https://github.com/robertanto/bob_telegram_tools

Bob Telegram Tools is a python library that allows you to monitor your machine learning methods just by using Telegram without any additional application.
https://github.com/robertanto/bob_telegram_tools

keras telegram-bot

Last synced: 25 days ago
JSON representation

Bob Telegram Tools is a python library that allows you to monitor your machine learning methods just by using Telegram without any additional application.

• Host: GitHub
• URL: https://github.com/robertanto/bob_telegram_tools
• Owner: robertanto
• License: gpl-3.0
• Created: 2020-03-22T19:20:03.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-07-10T08:02:18.000Z (over 4 years ago)
• Last Synced: 2024-09-30T10:02:57.745Z (about 1 month ago)
• Topics: keras, telegram-bot
• Language: Python
• Homepage: https://robertanto.github.io/bob_telegram_tools/
• Size: 1.25 MB
• Stars: 10
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        









Bob Telegram Tools is a python library which allows you to monitor your machine learning methods just by using Telegram without any additional application.

Documentation

=============

See https://robertanto.github.io/bob_telegram_tools/ for detailed instruction, manuals and tutorials.

Installation instructions

=========================

You can install the package with pip:

`pip install bob-telegram-tools` 

Getting started

=======







```python

import keras

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import RMSprop

import numpy as np

from bob_telegram_tools.keras import KerasTelegramCallback

from bob_telegram_tools.bot import TelegramBot

X = np.random.rand(1000, 100)

y = (np.random.rand(1000, 3) > 0.5).astype('float32')

model = Sequential()

model.add(Dense(512, activation='relu', input_shape=(100,)))

model.add(Dense(512, activation='relu'))

model.add(Dense(3, activation='softmax'))

model.compile(loss='categorical_crossentropy',

              optimizer=RMSprop(),

              metrics=['accuracy'])

n_epochs = 3

token = ''

user_id = int('')

bot = TelegramBot(token, user_id)

tl = KerasTelegramCallback(bot, epoch_bar=True, to_plot=[

    {

        'metrics': ['loss', 'val_loss']

    },

    {

        'metrics': ['acc', 'val_acc'],

        'title':'Accuracy plot',

        'ylabel':'acc',

        'ylim':(0, 1),

        'xlim':(1, n_epochs)

    }

])

history = model.fit(X, y,

                    batch_size=10,

                    epochs=n_epochs,

                    validation_split=0.15,

                    callbacks=[tl])

```

License

=======

Code released under the [GNU GENERAL PUBLIC LICENSE](https://github.com/robertanto/bob_telegram_tools/tree/master/LICENSE).