autoencoders¶

Collection of Fully Convolutional Autoencoders.

Classes

`Autoencoder`	Primitive Model for all convolutional autoencoders.
`FCNNAutoencoder`	Primitive Model for all fully convolutional autoencoders.

class ashpy.models.convolutional.autoencoders.Autoencoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, channels)[source]¶

Bases: tensorflow.python.keras.engine.training.Model

Primitive Model for all convolutional autoencoders.

Examples

Direct Usage:

autoencoder = Autoencoder(
    layer_spec_input_res=(64, 64),
    layer_spec_target_res=(8, 8),
    kernel_size=5,
    initial_filters=32,
    filters_cap=128,
    encoding_dimension=100,
    channels=3,
)

encoding, reconstruction = autoencoder(tf.zeros((1, 64, 64, 3)))
print(encoding.shape)
print(reconstruction.shape)

__init__(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, channels)[source]¶

Instantiate the BaseAutoEncoder.

Parameters:

layer_spec_input_res (tuple of (int, int)) – Shape of the input tensors.
layer_spec_target_res – (tuple of (int, int)): Shape of tensor desired as output of _get_layer_spec().
kernel_size (int) – Kernel used by the convolution layers.
initial_filters (int) – Numbers of filters to used as a base value.
filters_cap (int) – Cap filters to a set amount, in the case of an Encoder is a ceil value AKA the max amount of filters.
encoding_dimension (int) – encoding dimension.
channels (int) – Number of channels for the reconstructed image.

Returns:

None

call(inputs, training=True)[source]¶

Execute the model on input data.

Parameters:	inputs (`tf.Tensor`) – Input tensors. training (`bool`) – Training flag.
Returns:	(encoding, reconstruction) – Pair of tensors.

class ashpy.models.convolutional.autoencoders.FCNNAutoencoder(layer_spec_input_res, layer_spec_target_res, kernel_size, initial_filters, filters_cap, encoding_dimension, channels)[source]¶