# 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
---
The simplest way to install framework is using `pip`:
```
pip install torchcnnbuilder
```
### Usage examples
---
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`](./examples) 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`)*
### Sources
---
- [Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks](https://www.sciencedirect.com/science/article/pii/S1877050923020094)
- [Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble](https://arxiv.org/abs/2312.04330)
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"description": "# TorchCNNBuilder\n\n<div id=\"badges\">\n <a href=\"https://pytorch.org/\">\n <img src=\"https://img.shields.io/badge/pytorch-CB2C31?style=flat&logo=pytorch&logoColor=white\" alt=\"pytorch badge\"/>\n </a>\n <img alt=\"Dynamic JSON Badge\" src=\"https://img.shields.io/pypi/pyversions/torch\">\n <a href=\"https://badge.fury.io/py/torchcnnbuilder\">\n <img src=\"https://badge.fury.io/py/torchcnnbuilder.svg\" alt=\"PyPI version\" height=\"18\">\n </a>\n</div>\n\n---\n**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)*\n\n### Installation\n\n---\nThe simplest way to install framework is using `pip`:\n```\npip install torchcnnbuilder\n```\n\n### Usage examples\n\n---\nThe 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`](./examples) files in order to see the ways to use the proposed toolkit. In short, there is the following functionality:\n\n- the ability to calculate the size of tensors after (transposed) convolutional layers\n- preprocessing an n-dimensional time series in `TensorDataset`\n- automatic creation of (transposed) convolutional sequences\n- automatic creation of (transposed) convolutional layers and (transposed) blocks from convolutional layers\n\nThe structure of the main part of the package:\n\n```\n\u251c\u2500\u2500 examples\n\u2502 \u251c\u2500\u2500 builder_examples.ipynb\n\u2502 \u251c\u2500\u2500 preprocess_examples.ipynb\n\u2502 \u251c\u2500\u2500 models_examples.ipynb\n\u2502 \u2514\u2500\u2500 tools # additional functions for the examples\n\u2514\u2500\u2500 torchcnnbuilder\n \u251c\u2500\u2500 preprocess\n \u2502 \u2514\u2500\u2500 time_series.py\n \u251c\u2500\u2500 builder.py\n \u2514\u2500\u2500 models.py\n```\nInitially, 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`)*\n\n### Sources\n\n---\n- [Forecasting of Sea Ice Concentration using CNN, PDE discovery and Bayesian Networks](https://www.sciencedirect.com/science/article/pii/S1877050923020094)\n- [Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble](https://arxiv.org/abs/2312.04330)\n\n",
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