mediapipe_model_maker.text_classifier.HParams

Hyperparameters used for training models.

mediapipe_model_maker.text_classifier.HParams(
    learning_rate: float,
    batch_size: int,
    epochs: int,
    steps_per_epoch: Optional[int] = None,
    shuffle: bool = False,
    export_dir: str = tempfile.mkdtemp(),
    distribution_strategy: str = 'off',
    num_gpus: int = -1,
    tpu: str = ''
)

A common set of hyperparameters shared by the training jobs of all model maker tasks.

Attributes
learning_rate	The learning rate to use for gradient descent training.
batch_size	Batch size for training.
epochs	Number of training iterations over the dataset.
steps_per_epoch	An optional integer indicate the number of training steps per epoch. If not set, the training pipeline calculates the default steps per epoch as the training dataset size divided by batch size.
shuffle	True if the dataset is shuffled before training.
export_dir	The location of the model checkpoint files.
distribution_strategy	A string specifying which Distribution Strategy to use. Accepted values are 'off', 'one_device', 'mirrored', 'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to use TPUStrategy using tpu_address. See the tf.distribute.Strategy documentation for more details: https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy.
num_gpus	How many GPUs to use at each worker with the DistributionStrategies API. The default is -1, which means utilize all available GPUs.
tpu	The Cloud TPU to use for training. This should be either the name used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.

Methods

__eq__

__eq__(
    other
)

Class Variables
distribution_strategy	'off'
export_dir	'/tmpfs/tmp/tmpqngo3zix'
num_gpus	-1
shuffle	False
steps_per_epoch	None
tpu	''