def __init__(self,
in_channels,
hidden_channels1,
hidden_channels2,
kernel_size,
include_original,
include_time,
sig_depth,
out_channels,
batch_norm=False):
"""
Inputs:
in_channels: As SignatureModel.
hidden_channels1: How large to make certain hidden channels within the model.
hidden_channels2: How large to make certain hidden channels within the model.
kernel_size: How far to look back in time.
include_original: As SignatureModel.
include_time: As SignatureModel.
sig_depth: As SignatureModel.
out_channels: As SignatureModel.
"""
super().__init__()
self.kernel_size = kernel_size
self.batch_norm = batch_norm
self.padding1 = torch.nn.ConstantPad1d((kernel_size - 1, 0), 0)
self.augment1 = signatory.Augment(in_channels=in_channels,
layer_sizes=(hidden_channels1,
hidden_channels1,
hidden_channels2),
kernel_size=kernel_size,
include_original=include_original,
include_time=include_time)
self.signature1 = signatory.Signature(depth=sig_depth, stream=True)
sig_hidden_channels = hidden_channels2
if include_original:
sig_hidden_channels += in_channels
if include_time:
sig_hidden_channels += 1
sig_channels1 = signatory.signature_channels(
channels=sig_hidden_channels, depth=sig_depth)
self.padding2 = torch.nn.ConstantPad1d((kernel_size - 1, 0), 0)
self.augment2 = signatory.Augment(in_channels=sig_channels1,
layer_sizes=(hidden_channels1,
hidden_channels1,
hidden_channels2),
kernel_size=kernel_size,
include_original=False,
include_time=False)
self.signature2 = signatory.Signature(depth=sig_depth, stream=False)
sig_channels2 = signatory.signature_channels(channels=hidden_channels2,
depth=sig_depth)
self.linear = torch.nn.Linear(sig_channels2, out_channels)
if self.batch_norm:
self.bn1 = nn.BatchNorm1d(num_features=sig_channels1)
self.bn2 = nn.BatchNorm1d(num_features=sig_channels2)
def __init__(self, in_channels, out_dimension, sig_depth):
super(SigNet, self).__init__()
self.augment = signatory.Augment(in_channels=in_channels,
layer_sizes=(),
kernel_size=1,
include_original=True,
include_time=True)
self.signature = signatory.Signature(depth=sig_depth)
# +1 because signatory.Augment is used to add time as well
sig_channels = signatory.signature_channels(channels=in_channels + 1,
depth=sig_depth)
self.linear = torch.nn.Linear(sig_channels, out_dimension)
def __init__(self, in_channels, out_dimension, sig_depth):
super(SigNet3, self).__init__()
self.augment1 = signatory.Augment(in_channels=in_channels,
layer_sizes=(8, 8, 4),
kernel_size=4,
include_original=True,
include_time=True)
self.signature1 = signatory.Signature(depth=sig_depth, stream=True)
# +5 because self.augment1 is used to add time, and 4 other
# channels, as well
sig_channels1 = signatory.signature_channels(channels=in_channels + 5,
depth=sig_depth)
self.augment2 = signatory.Augment(in_channels=sig_channels1,
layer_sizes=(8, 8, 4),
kernel_size=4,
include_original=False,
include_time=False)
self.signature2 = signatory.Signature(depth=sig_depth, stream=False)
# 4 because that's the final layer size in self.augment2
sig_channels2 = signatory.signature_channels(channels=4,
depth=sig_depth)
self.linear = torch.nn.Linear(sig_channels2, out_dimension)
def __init__(self, input_dim, latent_dim, gru_n_layers = 2, augment_chs = 8):
super().__init__()
self.latent_dim = latent_dim
self.augment1 = signatory.Augment(in_channels=input_dim,
layer_sizes=(32, augment_chs),
kernel_size=1,
include_original=True,
include_time=True)
self.signature1 = signatory.Signature(depth=2,
stream=True)
sig_channels1 = signatory.signature_channels(channels=input_dim + augment_chs + 1,
depth=2)
self.gru = nn.GRU(input_size = sig_channels1, hidden_size = latent_dim, num_layers = gru_n_layers, batch_first = True)
def __init__(self, latent_dim, output_dim, decoder_hidden_units = 64):
super().__init__()
self.latent_dim = latent_dim
self.decoder_hidden_units = decoder_hidden_units
self.augment2 = signatory.Augment(in_channels=latent_dim,
layer_sizes=(64, 32),
kernel_size=1,
include_original=False,
include_time=False)
self.signature2 = signatory.Signature(depth=2,
stream=True)
sig_channels2 = signatory.signature_channels(channels= 32,
depth=2)
self.linear1 = nn.Linear(in_features = sig_channels2, out_features = decoder_hidden_units)
self.linear2 = nn.Linear(in_features = decoder_hidden_units, out_features = output_dim)