--- a
+++ b/evaluateMBIMDS.m
@@ -0,0 +1,30 @@
+function [t_pred, score, y_target] = evaluateMBIMDS(network,imds)
+
+%%
+% To be run on the following networks:
+% network = '1024x1024_34_true_5_class_prostate.onnx'
+% network = '1024x1024_50_true_5_class_prostate.onnx'
+% network = '1024x1024_152_true_5_class_prostate.onnx'
+%%
+% Use michaels original test set for 1024 images
+% imds = imageDatastore('ABSOLUTE_PATH','IncludeSubfolders',true,'labelsource','foldernames');
+% Absolute path should be the top level folder containing folders that
+% seperate classes
+%%
+% For ease of use, set code directory as available path and run each
+% network in its own network folder. This will ensure the saves are
+% appropriately placed.
+
+warning off
+net = importONNXNetwork(network,'OutputLayerType','classification');
+warning on
+
+imdsT = transform(imds,@(x) preNetNorm(x));
+
+[t_pred, score] = classify(net,imdsT);
+
+y_target = imdsT.UnderlyingDatastores{1,1}.Labels;
+
+csvwrite(sprintf('%s_prediction.csv',network(1:end-5)),grp2idx(t_pred));
+csvwrite(sprintf('%s_score.csv',network(1:end-5)),score);
+csvwrite(sprintf('%s_target.csv',network(1:end-5)),grp2idx(y_target));