byzh-ai
更方便的深度学习。
pip install byzh-ai → 导入名 byzh_ai
模块一览
所有子包以 B 开头，类以 B_ 开头，函数以 b_ 开头。
| 模块 | 说明 |
|---|---|
| Bdata | 主流数据集一键下载/缓存/标准化（MNIST、CIFAR、FashionMNIST、Tiny ImageNet 等），支持 TensorDataset / DataLoader 构建与分层采样 |
| Bdemo | MNIST 完整训练演示、训练器功能测试（单卡/多卡）、分类评估测试 |
| Bearly_stop | 多种早停策略（精度停滞、精度增量不足、损失停滞、过拟合检测）及模型参数自动回滚重载 |
| Blr_schedulers | 自定义学习率调度器：Warmup 线性升温、阶梯衰减、Warmup + 衰减组合 |
| Bmodel | 即用型深度学习模型：SimpleCNN、LeNet-5、ResNet-18、RNN、LSTM、Transformer、脉冲神经网络（Spike RNN/LSTM/ResNet-18）等 |
| Btrainer | 通用训练器，封装训练/验证/测试循环，集成早停/重载、学习率调度、checkpoint、多 GPU（DataParallel / DDP）、SNN 支持、日志记录、损失/精度绘图 |
| Butils | 深度学习工具：自动设备选择（NPU/GPU/CPU）、GPU 监控与空闲检测、模型参数量/FLOPs 统计、随机种子统一设置、数据集均值/标准差计算、超参数网格搜索、递增运行目录管理 |
| Bvisual | 训练损失/精度曲线绘制、混淆矩阵热力图绘制 |
依赖
byzh-core>=0.0.10.0— 进度条、日志写入器、彩色终端输出、文件/目录操作等基础工具torch、torchvision— 深度学习框架numpy、pandas、matplotlib、seaborn— 数据处理与可视化scikit-learn— 数据划分、指标计算thop— FLOPs 计算（需自行安装）spikingjelly— 脉冲神经网络（可选）PyYAML— YAML 配置解析Pillow— 图像处理
License
1 maintainer
Metadata
1 maintainer
Release files for byzh-ai 0.0.10.18
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| File | Size | Uploaded | Provenance | |
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| byzh_ai-0.0.10.18.tar.gz | 134.4 kB | Without provenance | Details |
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| byzh_ai-0.0.10.18-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 368.1 kB
Release files / byzh_ai-0.0.10.18.tar.gz
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| Tags | Python 3 |
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