The Inception module in MiniGoogLenet is a variation of the original Inception module designed by Szegedy et al. We will be using the MiniGoogLeNet architecture in our multi-GPU experiments later in this post. Image credit to and Figure 1 above we can see the individual convolution ( left), inception ( middle), and downsample ( right) modules, followed by the overall MiniGoogLeNet architecture ( bottom), constructed from these building blocks. The MiniGoogLeNet deep learning architecture Figure 1: The MiniGoogLeNet architecture is a small version of it’s bigger brother, GoogLeNet/Inception.
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In the remainder of today’s blog post I’ll be demonstrating how to train a Convolutional Neural Network for image classification using Keras, Python, and deep learning. I’ve been using and testing this multi-GPU function for almost a year now and I’m incredibly excited to see it as part of the official Keras distribution. Much of this credit goes to and their keras-extras repo. I preferred using the mxnet backend (or even the mxnet library outright) to Keras when performing multi-GPU training, but that introduced even more configurations to handle.Īll of that changed with François Chollet’s announcement that multi-GPU support using the TensorFlow backend is now baked in to Keras v2.0.9. TensorFlow was a possibility, but it could take a lot of boilerplate code and tweaking to get your network to train using multiple GPUs.
If you were using Theano, forget about it - multi-GPU training wasn’t going to happen. However, one of my biggest frustrations with Keras is that it could be a bit non-trivial to use in multi-GPU environments. Yet it’s extremely powerful, capable of implementing and training state-of-the-art deep neural networks. It’s simple and elegant, similar to scikit-learn. When I first started using Keras I fell in love with the API.
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Sentences in this introduction section may be misleading given the update to TensorFlow/Keras they are left “as-is” for historical purposes.
Update: This blog post is now TensorFlow 2+ compatible! Keras is now built into TensorFlow 2 and serves as TensorFlow’s high-level API. Looking for the source code to this post? Jump Right To The Downloads Section How-To: Multi-GPU training with Keras, Python, and deep learning
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