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fix crash

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Starbeamrainbowlabs 2022-08-31 16:25:27 +01:00
parent fe7a8b3fc0
commit f2312c1184
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 3 additions and 3 deletions
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4
README.md
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@ -4,7 +4,7 @@
This is the 3rd major version of this model. This is the 3rd major version of this model.
Unfortunately using this model is rather complicated and involves a large number of steps. This README (will) explain it the best I can though. Unfortunately using this model is rather complicated and involves a large number of steps. There is no way around this. This README (will) explain it the best I can though.
## System Requirements ## System Requirements
@ -24,7 +24,7 @@ The process of using this model is as follows.
3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this) 3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this)
4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data] 4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data]
5. Push through [HAIL-CAESAR](*https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename) 5. Push through [HAIL-CAESAR](*https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename)
6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .tfrecord files 6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files
7. Pretrain a contrastive learning model 7. Pretrain a contrastive learning model
8. Encode the rainfall radar data with the contrastive learning model you pretrained 8. Encode the rainfall radar data with the contrastive learning model you pretrained
9. Train the *actual* model to predict water depth 9. Train the *actual* model to predict water depth

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aimodel/src/lib/dataset/dataset.py
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@ -7,7 +7,7 @@ from loguru import logger
import tensorflow as tf import tensorflow as tf
from shuffle import shuffle from .shuffle import shuffle
# TO PARSE: # TO PARSE: