Keras - Python Deep Learning Neural Network API

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Initializing and Accessing Bias with Keras

April 18, 2018 by

Description

Let's see how we can initialize and access the biases in a neural network in code with Keras. Specifically, we’ll be working with the Keras Sequential model along with the use_bias and bias_initializer parameters to initialize biases. We’ll then observe the values of the biases by calling get_weights() on the model. Find out more information on bias in neural networks in the following video that spills all the details. https://youtu.be/HetFihsXSys Follow deeplizard: YouTube: https://www.youtube.com/deeplizard Twitter: https://twitter.com/deeplizard Facebook: https://www.facebook.com/Deeplizard-145413762948316 Steemit: https://steemit.com/@deeplizard Instagram: https://www.instagram.com/deeplizard/ Support deeplizard on Patreon: https://www.patreon.com/deeplizard Checkout products deeplizard suggests on Amazon: https://www.amazon.com/shop/deeplizard Support deeplizard with crypto: Bitcoin: 1AFgm3fLTiG5pNPgnfkKdsktgxLCMYpxCN Litecoin: LTZ2AUGpDmFm85y89PFFvVR5QmfX6Rfzg3 Ether: 0x9105cd0ecbc921ad19f6d5f9dd249735da8269ef Recommended books on AI: The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive: http://amzn.to/2GtjKqu Life 3.0: Being Human in the Age of Artificial Intelligence https://amzn.to/2H5Iau4 Playlists: Keras - https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL Machine Learning - https://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Data Science - https://www.youtube.com/playlist?list=PLZbbT5o_s2xo_SRS9wn9OSs_kzA9Jfz8k Checkout posts for this video: https://www.patreon.com/posts/18304689 https://www.instagram.com/p/BhznPvDlsY9/?taken-by=deeplizard https://twitter.com/deeplizard/status/987432151019851777 https://steemit.com/deep-learning/@deeplizard/initializing-and-accessing-bias-in-an-artificial-neural-network-with-keras Music: Brittle Rille by Kevin MacLeod Website: http://incompetech.com/ Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/