Deep learning networksΒΆ

aeon networks are the models behind the deep learning estimators, and can be used in

their own right to construct bespoke solutions.

BaseDeepLearningNetwork([soft_dependencies, ...])

Abstract base class for deep learning networks.

CNNNetwork(*args, **kwargs)

Establish the network structure for a CNN.

TimeCNNNetwork([n_layers, kernel_size, ...])

Establish the network structure for a CNN.

EncoderNetwork([kernel_size, n_filters, ...])

Establish the network structure for an Encoder.

FCNNetwork([n_layers, n_filters, ...])

Establish the network structure for a FCN.

InceptionNetwork([n_filters, ...])

Inception Network.

MLPNetwork()

Establish the network structure for a MLP.

ResNetNetwork([n_residual_blocks, ...])

Establish the network structure for a ResNet.

TapNetNetwork([dropout, filter_sizes, ...])

Establish Network structure for TapNet.

AEFCNNetwork([latent_space_dim, ...])

Establish the network structure for a AE-FCN.

AEResNetNetwork([latent_space_dim, ...])

Establish the network structure for a AE-ResNet.

LITENetwork([n_filters, kernel_size, ...])

LITE Network.

AEBiGRUNetwork([latent_space_dim, n_layers, ...])

A class to implement an Auto-Encoder based on Bidirectional GRUs.