Metadata-Version: 2.1
Name: openml-keras
Version: 0.0.7
Summary: Keras extension for Openml python API
Home-page: http://openml.org/
Author: Adrian-Stefan Mares, Andrei Danila, Bogdan Enache, Gergana Goncheva, Loïc Alexander Hijl, Veselin Minev, Thanh-Dat Nguyen, Antoine Labasse, Lutou Nijhuis, Claudiu-Teodor Nohai, Dragos Mihai Serban, Tsvetan Zahariev, Sonya Zarkova
Author-email: 
Maintainer: Prabhant Singh
Maintainer-email: prabhantsingh@gmail.com
License: BSD 3-clause
Project-URL: Documentation, https://openml.github.io/openml-keras/
Project-URL: Source Code, https://github.com/openml/openml-keras
Description: # Keras extension for OpenML python
        
        Keras extension for [openml-python API](https://github.com/openml/openml-python).
        
        #### Installation Instructions:
        
        `pip install openml-keras`
        
        PyPi link https://pypi.org/project/openml-keras/
        
        #### Usage
        Import openML libraries
        ```python
        import openml
        import openml_keras
        ```
        Create  and compile a keras model
        ```python
        model = keras.models.Sequential([
            keras.layers.BatchNormalization(),
            keras.layers.Dense(units=1024, activation=keras.activations.relu),
            keras.layers.Dropout(rate=0.4),
            keras.layers.Dense(units=2, activation=keras.activations.softmax),
        ])
        
        # We will compile using the Adam optimizer while targeting accuracy.
        model.compile(optimizer='adam',
                      loss='sparse_categorical_crossentropy',
                      metrics=['accuracy'])
        ```
        Download the task from openML and run the model on task.
        ```python
        task = openml.tasks.get_task(3573)
        run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)
        run.publish()
        print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))
        ```
        Note: The input layer of the network should be compatible with OpenML data output shape. Please check examples for more information.
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Description-Content-Type: text/markdown
