata_dir: Union = None,ĭecoders: Optional] = None, Loads the named dataset into a tf.data.Dataset.