"""
AE Template
============

This is a basic template for implementing an Autoencoder in PyTorch Lightning.

A default encoder and decoder have been provided but can easily be replaced by custom models.

This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the image
 width and image height are even values. For other types of data, such as sound, it will be necessary
 to change the Encoder and Decoder.

The default encoder is a resnet18 backbone followed by linear layers which map representations to latent space.
 The default decoder mirrors the encoder architecture and is similar to an inverted resnet18.

.. code-block:: python

    from pl_bolts.models.autoencoders import AE

    model = AE()
    trainer = pl.Trainer()
    trainer.fit(model)
"""

"""
AE Template
============

This is a basic template for implementing an Autoencoder in PyTorch Lightning.

A default encoder and decoder have been provided but can easily be replaced by custom models.

This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the image
 width and image height are even values. For other types of data, such as sound, it will be necessary
 to change the Encoder and Decoder.

The default encoder is a resnet18 backbone followed by linear layers which map representations to latent space.
 The default decoder mirrors the encoder architecture and is similar to an inverted resnet18.

.. code-block:: python

    from pl_bolts.models.autoencoders import AE

    model = AE()
    trainer = pl.Trainer()
    trainer.fit(model)
"""

AE Template ============  This is a basic template for implementing an Autoencoder in PyTorch Lightning.  A default encoder and decoder have been provided but can easily be replaced by custom models.  This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the image  width and image height are even values. For other types of data, such as sound, it will be necessary  to change the Encoder and Decoder.  The default encoder is a resnet18 backbone followed by linear layers which map representations to latent space.  The default decoder mirrors the encoder architecture and is similar to an inverted resnet18.  .. code-block:: python      from pl_bolts.models.autoencoders import AE      model = AE()     trainer = pl.Trainer()     trainer.fit(model)