function for classifier

Author: epnev

utilities/cnn_classifier.m

@@ -0,0 +1,40 @@
+function [ind,value] = cnn_classifier(A,dims,classifier,thr)
+
+%cnn_classifer classify spatial components using a pretrained CNN
+%classifier using the keras importer add on.
+%   IND = cnn_classifier(A,dims,classifier,thr) returns a binary vector indicating
+%   whether the set of spatial components A, with dimensions of the field
+%   of view DIMS, pass the threshold THR for the given CLASSIFIER
+%
+%   [IND,VALUE] = cnn_classifier(A,dims,classifier,thr) also returns the
+%   output value of the classifier 
+%
+%   INPUTS:
+%   A:              2d matrix
+%   dims:           vector with dimensions of the FOV
+%   classifier:     path to pretrained classifier model
+%   thr:            threshold for accepting component (default: 0.2)
+%
+%   note: The function requires Matlab version 2017b (9.3) or later, Neural
+%   Networks toolbox version 2017b (11.0) or later, the Neural Network 
+%   Toolbox(TM) Importer for TensorFlow-Keras Models.
+
+%   Written by Eftychios A. Pnevmatikakis. Classifier trained by Andrea
+%   Giovannucci, Flatiron Institute, 2017
+
+if verLessThan('matlab','9.3') || verLessThan('nnet','11.0') || isempty(which('importKerasNetwork'))
+    error(strcat('The function requires Matlab version 2017b (9.3) or later, Neural\n', ...
+        'Networks toolbox version 2017b (11.0) or lvalater, the Neural Networks ', ...
+        'Toolbox(TM) Importer for TensorFlow-Keras Models.'))
+end
+
+if ~exist('thr','var'); thr = 0.2; end
+
+K = size(A,2);                          % number of components
+A = A/spdiags(sqrt(sum(A.^2,1))'+eps,0,K,K);      % normalize to sum 1 for each compoennt
+A_com = extract_patch(A,dims,[50,50]);  % extract 50 x 50 patches
+
+net_classifier = importKerasNetwork(classifier);
+out = predict(net_classifier,double(A_com));
+value = out(:,2);
+ind = (value >= thr);