Skip to content

Instantly share code, notes, and snippets.

@nickmitchko
Created November 14, 2016 02:27
Show Gist options
  • Save nickmitchko/53be14a2a99664c79d5d1e6302fd970e to your computer and use it in GitHub Desktop.
Save nickmitchko/53be14a2a99664c79d5d1e6302fd970e to your computer and use it in GitHub Desktop.
nicholai@nicholai-desktop $> python3 train.py --verbose
/usr/bin/python3.5 /media/nicholai/AEEE-B5B7/School/F16/ComputerVision/Final/Neural-Emotion/adaptiveTraining.py
Using gpu device 0: GeForce GTX 1070 (CNMeM is enabled with initial size: 85.0% of memory, cuDNN 5105)
/home/nicholai/.local/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
/home/nicholai/.local/lib/python3.5/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
"downsample module has been moved to the theano.tensor.signal.pool module.")
/home/nicholai/.local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
Explanation
X, Y: image dimensions
cap.: learning capacity
cov.: coverage of image
magenta: capacity too low (<1/6)
cyan: image coverage too high (>100%)
red: capacity too low and coverage too high
epoch trn loss val loss trn/val valid acc dur
------- ---------- ---------- --------- ----------- ------
1 1.99494 1.96853 1.01341 0.21716 34.52s
2 1.93688 1.90371 1.01742 0.22635 34.42s
3 1.84026 1.85201 0.99365 0.22635 34.15s
...
500 1.84026 1.85201 0.99365 0.22635 34.15s
Parameters Saved.
nicholai@nicholai-desktop $> python3 predict.py --verbose happy.jpg
Loaded Image happy.jpg (filesize: 480x640)
Converted To Grayscale
Extracted Face Location: Top Left => 176x142 , Bottom Right 391x407
Rescaling Face: Size (1x196x196)
Predicting Image: Done!
Emotion: Anger
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment