# AutoKeras — AutoML for Deep Learning with Keras

> AutoKeras automatically searches for optimal neural network architectures and hyperparameters for image classification, text classification, regression, and structured data tasks using the Keras API.

## Install

Save in your project root:

# AutoKeras — AutoML for Deep Learning with Keras

## Quick Use
```bash
pip install autokeras
python -c "
import autokeras as ak
import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
clf = ak.ImageClassifier(max_trials=10)
clf.fit(x_train, y_train, epochs=3)
print(clf.evaluate(x_test, y_test))
"
```

## Introduction
AutoKeras is an AutoML library built on top of Keras that automates neural architecture search and hyperparameter tuning. It lets developers train high-quality deep learning models with minimal machine learning expertise by specifying only the task type and data.

## What AutoKeras Does
- Searches for optimal neural network architectures using efficient NAS algorithms
- Automatically selects preprocessing, data augmentation, and normalization strategies
- Supports image classification, text classification, structured data, and regression tasks
- Tunes hyperparameters like learning rate, layer sizes, and dropout rates
- Exports the best model as a standard Keras model for deployment

## Architecture Overview
AutoKeras uses Keras Tuner as its search backend. It defines a search space of possible architectures as a graph of interchangeable blocks (convolutional, dense, transformer). The tuner explores this space using Bayesian optimization or greedy search, training candidate models and evaluating them on a validation set. The best architecture is returned as a standard Keras Sequential or Functional model.

## Self-Hosting & Configuration
- Install via pip; requires TensorFlow 2.x as the backend
- Set max_trials to control how many architectures are evaluated
- Use overwrite=True to restart a search or False to resume a previous one
- Configure epochs and validation_split in the fit() call
- Export the final model with export_model() for serving with TensorFlow Serving or ONNX

## Key Features
- Task-oriented API covers image, text, structured data, and time-series problems
- Multi-modal input support combines images and tabular data in a single model
- Customizable search space lets advanced users constrain architecture blocks
- Integrated early stopping prevents wasted compute on poor candidates
- Exports standard Keras models compatible with the full TensorFlow ecosystem

## Comparison with Similar Tools
- **Auto-Sklearn** — AutoML for scikit-learn classical models; AutoKeras targets deep learning architectures
- **H2O AutoML** — enterprise platform covering many model types; AutoKeras focuses on neural networks with Keras
- **Ludwig** — declarative config-based training; AutoKeras automates architecture search
- **FLAML** — fast lightweight AutoML; AutoKeras specializes in neural architecture search
- **Google AutoML** — managed cloud service; AutoKeras is open-source and runs locally

## FAQ
**Q: How long does an AutoKeras search take?**
A: Depends on max_trials and dataset size. A 10-trial search on MNIST finishes in minutes; larger datasets with more trials can take hours.

**Q: Can I use a GPU?**
A: Yes. AutoKeras uses TensorFlow, which automatically detects and uses available GPUs.

**Q: Is AutoKeras suitable for production models?**
A: The exported Keras model is a standard TensorFlow model that can be served with TF Serving, converted to TFLite, or exported to ONNX.

**Q: Can I customize the search space?**
A: Yes. Use AutoModel with custom blocks from the autokeras.blocks module to define which layers and hyperparameters to search.

## Sources
- https://github.com/keras-team/autokeras
- https://autokeras.com/

---
Source: https://tokrepo.com/en/workflows/asset-9bbd460e
Author: AI Open Source