Source code for gluonts.nursery.spliced_binned_pareto.distr_tcn

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# Implementation taken and modified from
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# Implementation of causal CNNs partly taken and modified from
# https://github.com/locuslab/TCN/blob/master/TCN/tcn.py, originally created
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import torch
import torch.nn

from .tcn import TCNBlock

from torch.distributions.normal import Normal


[docs]class DistributionalTCN(torch.nn.Module): """ Distributional Temporal Convolutional Network: a TCN to learn a time- varying distribution. Composed of a sequence of causal convolution blocks. Takes as input a three-dimensional tensor (`B`, `C`, `L`) where `B` is the batch size, `C` is the number of input channels, and `L` is the length of the input. Outputs a three-dimensional tensor (`B`, `C_out`, `L`). Args: in_channels : Number of input channels, typically the dimensionality of the time series out_channels : Number of output channels, typically the number of parameters in the time series distribution kernel_size : Kernel size of the applied non-residual convolutions. channels : Number of channels processed in the network and of output channels, typically equal to out_channels for simplicity, expand for better performance. layers : Depth of the network. bias : If True, adds a learnable bias to the convolutions. fwd_time : If True the network is the relation relation if from past to future (forward), if False, the relation from future to past (backward). output_distr: Distribution whose parameters will be specified by the network output """ def __init__( self, in_channels: int, out_channels: int, kernel_size: int, channels: int, layers: int, bias: bool = True, fwd_time: bool = True, output_distr=Normal(torch.tensor([0.0]), torch.tensor([1.0])), ): super().__init__() self.out_channels = out_channels # Temporal Convolution Network layers = int(layers) net_layers = [] # List of sequential TCN blocks dilation_size = 1 # Initial dilation size for i in range(layers): in_channels_block = in_channels if i == 0 else channels net_layers.append( TCNBlock( in_channels=in_channels_block, out_channels=channels, kernel_size=kernel_size, dilation=dilation_size, bias=bias, fwd_time=fwd_time, final=False, ) ) dilation_size *= 2 # Doubles the dilation size at each step # Last layer net_layers.append( TCNBlock( in_channels=channels, out_channels=self.out_channels, kernel_size=kernel_size, dilation=dilation_size, bias=bias, fwd_time=fwd_time, final=True, ) ) self.network = torch.nn.Sequential(*net_layers) self.output_distr = output_distr
[docs] def forward(self, x): net_out = self.network(x) net_out_final = net_out[..., -1].squeeze() self.output_distr(net_out_final) return self.output_distr