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ChrisLisbon merged 2 commits into from
Aug 7, 2025
Merged

examples fix #25

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da97ad7
examples fix
ChrisLisbon Aug 7, 2025
71a234f
repo and docstrings
ChrisLisbon Aug 7, 2025
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23 changes: 23 additions & 0 deletions examples/README.md
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[[Click For Russian version]](README_RU.md)

## Quick Start

1. Clone the repository:
```bash
git clone https://github.com/yourusername/TorchCNNBuilder.git
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@nicl-nno nicl-nno Aug 7, 2025

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yourusername?

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@ChrisLisbon ChrisLisbon Aug 7, 2025

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Поправила

cd TorchCNNBuilder

2. Install in development mode (recommended):
```bash
pip install -e .

**This will**: install all required dependencies, make the package available system-wide, allow
you to modify code and see changes immediately.

3. For just running examples (without development):
```bash
pip install numpy torch matplotlib jupyter

4. ⚠️ Note: The examples use relative paths for data loading.
To avoid file not found errors, always launch Jupyter from the repository root directory:
```bash
jupyter notebook examples/example_name.ipynb

# Usage examples

Relevant usage examples can be found in this directory. The API examples of each submodule are located in the corresponding `ipynb` file:
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24 changes: 24 additions & 0 deletions examples/README_RU.md
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# Примеры использования

## Быстрый старт

1. Клонируйте репозиторий:
```bash
git clone https://github.com/yourusername/TorchCNNBuilder.git
cd TorchCNNBuilder

2. Установка в режиме разработки (рекомендуется):
```bash
pip install -e .

**Это позволит**: установить все необходимые зависимости, сделать пакет доступным системе,
видеть изменения в коде без переустановки.

3. Только для запуска примеров (без разработки):

```bash
pip install numpy torch matplotlib jupyter

4. ⚠️ Важно! Примеры используют относительные пути к данным.
Всегда запускайте ноутбуки из корня репозитория:
```bash
jupyter notebook examples/имя_примера.ipynb

## Примеры работы с компонентами библиотеки
Примеры обращения в API для каждого подмодуля расположены в соответствующих файлах `ipynb`:
- [`torchcnnbuilder`](usage_examples/main_examples_ru.ipynb) - основные переменные и мат. аппартат сверток
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4 changes: 2 additions & 2 deletions examples/anime_example.ipynb
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Expand Up @@ -9,15 +9,15 @@
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"!pip install numpy\n",
"!pip install tqdm\n",
"!pip install matplotlib \n",
"!pip install pillow"
],
"id": "9a0fa490fe399727"
"id": "9a0fa490fe399727",
"outputs": []
},
{
"metadata": {},
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54 changes: 38 additions & 16 deletions examples/anime_example_ru.ipynb
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Expand Up @@ -47,8 +47,8 @@
"id": "e6a3d5d05db5d1ab",
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-01T13:28:21.510394Z",
"start_time": "2025-07-01T13:28:21.372396Z"
"end_time": "2025-08-07T15:26:29.602405Z",
"start_time": "2025-08-07T15:26:29.491405Z"
}
},
"source": [
Expand Down Expand Up @@ -94,8 +94,8 @@
"id": "61165453374fda73",
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-01T13:28:44.614031Z",
"start_time": "2025-07-01T13:28:44.113957Z"
"end_time": "2025-08-07T15:26:30.165543Z",
"start_time": "2025-08-07T15:26:29.743406Z"
}
},
"source": [
Expand Down Expand Up @@ -148,8 +148,8 @@
"id": "4fdf405ba3465604",
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-01T13:30:29.858767Z",
"start_time": "2025-07-01T13:30:28.296769Z"
"end_time": "2025-08-07T15:26:32.311631Z",
"start_time": "2025-08-07T15:26:31.071571Z"
}
},
"source": [
Expand All @@ -173,8 +173,8 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-07-01T13:31:15.123617Z",
"start_time": "2025-07-01T13:31:14.942092Z"
"end_time": "2025-08-07T15:26:32.483645Z",
"start_time": "2025-08-07T15:26:32.312634Z"
}
},
"cell_type": "code",
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{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-14T14:13:30.181383Z",
"start_time": "2024-10-14T14:13:29.281678Z"
"end_time": "2025-08-07T15:26:34.038192Z",
"start_time": "2025-08-07T15:26:33.359231Z"
}
},
"cell_type": "code",
Expand All @@ -222,8 +222,8 @@
"epochs_list = []"
],
"id": "5bb4b425f1a33233",
"execution_count": 5,
"outputs": []
"outputs": [],
"execution_count": 5
},
{
"metadata": {},
Expand All @@ -234,8 +234,8 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-14T14:25:59.822849Z",
"start_time": "2024-10-14T14:19:12.883444Z"
"end_time": "2025-08-07T15:33:42.113459Z",
"start_time": "2025-08-07T15:26:35.837073Z"
}
},
"cell_type": "code",
Expand Down Expand Up @@ -296,8 +296,30 @@
"print(f'time spent: {end-start}')"
],
"id": "1075054a695b6ee0",
"execution_count": 10,
"outputs": []
"outputs": [
{
"data": {
"text/plain": [
" 0%| | 0/100000 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9ff4f1a53bf04bb1bf3beec9393477de"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time spent: 426.2163863182068\n"
]
}
],
"execution_count": 6
},
{
"metadata": {},
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