Tokenizers

Imagine a word is an interval of time series data…

InceptionTokenizer


def InceptionTokenizer(
    c_in, # the number of input channels
    patch_size, # the length of the patches (either stft or interval length)
    d_model, # the dimension of the initial linear layers for inputting patches into transformer
    patch_stride:NoneType=None, # the stride of the patches
    shared_embedding:bool=True, tokenizer_kwargs:VAR_KEYWORD
):

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

source

TS_Tokenizer_Complex


def TS_Tokenizer_Complex(
    c_in, patch_size, d_model, constant_pad_value:float=0.0
):

Time series 2D convolutional Embedding

/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/fastcore/docscrape.py:259: UserWarning: potentially wrong underline length... 
Tokenizer class based on a Conv1D that handles both nested and regular tensors. 
--- in 
Tokenizer class based on a Conv1D that handles both nested and regular tensors.
---...
  else: warn(msg)

source

TS_Tokenizer


def TS_Tokenizer(
    c_in, patch_size, d_model, patch_stride:NoneType=None, shared_embedding:bool=True,
    variable_channel_callback:NoneType=None
):

Tokenizer class based on a Conv1D that handles both nested and regular tensors.

c_in (int): Number of input channels
patch_size (int): Size of each patch/kernel
d_model (int): Output embedding dimension

source

PatchEncoder


def PatchEncoder(
    c_in, # the number of input channels
    patch_len, # the length of the patches (either stft or interval length)
    d_model, # the dimension of the initial linear layers for inputting patches into transformer
    shared_embedding, # indicator of whether to project each channel individually or together
):

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool