The implementation of this network is based on the Highway networks paper.
The Highway Network introduces 2 gates in the normal network layer. One gate is called "Transform" gate. The Transform gate is there to control how much information is going to put through from the activation of that layer. Second gate is called the "Carry" gate. It is just 1 - Transform gate. The Carry gate controls how much of UNMODIFIED informations we pass through the network.
By combining these two gates with normal formula for the layer f(xW + b) we get the next formula:
Note: The tricky part with DC Highway networks are dimensions. Just be conscious about the highway layer tensor dimensions.
- Python version used in this project: 3.5+
- TensorFlow 1.2.0
- Numpy 1.10.4
- Time
The code for this project is inside highway_dc_network.ipynb.
To run this project execute one of these two lines in your terminal/cmd:
ipython notebook highway_dc_network.ipynb
or
jupyter notebook highway_dc_network.ipynb
HIGHWAY AND RESIDUAL NETWORKS LEARN UNROLLED ITERATIVE ESTIMATION
Bonus read: Convolutional Networks
MIT License
Copyright (c) 2017 Luka Anicin
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