torchcnnbuilder 0.0.1

Framework for the automatic creation of CNN architectures

TorchCNNBuilder

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TorchCNNBuilder is an open-source framework for the automatic creation of CNN architectures. This framework should first of all help researchers in the applicability of CNN models for a huge range of tasks, taking over most of the writing of the architecture code. This framework is distributed under the 3-Clause BSD license. All the functionality is written only using pytorch (no third-party dependencies)

Installation

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The simplest way to install framework is using pip:

pip install torchcnnbuilder

Usage examples

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The basic structure of the framework is presented below. Each subdirectory has its own example of using the appropriate available functionality. You can check <directory>_examples.ipynb files in order to see the ways to use the proposed toolkit. In short, there is the following functionality:

• the ability to calculate the size of tensors after (transposed) convolutional layers
• preprocessing an n-dimensional time series in TensorDataset
• automatic creation of (transposed) convolutional sequences
• automatic creation of (transposed) convolutional layers and (transposed) blocks from convolutional layers

The structure of the main part of the package:

├── examples
│ ├── builder_examples.ipynb
│ ├── preprocess_examples.ipynb
│ ├── models_examples.ipynb
│ └── tools                     # additional functions for the examples
└── torchcnnbuilder
    ├── preprocess
    │ └── time_series.py
    ├── builder.py
    └── models.py

Initially, the library was created to help predict n-dimensional time series (geodata), so there is a corresponding functionality and templates of predictive models (like ForecasterBase)

To Do

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• add support for pulling and sampling layers
• add more templates for different CNN-models
• add more usage examples
• add auto tests

Sources

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• Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble