Comments (2)
Teh difference between forcings
and inputs
mostly have to do with the time associated with the features. inputs
contain time data associated with past and present times. forcings
and targets
contain time data associated with future times.
Having a variable both in inputs
and in forcings
means you are going to condition your model both on present/past values of that variable, as well as on future values of that variable. Conditioning on a future value is possible because every forcing
variable is something that can be computed analytically for any future date.
Additionally, to be able to rollout
the model autoregressively, every single variable in inputs
, needs to be either static, be a target, or be a forcing, because as you advance the prediction by one step, then the future timesteps become the present timesteps, so anything that is not predicted for the future, it needs to be available as a forcing. I you look at rollout.py
, you will see how at each step as time shifts, some forcings variables are moved to the inputs variables.
from graphcast.
Ah okay now I understand. The variables in inputs
are from the previous state (t-1) and the present (t), and forcing are from the future (t+1), so they are not repeated.
I have another question. Using the same model after concatenating inputs
and forcing
I get a numpy array of size num_points x batch x 183
, but in params the weight of the embedding layer have size 186 x 512. What are these 3 features and where are they added?
Thank you so much!
from graphcast.
Related Issues (20)
- Jax Error only when TPU-enabled runtime selected HOT 2
- Predicting Forecast for 10 Days , 5 Days HOT 2
- Obtaining successive forecasts based on previous predictions HOT 2
- Haiku needs all `hk.Module` must be initialized inside an `hk.transform` HOT 1
- About loss weights HOT 3
- GPU / TPU memory requirements for training HOT 3
- [GraphCast Operational Model] Issue with Negative Precipitation Data in GraphCast Operational Model Output HOT 2
- How to get the data in the paper? HOT 1
- weights license - use of graphcast HOT 5
- Graphcast error on Mac os HOT 1
- Problems feeding data to operational model: Target variable geopotential_at_surface must be time-dependent HOT 1
- when is the prediction result of this demo? HOT 2
- Forecasting beyond 10 days HOT 8
- Cyclone tracking
- There are some questions about forecasting. HOT 1
- Fine-Tuning Strategy for the GraphCast Operational Model HOT 2
- About the atmospheric variable “Vertical velocity”
- about the autoregressive finetuning HOT 1
- How to train a model by myself HOT 6
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from graphcast.