Keras 2.2.5 was the last release of Keras implementing the 2.2.* API. It was the last release to only support TensorFlow 1 (as well as Theano and CNTK). The current release is Keras 2.3.0, which makes significant API changes and add support for TensorFlow 2.0. The 2.3.0 release will be the last major release of multi-backend Keras.
You first need to access the under lying model over which the kerasregressor wrapper is used, and then call save on it. Use model.model.tojson() and model.model.save_weights() – Vivek Kumar Jun 4 '17 at 1:55
Implementation of the scikit-learn regressor API for Keras
Keras is one of the most popular deep learning libraries in Python for research and development because of its simplicity and ease of use. The scikit-learn library is the most popular library for general machine learning in Python. In this post you will discover how you can use deep learning models
On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset. Note that the training score and the cross-validation score are both not very good at the end. However, the shape of the curve can be found in more complex datasets very often: the training score is very
Keras Documentation. Docs ... This means you implement a class that inherits from either KerasClassifier or KerasRegressor. The call method of the present class will then be treated as the default build_fn. sk_params takes both model parameters and fitting parameters.