spacecruft
/
satnogs-wut
0
0
Fork 0
Code Issues 6 Releases Activity

rename validation/ directory to val/

master
cv server 2020-01-02 19:28:26 -07:00
parent db31f81c00
commit 1e4d21dc1b
2 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
README.md
View File

@ -47,9 +47,9 @@ mkdir -p download
mkdir -p data/train/good mkdir -p data/train/good
mkdir -p data/train/bad mkdir -p data/train/bad
mkdir -p data/train/failed mkdir -p data/train/failed
mkdir -p data/validation/good mkdir -p data/val/good
mkdir -p data/validataion/bad mkdir -p data/val/bad
mkdir -p data/validataion/failed mkdir -p data/val/failed
mkdir -p data/staging mkdir -p data/staging
mkdir -p data/test/unvetted mkdir -p data/test/unvetted
``` ```
@ -104,12 +104,12 @@ The following steps need to be performed:
* `data/train/good/` * `data/train/good/`
* `data/train/bad/` * `data/train/bad/`
* `data/train/failed/` * `data/train/failed/`
* `data/validation/good/` * `data/val/good/`
* `data/validataion/bad/` * `data/val/bad/`
* `data/validataion/failed/` * `data/val/failed/`
1. Use machine learning script `wut-ml` to build a model based on 1. Use machine learning script `wut-ml` to build a model based on
the files in the `data/train` and `data/validation` directories. the files in the `data/train` and `data/val` directories.
1. Rate an observation using the `wut` script. 1. Rate an observation using the `wut` script.

2
wut-ml
View File

@ -16,7 +16,7 @@ from tensorflow.python.keras.preprocessing.image import img_to_array
datagen = ImageDataGenerator() datagen = ImageDataGenerator()
train_it = datagen.flow_from_directory('data/train/', class_mode='binary') train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('data/validation/', class_mode='binary') val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('data/test/', class_mode='binary') test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
batchX, batchy = train_it.next() batchX, batchy = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max())) print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))