Other articles

  1. Building Autoencoders in Keras

    In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models:

    • a simple autoencoder based on a fully-connected layer
    • a sparse autoencoder
    • a deep fully-connected autoencoder
    • a deep convolutional autoencoder
    • an image denoising model
    • a sequence-to-sequence autoencoder
    • a variational autoencoder ...
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  2. Introducing Keras 1.0

    Keras was initially released a year ago, late March 2015. It has made tremendous progress since, both on the development front, and as a community.

    But continuous improvement isn't enough. A year of developing Keras, using Keras, and getting feedback from thousands of users has taught us a lot ...

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  3. How convolutional neural networks see the world

    An exploration of convnet filters with Keras

    In this post, we take a look at what deep convolutional neural networks (convnets) really learn, and how they understand the images we feed them. We will use Keras to visualize inputs that maximize the activation of the filters in different layers of ...

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  4. Keras, now running on TensorFlow

    The purpose of Keras is to be a model-level framework, providing a set of "Lego blocks" for building Deep Learning models in a fast and straightforward way. Among Deep Learning frameworks, Keras is resolutely high up on the ladder of abstraction.

    As such, Keras does not handle itself low-level tensor ...

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