FCNNetwork

class FCNNetwork(n_layers=3, n_filters=None, kernel_size=None, dilation_rate=1, strides=1, padding='same', activation='relu', use_bias=True)

Establish the network structure for a FCN.

Adapted from the implementation used in [1]

Parameters:

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]

Wang et al. Time series classification from scratch with deep neural

networks: a strong baseline, IJCNN, 2017

Methods

build_network(input_shape, **kwargs)

Construct a network and return its input and output layers.

build_network(input_shape, **kwargs)

Construct a network and return its input and output layers.

Parameters:

input_shapetuple

shape = (n_timepoints (m), n_channels (d)), the shape of the data fed into the input layer.

Returns:

input_layera keras layer

output_layera keras layer