chore: simplify readme

README.md

@@ -1,111 +1,79 @@
 # KerasTuner
 
 [![license](https://img.shields.io/badge/License-Apache_2.0-green)](https://github.com/ghsanti/keras-tuner/blob/master/LICENSE)
-![py-version](https://img.shields.io/badge/Python-3.11+-blue)
+![py-version](https://img.shields.io/badge/Python-3.10+-blue)
 [![tests](https://github.com/keras-team/keras-tuner/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/ghsanti/actions?query=workflow%3ATests+branch%3Amaster)
 [![codecov](https://codecov.io/gh/ghsanti/keras-tuner/branch/master/graph/badge.svg)](https://codecov.io/gh/ghsanti/keras-tuner)
 
-Personal fork of the great KerasTuner, uploading in case others want to use it.
+[![jax](https://img.shields.io/badge/jax-blue)](https://github.com/jax-ml/jax)
+[![tf](https://img.shields.io/badge/tensorflow-yellow)](https://github.com/tensorflow/tensorflow)
+[![pytorch](https://img.shields.io/badge/pytorch-orange)](https://github.com/pytorch/pytorch)
 
-It was modified to fit personal purposes, but the goal is to make it easy to fix and improve.
+Personal fork of the great [KerasTuner: Original Repo](https://github.com/keras-team/keras-tuner). Feel free to try it out.
 
-## Try it / Install
+## Install
 
-You can run something like below, or try checking the code to a Codespace (it should automatically install everything.):
-
-- Make a test dir: `mkdir test_tuner && cd test_tuner`
-- Create a venv with Python 3.11 `python3.11 -m venv .venv && source .venv/bin/activate`
-- Install stuff
+Check out to Codespace, or install locally:
 
 ```bash
 pip install git+https://github.com/ghsanti/keras-tuner
-pip install tensorflow-cpu # or jax or torch; only tested tf and jax[cpu]
+pip install jax[cpu] # or tf, or torch.
 ```
 
-Then paste the code from `test.py` and see the results.
-
-## Main Changes
-
-- Logs epochs and executions by default, no averaging.
-- Type Annotations.
-- Backwards Incompatible: Drop most old stuff, rename functions, refactor (mostly metrics tracking.)
-
-Yet, usage and output are very similar.
-
-Below you will find the original authors, guides, links etc. All still valid.
-
----
+Try [example.py](https://github.com/ghsanti/keras-tuner/blob/master/example.py) and see the results.
 
-[Original GitHub repository](https://github.com/keras-team/keras-tuner).
+<details>
+<summary>
+Main Changes
+</summary>
+- Detailed results and type annotations (some.)
+</details>
 
-KerasTuner allows you to leverage one of the available search algorithms to find the best hyperparameter values for your models.
-KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.
+<details>
+<summary>
+Built-in algorithms:
+</summary>
+Find the best parameters using the the built-in algorithms:
 
-## Quick links
+- Bayesian Optimization,
+- Hyperband,
+- Random Search
 
-- [Getting started with KerasTuner](https://keras.io/guides/keras_tuner/getting_started)
-- [KerasTuner developer guides](https://keras.io/guides/keras_tuner/)
-- [KerasTuner API reference](https://keras.io/api/keras_tuner/)
+or extend in order to experiment with new search algorithms.
 
-## Quick introduction
+</details>
 
-Import KerasTuner and TensorFlow:
+<details>
+<summary>
+Code Example
+</summary>
 
 ```python
 import keras_tuner
 import keras
-# no need to import tf, it's the default.
-# you have to install it though.
-```
-
-Write a function that creates and returns a Keras model.
-Use the `hp` argument to define the hyperparameters during model creation.
-
-```python
 def build_model(hp):
   model = keras.Sequential()
   model.add(keras.layers.Dense(
       hp.Choice('units', [8, 16, 32]),
       activation='relu'))
-  model.add(keras.layers.Dense(1, activation='relu'))
+  model.add(keras.layers.Dense(1))
   model.compile(loss='mse')
   return model
-```
 
-Initialize a tuner (here, `RandomSearch`).
-We use `objective` to specify the objective to select the best models,
-and we use `max_trials` to specify the number of different models to try.
-
-```python
 tuner = keras_tuner.RandomSearch(
     build_model,
     objective='val_loss',
-    max_trials=5)
-```
+    max_trials=5 # tries with the same parameters.
+  )
 
-Start the search and get the best model:
-
-```python
 tuner.search(x_train, y_train, epochs=5, validation_data=(x_val, y_val))
 best_model = tuner.get_best_models()[0]
 ```
 
-To learn more about KerasTuner, check out [this starter guide](https://keras.io/guides/keras_tuner/getting_started/).
+</details>
 
-## Contributing Guide
-
-Please refer to the [CONTRIBUTING.md](https://github.com/keras-team/keras-tuner/blob/master/CONTRIBUTING.md) for the contributing guide.
-
-## Citing KerasTuner
+- [Starter guide](https://keras.io/guides/keras_tuner/getting_started/).
 
-If KerasTuner helps your research, we appreciate your citations.
-Here is the BibTeX entry:
+## Contributing Guide
 
-```bibtex
-@misc{omalley2019kerastuner,
-	title        = {KerasTuner},
-	author       = {O'Malley, Tom and Bursztein, Elie and Long, James and Chollet, Fran\c{c}ois and Jin, Haifeng and Invernizzi, Luca and others},
-	year         = 2019,
-	howpublished = {\url{https://github.com/keras-team/keras-tuner}}
-}
-```
+Please refer to the [CONTRIBUTING.md](https://github.com/ghsanti/keras-tuner/blob/master/CONTRIBUTING.md) for the contributing guide.