HuggingMouse.pipelines.multitrial_regression

Classes

ZIGRegression	Base class for all neural network modules.
MultiTrialRegressionPipeline

Module Contents

class HuggingMouse.pipelines.multitrial_regression.ZIGRegression(ViTEmbDim, NNeurons, gen_nodes, factor)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them 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):
        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 have their parameters converted too when you call to(), etc.

Note

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

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

fc1

fc2

fc_theta

fc_p

logk

forward(X, Y=None)

class HuggingMouse.pipelines.multitrial_regression.MultiTrialRegressionPipeline(**kwargs)

model

model_name_str

model_prefix

regression_model

test_set_size

regr_analysis_results_cache

boc

eid_dict

stimulus_session_dict

processor