- In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
- The core data structure of Keras is a model, a way to organize layers.
- A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
- You can now iterate on your training data in batches:
Alternatively, you can feed batches to your model manually:
Evaluate your performance in one line:
Or generate predictions on new data:
Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast.
- For a more in-depth tutorial about Keras, you can check out:
In the examples folder of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Continue reading “Keras: Deep Learning library for Theano and TensorFlow”