Keras 1.0 – Python deep learning framework
blog.keras.io
blog.keras.io
- it's much easier to use. Using pure TensorFlow is considered "advanced" and requires familiarity with deep learning, understanding of what a symbolic computation graph is, etc. Keras, meanwhile, is meant to make deep learning more accessible.
- even if you don't care about accessibility, Keras provides higher-level building blocks that speed up your workflow even if you are an expert. It is currently used by dozens of companies and hundreds of researchers, precisely for this reason: it allows quick prototyping.
- with Keras, you can work with both Theano and TensorFlow interchangeably. They complement each nicely in a workflow: TensorFlow has low compilation times, which is great for debugging, and Theano tends to be faster for runtime (especially for RNNs). So you can prototype in TF, train in Theano, then to switch to production you can export the TF model.
Great job!
There are a variety of other wrappers for Tensorflow (skflow, tf-slim) that may someday be released as part of the Tensorflow project. But Keras is far more mature than those right now.
Keep up the great work!
I hear a lot about new ML/neural network frameworks these days and it's difficult to know which complement other frameworks and which build upon other frameworks/create another layer.
Congrats on the 1.0 release!
I came across that glossary a few days ago when looking to solve an RNN problem with a deadline. You may be pleased to know I think I may have solved it by switching to Keras, and this post actually helped unsticking me from a smaller problem too. Thanks, and congratulations on version 1.0!
I'm brand new to Keras and online tutorials say that "everything, even activations are their own layer" but I can see that all the layers have a activation argument.