Skip to content

Latest commit

 

History

History
65 lines (48 loc) · 2.28 KB

README.md

File metadata and controls

65 lines (48 loc) · 2.28 KB
Raw

Tensorflow extension for OpenML python

Tensorflow extension for openml-python API. This library provides a simple way to run your Tensorflow models on OpenML tasks.

Installation Instructions:

pip install openml-tensorflow

PyPi link https://pypi.org/project/openml-tensorflow/

Usage

Import openML libraries

import openml
import openml_tensorflow
from tensorflow.keras import layers, models

Create and compile a tensorflow model

model = models.Sequential()
model.add(layers.Conv2D(128, (3, 3), activation='relu', input_shape=IMG_SHAPE))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(84, activation='relu'))
model.add(layers.Dense(19, activation='softmax'))  
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['AUC'])

# We will compile using the Adam optimizer while targeting accuracy.
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['AUC'])

Download the task from openML and run the model on task.

task = openml.tasks.get_task(362071)
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.

Additionally, if you want to publish the run with onnx file, then you must call openml_tensorflow.add_onnx_to_run() immediately before run.publish().

run = openml_tensorflow.add_onnx_to_run(run)

Using docker image

The docker container has the latest version of OpenML-Tensorflow downloaded and pre-installed. It can be used to run TensorFlow Deep Learning analysis on OpenML datasets. See docker.

This library is currently under development, please report any bugs or feature reuest in issues section.