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@@ -28,11 +28,13 @@ I've also made lots of tweaks for flexibility and stability and [replicated the
- [Example NP](#example-np)
- [Example ANP outputs (sequential)](#example-anp-outputs-sequential)
- [Example ANP-RNN outputs](#example-anp-rnn-outputs)
- [Example of ANP-RNN with MCDropout](#example-of-anp-rnn-with-mcdropout)
- [Replicating DeepMind's tensorflow ANP behaviour](#replicating-deepminds-tensorflow-anp-behaviour)
- [Using Monte Carlo Dropout](#using-monte-carlo-dropout)
- [Usage](#usage)
- [Smartmeter Data](#smartmeter-data)
- [Code](#code)
- [ANP-RNN diagram](#anp-rnn-diagram)
- [See also:](#see-also)
@@ -90,11 +92,10 @@ This has a better calibrated uncertainty and a better fit
![](docs/anp-rnn_4.png)
A Diagram of ANP-RNN's
![](docs/np_lstm.jpeg)
### Example of ANP-RNN with MCDropout
![](docs/anp-rnn-mcdropout.png)
## Replicating DeepMind's tensorflow ANP behaviour
@@ -177,6 +178,10 @@ Changes for stability:
- check and skip nonfinite values because for extreme inputs we can still get nan's. Also gradient clipping
- use pytorch lightning for early stopping, hyperparam opt, and reduce learning rate on plateau
## ANP-RNN diagram
![](docs/anp-rnn-mcdropout.png)
## See also:
A list of projects I used as reference or modified to make this one: