AEFCNNetwork

class AEFCNNetwork(latent_space_dim=128, temporal_latent_space=False, n_layers=3, n_filters=None, kernel_size=None, dilation_rate=1, strides=1, padding='same', activation='relu', use_bias=True)[source]

Establish the network structure for a AE-FCN.

Auto-Encoder based Fully Convolutional Netwwork (AE-FCN), adapted from the implementation used in [1].

Parameters:
latent_space_dimint, default = 128

Dimension of the auto-encoder’s latent space.

temporal_latent_spacebool, default = False

Flag to choose whether the latent space is an MTS or Euclidean space.

n_layersint, default = 3

Number of convolution layers.

n_filtersint or list of int, default = [128,256,128]

Number of filters used in convolution layers.

kernel_sizeint or list of int, default = [8,5,3]

Size of convolution kernel.

dilation_rateint or list of int, default = 1

The dilation rate for convolution.

stridesint or list of int, default = 1

The strides of the convolution filter.

paddingstr or list of str, default = “same”

The type of padding used for convolution.

activationstr or list of str, default = “relu”

Activation used after the convolution.

use_biasbool or list of bool, default = True

Whether or not ot use bias in convolution.

Notes

Adapted from the implementation from Fawaz et. al https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/fcn.py

References

[1]

Network originally defined in:

@inproceedings{wang2017time,
title={Time series classification from scratch with deep neural networks:

A strong baseline},

author={Wang, Zhiguang and Yan, Weizhong and Oates, Tim}, booktitle={2017 International joint conference on neural networks (IJCNN)}, pages={1578–1585}, year={2017}, organization={IEEE}

}

Methods

build_network(input_shape, **kwargs)

Construct a network and return its input and output layers.

build_network(input_shape, **kwargs)[source]

Construct a network and return its input and output layers.

Returns:
encodera keras Model.
decodera keras Model.