Pyhton layer for the Caffe Caffe deep learning framework to compute the accuracy and the confusion matrix. This layer will print a confusion matrix of the TEST predictions after the whole TEST images have been processed. It will also work as an accuracy layer, providing Caffe with the predictions accuracy on the TEST set.
The usage is very simple, the layer just has to be used as an accuracy layer in the prototxt file like:
layer {
type: 'Python'
name: 'py_accuracy'
top: 'py_accuracy'
bottom: 'ip2'
bottom: 'label'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'python_confmat'
# the layer name -- the class name in the module
layer: 'PythonConfMat'
param_str: '{"test_iter":100}'
}
include {
phase: TEST
}
}
There is a working example in the examples folder, which must be copied in caffe/examples folder in order for the relative paths to work. The file python_confmat.py must be copied in caffe/examples/mnist to work for the example, but for your own usage you can place it anywhere as long as the path is included in your $PYTHONPATH.
The confusion matrix is printed to console and looks like this:
Confusion Matrix | Accuracy
------------------------------------------------------------------------
3438 166 191 16 45 9 136 0 | 85.93 %
191 3306 177 1 69 2 15 0 | 87.90 %
88 114 3205 34 431 46 80 3 | 80.10 %
30 12 98 3735 78 23 24 0 | 93.38 %
11 28 437 29 3196 65 45 11 | 83.62 %
3 0 64 7 38 3702 8 0 | 96.86 %
59 4 79 42 44 5 3234 1 | 93.25 %
2 0 29 3 113 9 6 2639 | 94.22 %
Number of test samples: 29676