semvec/pypi-packages huggingface dataset:
Telezhka 0.0.11
Telegram bot api library in python
License: MIT
Requires Python: >=3
Keywords: Telegram

<div align="center">
	<h1>Telezhka</h1>

Telegram api library in python

![pypi](https://badge.fury.io/py/Telezhka.svg)

</div>

## Install
`pip install --upgrade Telezhka`

## Simple usage
```python
from Telezhka import Telegram
api = Telegram("883304628:ONTB4H2SAMPLEtext9m0g")

for event in api.listen():
    if "message" in event and "text" in event.message:
        if event.message.text == "/hello":
            api.sendMessage(text="Hello :)", chat_id=event.message.chat.id)
```

## Keyboard usage
```python
from Telezhka import Keyboard

kb = Keyboard(resize_keyboard=True)
kb.add_button(text="hello")
kb.add_line({"text": "hello2"})
# Send message with keyboard
api.sendMessage(reply_markup=kb.compile(), text="Hello", chat_id=event.message.chat.id)
```
keras-adabound 0.6.0
AdaBound optimizer in Keras
License: MIT

# Keras AdaBound

[![Travis](https://travis-ci.org/CyberZHG/keras-adabound.svg)](https://travis-ci.org/CyberZHG/keras-adabound)
[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-adabound/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-adabound)

[AdaBound](https://github.com/Luolc/AdaBound) optimizer in Keras.

## Install

```bash
pip install keras-adabound
```

## Usage

### Use the optimizer

```python
from keras_adabound import AdaBound

model.compile(optimizer=AdaBound(lr=1e-3, final_lr=0.1), loss=model_loss)
```

### Load with custom objects

```python
from keras_adabound import AdaBound

model = keras.models.load_model(model_path, custom_objects={'AdaBound': AdaBound})
```

### About weight decay

The optimizer does not have an argument named `weight_decay` (as in the official repo) since it can be done by adding L2 regularizers to weights:

```python
import keras

regularizer = keras.regularizers.l2(WEIGHT_DECAY / 2)
for layer in model.layers:
    for attr in ['kernel_regularizer', 'bias_regularizer']:
        if hasattr(layer, attr) and layer.trainable:
            setattr(layer, attr, regularizer)
```