DCNNNetwork¶
- class DCNNNetwork(latent_space_dim=128, n_layers=4, kernel_size=3, activation='relu', n_filters=None, dilation_rate=None, padding='causal')[source]¶
Establish the network structure for a DCNN-Model.
Dilated Convolutional Neural Network based Model for low-rank embeddings.
- Parameters:
- latent_space_dim: int, default=128
Dimension of the models’s latent space.
- n_layers: int, default=4
Number of convolution layers.
- kernel_size: Union[int, List[int]], default=3
Size of the 1D Convolutional Kernel. Defaults to a list of three’s for n_layers elements.
- activation: Union[str, List[str]], default=”relu”
The activation function used by convolution layers. Defaults to a list of “relu” for n_layers elements.
- n_filters: Union[int, List[int]], default=None
Number of filters used in convolution layers. Defaults to a list of multiple’s of 32 for n_layers elements.
- dilation_rate: Union[int, List[int]], default=None
The dilation rate for convolution. Defaults to a list of powers of 2 for n_layers elements.
- padding: Union[str, List[str]], default=”causal”
Padding to be used in each DCNN Layer. Defaults to a list of causal paddings for n_layers elements.
References
[1]Franceschi, J. Y., Dieuleveut, A., & Jaggi, M. (2019).
Unsupervised scalable representation learning for multivariate time series. Advances in neural information processing systems, 32.
Methods
build_network(input_shape)Construct a network and return its input and output layers.