Metadata-Version: 2.1
Name: bob-telegram-tools
Version: 1.1.0
Summary: A package to monitor your Machine Learning trainings every where without any additional app.
Home-page: https://github.com/robertanto/bob_telegram_tools
Author: Antonio Roberto
Author-email: roberto.antonio@outlook.it
License: UNKNOWN
Description: <p style="text-align:center;">
        <img style="" src="https://raw.githubusercontent.com/robertanto/bob_telegram_tools/master/docs_src/logo.png">
        </p>
        
        <br>
        
        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
        =======
        
        <p style="text-align:center;">
        <img style="" src="https://raw.githubusercontent.com/robertanto/bob_telegram_tools/master/docs_src/keras_ex/bot.jpg" width=300>
        </p>
        
        ```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 = '<your_token>'
        user_id = int('<your_chat_id>')
        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).
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
