semvec/pypi-packages huggingface dataset:
diagtomodel 0.3.3
Implementations of Artificial Neural Networks Based on their Diagrams
Requires Python: >=3.9
Keywords: Python,Machine Learning,Computer Vision,Deep Learning,Neural Networks

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### Implementations of Artificial Neural Networks Based on their Diagrams

## Authors
* Milad Sadeghi DM - [EverlookNeverSee@GitHub](https://github.com/EverLookNeverSee)
* List of [all contributors](https://github.com/EverLookNeverSee/diagtomodel/graphs/contributors) to this repository

## Implemented Models
* [PLNet](diagtomodel/pl_net/pl_net.py) - Convolutional Neural Network with Parallel Layers
* [MLANet](diagtomodel/mla_net/mla_net.py) - Convolutional Neural Network with Multiple Layer Additions
* [LeNet-5](diagtomodel/lenet_5/lenet-5.py) - Gradient-Based Learning Applied to Document Recognition
* [AlexNet](diagtomodel/alexnet/alexnet.py) - ImageNet Classification with Deep Convolutional
Neural Networks
* [VGG-16](diagtomodel/vgg_16/vgg-16.py) - Very Deep Convolutional Networks For Large Scale Image Recognition
* [Inception-v1](diagtomodel/inception_v1/inception-v1.py) - Going Deeper With Convolutions
* [Xception](diagtomodel/xception/xception.py) - Deep Learning with Depthwise Separable Convolutions


## License
This project licensed under the MIT License - see the [LICENSE](LICENSE) file for more details.
npend 1.0.1
Provides a convenient way to append numpy arrays to a file.

<a>Provides a convenient way to append numpy arrays to a file.</a><br>

<a>The NpendWriter and NpendReader classes are used to write and read numpy arrays respectively.</a>
<h3>Requirements</h3>
<ul>
	<li>numpy</li>
	<li>sip</li>
</ul>

<h3>Usage</h3>
<div style="background-color:#f9f9f9;border:2px solid #d3d3d3;">
	<pre>
    #! -*- coding:utf-8 -*-
    from npend import NpendWriter, NpendReader
    import numpy as np
    if __name__=="__main__":
        filePath="test.npd"
        nw=NpendWriter(filePath)
        arr=np.random.random((34,5,6))# Write an array that has three dimensions.
        nw.append(arr)# Keep the size of the last two dimensions the same as the data written before.
        arr=np.random.random((10,5,6))
        nw.append(arr)
        arr=np.random.random((5,6))# Write an array with two dimensions.
        nw.append(arr)
        nw.close()
        nr=NpendReader(filePath)
        arr=nr.read()# Load numpy array from file.
        print(arr.shape,arr.dtype)
	</pre>
</div>
asyncio-cache 0.0.1
A python library for asyncio caches (like functools cache and lru_cache)

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# asyncio-cache

A python library for asyncio caches (like functools cache and lru_cache)

# Installation

Install the last released version using `pip`:

```shell
python3 -m pip install --user -U asyncio-cache
```

Or install the latest version from sources:

```shell
git clone git@github.com:matan1008/asyncio-cache.git
cd asyncio-cache
python3 -m pip install --user -U -e .
```

# Usage

The usage is similar to `functools.cache` and `functools.lru_cache` usage:

```python
from asyncio_cache import cache


@cache
async def cached_read(reader):
    return await reader.read(100)
```