Tfs photosounder manual#
Tfs photosounder code#
Tfs photosounder plus#

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Tfs photosounder code#

Note that a dataset builder is not aware that it isĪ reference to this instantiated builder.Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Namespace, then the namespace must be provided such that it can be set If this dataset is a community dataset, and therefore has a Includes the config if specified, the version, and the data_dir that should Returns a reference to the dataset produced by this dataset builder. The config files are read from the same package where the DatasetBuilder hasīeen defined, so those metadata might be wrong for legacy builders. View source get_metadata() -> dataset_metadata.DatasetMetadata The default builder config if there is one Need a method that uses the instance to get BUILDER_CONFIGS and Note that for dataset builders that cannot use the cls.BUILDER_CONFIGS, we Returns the default builder config if there is one. View source get_default_builder_config() -> Optional If there is not enough disk space available. Record files in which the dataset will be written. Optional str or file_adapters.FileFormat, format of the If information is present both in passed arguments and config files, configĭownloads and prepares dataset for reading.ĭirectory where downloaded files are stored. Sub-class should call this and add information not present in config files Returns the DatasetInfo using given kwargs and config files. The entire dataset in tf.Tensors instead of a tf.data.Dataset. If batch_size is -1, will return feature dictionaries containing Tf.data.Dataset will have a dictionary with all the features. seed, num parallel reads.).īool, if True, the returned tf.data.Dataset will haveĪ 2-tuple structure (input, label) according toī_keys. Tfds.ReadConfig, Additional options to configure the input Of the whole dataset with tf.Tensors instead of a tf.data.Dataset.īool, whether to shuffle the input files. If batch_size = -1, will return feature dictionaries Should use batch_size=None and use the tf.data API to construct aĬustom pipeline. Note that variable-length features will beĠ-padded if batch_size is set. # The dataset consists of tuples (text, label)

Tfs photosounder plus#

# Same as above plus requesting a particular splitĭs_test_supervised = builder.as_dataset(as_supervised=True, split='test')Īssert isinstance(ds_test_supervised, tf.data.Dataset) # Each dataset (test, train, unsup.) consists of tuples (text, label)

Print(ds_all_supervised.keys()) # => Īssert isinstance(ds_all_supervised, tf.data.Dataset) # With as_supervised: tf.data.Dataset only contains (feature, label) tuplesĭs_all_supervised = builder.as_dataset(as_supervised=True)Īssert isinstance(ds_all_supervised, dict) # Each dataset (test, train, unsup.) consists of dictionaries Print(ds_all_dict.keys()) # => Īssert isinstance(ds_all_dict, tf.data.Dataset) # Default parameters: Returns the dict of tf.data.Dataset Examples: builder = tfds.builder('imdb_reviews') The output types vary depending on the parameters. Read_config: Optional = None,Ĭallers must pass arguments as keyword arguments. NotImplementedError if the data was not generated using ArrayRecords.ĭecoders: Optional] = None, 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. Split: Optional] = None,ĭecoders: Optional] = None Versions (canonical + availables), in preference order. Path to the directory containing the images. are automatically calculatedĭs = builder.as_dataset(split='train', shuffle_files=True) To use it: builder = tfds.ImageFolder('path/to/image_dir/') The data directory should have the following structure: path/to/image_dir/ ImageFolder creates a tf.data.Dataset reading the original image files. Tfds.ImageFolder tfds.folder_dataset.ImageFolder( Inherits From: DatasetBuilder View aliases

Tfs photosounder manual#

Generic image classification dataset created from manual directory.