https://github.com/openml/openml-tensorflow
Tensorflow extension for openml-python
https://github.com/openml/openml-tensorflow
hacktoberfest openml python tensorflow
Last synced: about 1 year ago
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Tensorflow extension for openml-python
- Host: GitHub
- URL: https://github.com/openml/openml-tensorflow
- Owner: openml
- License: bsd-3-clause
- Created: 2020-09-02T13:26:26.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-12-03T16:55:27.000Z (over 1 year ago)
- Last Synced: 2025-04-09T13:42:05.498Z (about 1 year ago)
- Topics: hacktoberfest, openml, python, tensorflow
- Language: Python
- Homepage: https://openml.github.io/openml-tensorflow/
- Size: 5.15 MB
- Stars: 2
- Watchers: 7
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tensorflow extension for OpenML python
Tensorflow extension for [openml-python API](https://github.com/openml/openml-python). 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
```python
import openml
import openml_tensorflow
from tensorflow.keras import layers, models
```
Create and compile a tensorflow model
```python
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.
```python
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](/docs/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()```.
```python
run = openml_tensorflow.add_onnx_to_run(run)
```
#### Using docker image
The docker container has the latest version of [OpenML-Tensorflow](https://github.com/openml/openml-tensorflow) downloaded and pre-installed. It can be used to run TensorFlow Deep Learning analysis on OpenML datasets.
See [docker](docs/Docker%20reference/Docker.md).
This library is currently under development, please report any bugs or feature reuest in issues section.