Merge branch 'master' of github.com:keras-team/keras-io

examples/vision/image_classification_from_scratch.py

@@ -214,7 +214,7 @@
 We'll build a small version of the Xception network. We haven't particularly tried to
 optimize the architecture; if you want to do a systematic search for the best model
  configuration, consider using
-[Keras Tuner](https://github.com/keras-team/keras-tuner).
+[KerasTuner](https://github.com/keras-team/keras-tuner).
 
 Note that:
 

examples/vision/ipynb/image_classification_from_scratch.ipynb

@@ -375,7 +375,7 @@
     "We'll build a small version of the Xception network. We haven't particularly tried to\n",
     "optimize the architecture; if you want to do a systematic search for the best model\n",
     " configuration, consider using\n",
-    "[Keras Tuner](https://github.com/keras-team/keras-tuner).\n",
+    "[KerasTuner](https://github.com/keras-team/keras-tuner).\n",
     "\n",
     "Note that:\n",
     "\n",

examples/vision/md/image_classification_from_scratch.md

@@ -291,7 +291,7 @@ val_ds = val_ds.prefetch(buffer_size=32)
 We'll build a small version of the Xception network. We haven't particularly tried to
 optimize the architecture; if you want to do a systematic search for the best model
  configuration, consider using
-[Keras Tuner](https://github.com/keras-team/keras-tuner).
+[KerasTuner](https://github.com/keras-team/keras-tuner).
 
 Note that:
 

guides/intro_to_keras_for_engineers.py

@@ -803,7 +803,7 @@ def train_step(self, data):
  want to leverage a systematic approach: hyperparameter search.
 
 You can use
-[Keras Tuner](https://keras-team.github.io/keras-tuner/documentation/tuners/) to find
+[KerasTuner](https://keras.io/api/keras_tuner/tuners/) to find
  the best hyperparameter for your Keras models. It's as easy as calling `fit()`.
 
 Here how it works.

guides/ipynb/intro_to_keras_for_engineers.ipynb

@@ -1116,7 +1116,7 @@
     " want to leverage a systematic approach: hyperparameter search.\n",
     "\n",
     "You can use\n",
-    "[Keras Tuner](https://keras-team.github.io/keras-tuner/documentation/tuners/) to find\n",
+    "[KerasTuner](https://keras.io/api/keras_tuner/tuners/) to find\n",
     " the best hyperparameter for your Keras models. It's as easy as calling `fit()`.\n",
     "\n",
     "Here how it works.\n",

guides/md/intro_to_keras_for_engineers.md

@@ -954,7 +954,7 @@ architecture choices, layer sizes, etc. Human intuition can only go so far, so y
  want to leverage a systematic approach: hyperparameter search.
 
 You can use
-[Keras Tuner](https://keras-team.github.io/keras-tuner/documentation/tuners/) to find
+[KerasTuner](https://keras.io/api/keras_tuner/tuners/) to find
  the best hyperparameter for your Keras models. It's as easy as calling `fit()`.
 
 Here how it works.

scripts/kt_api_master.py

@@ -131,7 +131,7 @@
 
 KT_API_MASTER = {
     'path': 'keras_tuner/',
-    'title': 'Keras Tuner',
+    'title': 'KerasTuner',
     'toc': True,
     'children': [
         {

templates/api/keras_tuner/hypermodels/index.md

@@ -1,4 +1,4 @@
-# Keras Tuner HyperModels
+# KerasTuner HyperModels
 
 The `HyperModel` base class makes the search space better encapsulated for
 sharing and reuse.  A `HyperModel` subclass only needs to implement a

templates/api/keras_tuner/index.md

@@ -1,4 +1,4 @@
-# Keras Tuner API
+# KerasTuner API
 
 The **Hyperparameters** class is used to specify a set of hyperparameters
 and their values, to be used in the model building function.

templates/api/keras_tuner/oracles/index.md

@@ -1,4 +1,4 @@
-# Keras Tuner Oracles
+# KerasTuner Oracles
 
 The `Oracle` class is the base class for all the search algorithms in KerasTuner. 
 An `Oracle` object receives evaluation results for a model (from a `Tuner` class)

templates/api/keras_tuner/tuners/index.md

@@ -1,4 +1,4 @@
-# The Tuner classes in Keras Tuner
+# The Tuner classes in KerasTuner
 
 The base `Tuner` class is the class that manages the hyperparameter search process,
 including model creation, training, and evaluation.  For each trial, a `Tuner` receives new
@@ -10,7 +10,7 @@ There are a few built-in `Tuner` subclasses available for widely-used tuning
 algorithms: `RandomSearch`, `BayesianOptimization` and `Hyperband`.
 
 You can also subclass the `Tuner` class to customize your tuning process.
-In particular, you can [override the `run_trial` function](/guides/keras-tuner/custom_tuner/#overriding-runtrial)
+In particular, you can [override the `run_trial` function](/guides/keras_tuner/custom_tuner/#overriding-runtrial)
 to customize model building and training.
 
 {{toc}}

templates/getting_started/ecosystem.md

@@ -5,12 +5,12 @@ It spans a wide range of related initiatives that cover every step of the machin
 
 ---
 
-## Keras Tuner
+## KerasTuner
 
-[Keras Tuner Documentation](/keras-tuner/) - [Keras Tuner GitHub repository](https://github.com/keras-team/keras-tuner)
+[KerasTuner Documentation](/keras_tuner/) - [KerasTuner GitHub repository](https://github.com/keras-team/keras-tuner)
 
 
-Keras Tuner 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. Keras Tuner 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.
+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.
 
 ---
 

templates/getting_started/faq.md

@@ -297,7 +297,7 @@ and cached model weights files from Keras Applications are stored by default in
 ### How to do hyperparameter tuning with Keras?
 
 
-We recommend using [Keras Tuner](https://keras-team.github.io/keras-tuner/).
+We recommend using [KerasTuner](https://keras.io/keras_tuner/).
 
 ---
 

templates/getting_started/intro_to_keras_for_engineers.md

@@ -954,7 +954,7 @@ architecture choices, layer sizes, etc. Human intuition can only go so far, so y
  want to leverage a systematic approach: hyperparameter search.
 
 You can use
-[Keras Tuner](https://keras-team.github.io/keras-tuner/documentation/tuners/) to find
+[KerasTuner](https://keras.io/api/keras_tuner/tuners/) to find
  the best hyperparameter for your Keras models. It's as easy as calling `fit()`.
 
 Here how it works.

templates/keras_tuner/index.md

@@ -84,7 +84,7 @@ Here is the BibTeX entry:
 
 ```bibtex
 @misc{omalley2019kerastuner,
-	title        = {Keras Tuner},
+	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}}

templates/why_keras.md

@@ -79,7 +79,7 @@ Keras is at the center of a wide ecosystem of tightly-connected projects that to
 
 - Rapid model prototyping with [AutoKeras](https://autokeras.com/)
 - Scalable model training in on GCP via [TF Cloud](https://github.com/tensorflow/cloud)
-- Hyperparameter tuning with [Keras Tuner](https://keras-team.github.io/keras-tuner/)
+- Hyperparameter tuning with [KerasTuner](https://keras.io/keras_tuner/)
 - Extra layers, losses, metrics, callbacks... via [TensorFlow Addons](https://www.tensorflow.org/addons/api_docs/python/tfa)
 - Inference model quantization & pruning with the [TF Model Optimization Toolkit](https://www.tensorflow.org/model_optimization)
 - Model deployment on mobile or on an embedded with [TF Lite](https://www.tensorflow.org/lite)