semvec/pypi-packages huggingface dataset:
nn-from-scratch 0.4.2
Neural Networks from Scratch
License: MIT

# 🔥torch-101
<img width="865" alt="image" src="https://github.com/user-attachments/assets/ad90c7ea-8d41-47fa-b9f6-1126cfedc30f">

![nn_from_scratch](./images/catvdog_train.gif)

## 💻 Try locally

```bash
pip install nn-from-scratch==0.3
``` 

## References

- [Pytorch Documentation](https://pytorch.org/resources/)
- [mrdbourke/pytorch-deep-learning](https://github.com/mrdbourke/pytorch-deep-learning)
vlm-utils 0.1.0
python call for C/C++
License: MIT
Requires Python: >=3.6.0


python call for C/C++

vlm_utils is a library for tracing function calls with depth control and filtering.

# Installation
    pip install vlm_utils
    
# Usage 1

    from vlm_utils.module import tracer

    def add_one(n):
        return n+1
    
    def add_two(n):
        n = add_one(n)
        n = add_one(n)
        return n

    @tracer
    def main():
        n = add_two(4)

    main()
    
# Usage 2
    
    to use c++ lib function
        
    from vlm_utils import lib_tools

# Install Requires

    python>=3.6.0
extra-keras-metrics 2.0.14
Additional metrics integrated with the TensorFlow and Keras NN library.
License: MIT

# Extra Keras Metrics

![PyPI version](https://badge.fury.io/py/extra-keras-metrics.svg)
[![python](https://img.shields.io/pypi/pyversions/extra-keras-metrics)](https://pypi.org/project/extra-keras-metrics/)
[![license](https://img.shields.io/pypi/l/extra-keras-metrics)](https://pypi.org/project/extra-keras-metrics/)
![Downloads](https://pepy.tech/badge/extra-keras-metrics)
[![mypy](https://github.com/LucaCappelletti94/extra_keras_metrics/actions/workflows/mypy.yml/badge.svg)](https://github.com/LucaCappelletti94/extra_keras_metrics/actions/)
[![Github Actions](https://github.com/LucaCappelletti94/extra_keras_metrics/actions/workflows/python.yml/badge.svg)](https://github.com/LucaCappelletti94/extra_keras_metrics/actions/)

Additional metrics integrated with the TensorFlow and Keras Neural Network libraries.

## How do I install this package?

As usual, just download it using pip:

```shell
pip install extra_keras_metrics
```

## How do I use this package?

In addition to importing individual metrics, sets of metrics are also available.

### Multi-class metrics

To retrieve an instance of the set of multi-class metrics, use:

```python
from extra_keras_metrics import get_minimal_multiclass_metrics

model = my_keras_model()
model.compile(
    optimizer="nadam",
    loss="categorical_crossentropy",
    metrics=get_minimal_multiclass_metrics()
)
```

### Sparse multi-class metrics

To retrieve an instance of the set of sparse multi-class metrics, use:

```python
from extra_keras_metrics import get_sparse_multiclass_metrics

model = my_keras_model()
model.compile(
    optimizer="nadam",
    loss="sparse_categorical_crossentropy",
    metrics=get_sparse_multiclass_metrics()
)
```

Note that currently, this only includes categorical accuracy, as it is the only one provided out-of-the-box by TensorFlow. More metrics are planned.

### Binary metrics

To retrieve an instance of the set of binary metrics, use:

```python
from extra_keras_metrics import get_standard_binary_metrics

model = my_keras_model()
model.compile(
    optimizer="nadam",
    loss="binary_crossentropy",
    metrics=get_standard_binary_metrics()
)
```

### All the binary metrics

We have implemented a wide range of binary metrics, including some lesser-known ones. To include **all** available binary metrics, use:

```python
from extra_keras_metrics import get_complete_binary_metrics

model = my_keras_model()
model.compile(
    optimizer="nadam",
    loss="binary_crossentropy",
    metrics=get_complete_binary_metrics()
)
```

## Extras

You might also enjoy these related packages:

- [extra_keras_utils](https://github.com/LucaCappelletti94/extra_keras_utils) - contains commonly used code for Keras projects.
- [plot_keras_history](https://github.com/LucaCappelletti94/plot_keras_history) - automatically plots Keras training history.