tff.simulation.baselines.emnist.create_character_recognition_task

Creates a baseline task for character recognition on EMNIST.

tff.simulation.baselines.emnist.create_character_recognition_task(
    train_client_spec: tff.simulation.baselines.ClientSpec,
    eval_client_spec: Optional[tff.simulation.baselines.ClientSpec] = None,
    model_id: Union[str, tff.simulation.baselines.emnist.CharacterRecognitionModel] = &#x27;cnn_dropout',
    only_digits: bool = False,
    cache_dir: Optional[str] = None,
    use_synthetic_data: bool = False,
    debug_seed: Optional[int] = None
) -> tff.simulation.baselines.BaselineTask

The goal of the task is to minimize the sparse categorical crossentropy between the output labels of the model and the true label of the image. When only_digits = True, there are 10 possible labels (the digits 0-9), while when only_digits = False, there are 62 possible labels (both numbers and letters).

This classification can be done using a number of different models, specified using the model_id argument. Below we give a list of the different models that can be used:

model_id = cnn_dropout: A moderately sized convolutional network. Uses two convolutional layers, a max pooling layer, and dropout, followed by two dense layers.
model_id = cnn: A moderately sized convolutional network, without any dropout layers. Matches the architecture of the convolutional network used by (McMahan et al., 2017) for the purposes of testing the FedAvg algorithm.
model_id = 2nn: A densely connected network with 2 hidden layers, each with 200 hidden units and ReLU activations.

Args
`train_client_spec`	A `tff.simulation.baselines.ClientSpec` specifying how to preprocess train client data.
`eval_client_spec`	An optional `tff.simulation.baselines.ClientSpec` specifying how to preprocess evaluation client data. If set to `None`, the evaluation datasets will use a batch size of 64 with no extra preprocessing.
`model_id`	A string identifier for a character recognition model. Must be one of 'cnn_dropout', 'cnn', or '2nn'. These correspond respectively to a CNN model with dropout, a CNN model with no dropout, and a densely connected network with two hidden layers of width 200.
`only_digits`	A boolean indicating whether to use the full EMNIST-62 dataset containing 62 alphanumeric classes (`True`) or the smaller EMNIST-10 dataset with only 10 numeric classes (`False`).
`cache_dir`	An optional directory to cache the downloadeded datasets. If `None`, they will be cached to `~/.tff/`.
`use_synthetic_data`	A boolean indicating whether to use synthetic EMNIST data. This option should only be used for testing purposes, in order to avoid downloading the entire EMNIST dataset.
`debug_seed`	An optional integer seed to force deterministic model initialization. This is intended for unittesting.

Returns
A `tff.simulation.baselines.BaselineTask`.

tff.simulation.baselines.emnist.create_character_recognition_task

Args

Returns