-
Notifications
You must be signed in to change notification settings - Fork 18
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
updated EXPERIMENTS.MD and added their artefacts and data respectively
- Loading branch information
1 parent
2ae17b7
commit 4d2f285
Showing
16 changed files
with
2,332 additions
and
13 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| # KerasTuner | ||
|
|
||
| <a class="github-button" href="https://github.com/keras-team/keras-tuner" data-size="large" data-show-count="true" aria-label="Star keras-team/keras-tuner on GitHub">Star</a> | ||
|
|
||
| KerasTuner is an easy-to-use, scalable hyperparameter optimization framework | ||
| that solves the pain points of hyperparameter search. Easily configure your | ||
| search space with a define-by-run syntax, then 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. | ||
|
|
||
| --- | ||
| ## Quick links | ||
|
|
||
| * [Getting started with KerasTuner](/guides/keras_tuner/getting_started/) | ||
| * [KerasTuner developer guides](/guides/keras_tuner/) | ||
| * [KerasTuner API reference](/api/keras_tuner/) | ||
| * [KerasTuner on GitHub](https://github.com/keras-team/keras-tuner) | ||
|
|
||
|
|
||
| --- | ||
| ## Installation | ||
|
|
||
| Install the latest release: | ||
|
|
||
| ``` | ||
| pip install keras-tuner --upgrade | ||
| ``` | ||
|
|
||
| You can also check out other versions in our | ||
| [GitHub repository](https://github.com/keras-team/keras-tuner). | ||
|
|
||
|
|
||
| --- | ||
| ## Quick introduction | ||
|
|
||
| Import KerasTuner and TensorFlow: | ||
|
|
||
| ```python | ||
| import keras_tuner | ||
| import keras | ||
| ``` | ||
|
|
||
| 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.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) | ||
| ``` | ||
|
|
||
| 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/). | ||
|
|
||
|
|
||
| --- | ||
| ## Citing KerasTuner | ||
|
|
||
| If KerasTuner helps your research, we appreciate your citations. | ||
| Here is the BibTeX entry: | ||
|
|
||
| ```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}} | ||
| } | ||
| ``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,146 @@ | ||
| # KerasNLP | ||
|
|
||
| <a class="github-button" href="https://github.com/keras-team/keras-nlp" data-size="large" data-show-count="true" aria-label="Star keras-team/keras-nlp on GitHub">Star</a> | ||
|
|
||
| KerasNLP is a natural language processing library that works natively | ||
| with TensorFlow, JAX, or PyTorch. Built on Keras 3, these models, layers, | ||
| metrics, and tokenizers can be trained and serialized in any framework and | ||
| re-used in another without costly migrations. | ||
|
|
||
| KerasNLP supports users through their entire development cycle. Our workflows | ||
| are built from modular components that have state-of-the-art preset weights when | ||
| used out-of-the-box and are easily customizable when more control is needed. | ||
|
|
||
| This library is an extension of the core Keras API; all high-level modules are | ||
| [`Layers`](/api/layers/) or | ||
| [`Models`](/api/models/) that receive that same level of polish | ||
| as core Keras. If you are familiar with Keras, congratulations! You already | ||
| understand most of KerasNLP. | ||
|
|
||
| See our [Getting Started guide](/guides/keras_nlp/getting_started) | ||
| to start learning our API. We welcome | ||
| [contributions](https://github.com/keras-team/keras-nlp/blob/master/CONTRIBUTING.md). | ||
|
|
||
| --- | ||
| ## Quick links | ||
|
|
||
| * [KerasNLP API reference](/api/keras_nlp/) | ||
| * [KerasNLP on GitHub](https://github.com/keras-team/keras-nlp) | ||
| * [List of available pre-trained models](/api/keras_nlp/models/) | ||
|
|
||
| ## Guides | ||
|
|
||
| * [Getting Started with KerasNLP](/guides/keras_nlp/getting_started/) | ||
| * [Uploading Models with KerasNLP](/guides/keras_nlp/upload/) | ||
| * [Pretraining a Transformer from scratch](/guides/keras_nlp/transformer_pretraining/) | ||
|
|
||
| ## Examples | ||
|
|
||
| * [GPT-2 text generation](/examples/generative/gpt2_text_generation_with_kerasnlp/) | ||
| * [Parameter-efficient fine-tuning of GPT-2 with LoRA](/examples/nlp/parameter_efficient_finetuning_of_gpt2_with_lora/) | ||
| * [Semantic Similarity](/examples/nlp/semantic_similarity_with_keras_nlp/) | ||
| * [Sentence embeddings using Siamese RoBERTa-networks](/examples/nlp/sentence_embeddings_with_sbert/) | ||
| * [Data Parallel Training with tf.distribute](/examples/nlp/data_parallel_training_with_keras_nlp/) | ||
| * [English-to-Spanish translation](/examples/nlp/neural_machine_translation_with_keras_nlp/) | ||
| * [GPT text generation from scratch](/examples/generative/text_generation_gpt/) | ||
| * [Text Classification using FNet](/examples/nlp/fnet_classification_with_keras_nlp/) | ||
|
|
||
| --- | ||
| ## Installation | ||
|
|
||
| KerasNLP supports both Keras 2 and Keras 3. We recommend Keras 3 for all new | ||
| users, as it enables using KerasNLP models and layers with JAX, TensorFlow and | ||
| PyTorch. | ||
|
|
||
| ### Keras 2 Installation | ||
|
|
||
| To install the latest KerasNLP release with Keras 2, simply run: | ||
|
|
||
| ``` | ||
| pip install --upgrade keras-nlp | ||
| ``` | ||
|
|
||
| ### Keras 3 Installation | ||
|
|
||
| There are currently two ways to install Keras 3 with KerasNLP. To install the | ||
| stable versions of KerasNLP and Keras 3, you should install Keras 3 **after** | ||
| installing KerasNLP. This is a temporary step while TensorFlow is pinned to | ||
| Keras 2, and will no longer be necessary after TensorFlow 2.16. | ||
|
|
||
| ``` | ||
| pip install --upgrade keras-nlp | ||
| pip install --upgrade keras | ||
| ``` | ||
|
|
||
| To install the latest nightly changes for both KerasNLP and Keras, you can use | ||
| our nightly package. | ||
|
|
||
| ``` | ||
| pip install --upgrade keras-nlp-nightly | ||
| ``` | ||
|
|
||
| **Note:** Keras 3 will not function with TensorFlow 2.14 or earlier. | ||
|
|
||
| See [Getting started with Keras](/getting_started/) for more information on | ||
| installing Keras generally and compatibility with different frameworks. | ||
|
|
||
| --- | ||
| ## Quickstart | ||
|
|
||
| Fine-tune BERT on a small sentiment analysis task using the | ||
| [`keras_nlp.models`](/api/keras_nlp/models/) API: | ||
|
|
||
| ```python | ||
| import os | ||
| os.environ["KERAS_BACKEND"] = "tensorflow" # Or "jax" or "torch"! | ||
|
|
||
| import keras_nlp | ||
| import tensorflow_datasets as tfds | ||
|
|
||
| imdb_train, imdb_test = tfds.load( | ||
| "imdb_reviews", | ||
| split=["train", "test"], | ||
| as_supervised=True, | ||
| batch_size=16, | ||
| ) | ||
| # Load a BERT model. | ||
| classifier = keras_nlp.models.BertClassifier.from_preset( | ||
| "bert_base_en_uncased", | ||
| num_classes=2, | ||
| ) | ||
| # Fine-tune on IMDb movie reviews. | ||
| classifier.fit(imdb_train, validation_data=imdb_test) | ||
| # Predict two new examples. | ||
| classifier.predict(["What an amazing movie!", "A total waste of my time."]) | ||
| ``` | ||
|
|
||
| --- | ||
| ## Compatibility | ||
|
|
||
| We follow [Semantic Versioning](https://semver.org/), and plan to | ||
| provide backwards compatibility guarantees both for code and saved models built | ||
| with our components. While we continue with pre-release `0.y.z` development, we | ||
| may break compatibility at any time and APIs should not be consider stable. | ||
|
|
||
| ## Disclaimer | ||
|
|
||
| KerasNLP provides access to pre-trained models via the `keras_nlp.models` API. | ||
| These pre-trained models are provided on an "as is" basis, without warranties | ||
| or conditions of any kind. The following underlying models are provided by third | ||
| parties, and subject to separate licenses: | ||
| BART, DeBERTa, DistilBERT, GPT-2, OPT, RoBERTa, Whisper, and XLM-RoBERTa. | ||
|
|
||
| ## Citing KerasNLP | ||
|
|
||
| If KerasNLP helps your research, we appreciate your citations. | ||
| Here is the BibTeX entry: | ||
|
|
||
| ```bibtex | ||
| @misc{kerasnlp2022, | ||
| title={KerasNLP}, | ||
| author={Watson, Matthew, and Qian, Chen, and Bischof, Jonathan and Chollet, | ||
| Fran\c{c}ois and others}, | ||
| year={2022}, | ||
| howpublished={\url{https://github.com/keras-team/keras-nlp}}, | ||
| } | ||
| ``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,21 @@ | ||
| # KerasCV Bounding Boxes | ||
|
|
||
| All KerasCV components that process bounding boxes require a `bounding_box_format` | ||
| argument. This argument allows you to seamlessly integrate KerasCV components into | ||
| your own workflows while preserving proper behavior of the components themselves. | ||
|
|
||
| Bounding boxes are represented by dictionaries with two keys: `'boxes'` and `'classes'`: | ||
|
|
||
| ``` | ||
| { | ||
| 'boxes': [batch, num_boxes, 4], | ||
| 'classes': [batch, num_boxes] | ||
| } | ||
| ``` | ||
|
|
||
| To ensure your bounding boxes comply with the KerasCV specification, you can use [`keras_cv.bounding_box.validate_format(boxes)`](https://github.com/keras-team/keras-cv/blob/master/keras_cv/bounding_box/validate_format.py). | ||
|
|
||
| The bounding box formats supported in KerasCV | ||
| [are listed in the API docs](/api/keras_cv/bounding_box/formats) | ||
| If a format you would like to use is missing, | ||
| [feel free to open a GitHub issue on KerasCV](https://github.com/keras-team/keras-cv/issues)! |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| # KerasCV | ||
|
|
||
| These guides cover the [KerasCV](/keras_cv/) library. | ||
|
|
||
| ## Available guides | ||
|
|
||
| {{toc}} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,7 @@ | ||
| # KerasNLP | ||
|
|
||
| These guides cover the [KerasNLP](/keras_nlp/) library. | ||
|
|
||
| ## Available guides | ||
|
|
||
| {{toc}} |
Oops, something went wrong.