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function out=fun_training_evaulation(model,nmb_of_hidden_layers)
%
%
nmb_of_modules=40;
nmb_of_module_subsets=2;
channels_names={'R','G','B','RGg1','RBg1','GBg1','RGg2','RBg2','GBg2','RB','RG','GB','eRGB','BW','X','Y','Z'};
% feature_RGB=[1 0 0
% 0 1 0
% 0 0 1
% 0.618 0.382 0
% 0.618 0 0.382
% 0 0.618 0.382
% 0.382 0.618 0
% 0.382 0 0.618
% 0 0.382 0.618
% 0.5 0.5 0
% 0.5 0 0.5
% 0 0.5 0.5
% 1/3 1/3 1/3
% 0.299 0.587 0.114
% 0.4125 0.3576 0.1804
% 0.2126 0.7152 0.0722
% 0.0193 0.1192 0.9502];
nmb_of_colors=length(channels_names);
patch=0;
nmb_of_labs_per_module=25;
pstvrt_model=zeros(nmb_of_colors,nmb_of_modules,nmb_of_module_subsets);
%%
parfor module=1:nmb_of_modules
% pm=param;
channels_names={'R','G','B','RGg1','RBg1','GBg1','RGg2','RBg2','GBg2','RB','RG','GB','eRGB','BW','X','Y','Z'};
for subset=1:nmb_of_module_subsets
reportname1 = sprintf('../../Data_Transformation/Transformed_IN1k_Data/Transformed_Data_for_SGD/train_data_patch_%d_module_%d_subset_%d_for_%d_labels_per_module.mat', ...
patch,module,subset,nmb_of_labs_per_module);
data_load=load(reportname1);
% % reportname1 = sprintf('../%s.mat',model);
% % temp_load=load(reportname1);
% % model_parameter=temp_load.model_parameters;
for color=1:nmb_of_colors
reportname1 = sprintf('../Model_Parameter/Trained_Parameter_patch_%d_module_%d_subset_%d_ch_%s.mat',...
patch, module, subset, char(channels_names(color)));
% str=struct('W', W, 'b', b);
temp_load=load(reportname1);
W=temp_load.W;
b=temp_load.b;
% Assign the trained parameters
%
W1=W.LayerName1;
W2=W.LayerName2;
b1=b.LayerName1;
b2=b.LayerName2;
% % W1=model_parameter.W1(:,:,color,subset,module);
% % W2=model_parameter.W2(:,:,color,subset,module);
% % b1=model_parameter.b1(:,:,color,subset,module);
% % b2=model_parameter.b2(:,:,color,subset,module);
a_0=data_load.data(:,:,color);
true_label=data_load.labels;
dtsz=length(true_label);
nmb_of_labels=length(b2);
z1=W1*a_0+b1;
[a1,~]=fun_activation(z1);
z2=W2*a1+b2;
if nmb_of_hidden_layers==1
[a2,~]=fun_softmax(z2);
predicted_vector=a2;
else
W3=W.LayerName3;
b3=b.LayerName3;
nmb_of_labels=length(b3);
[a2,~]=fun_activation(z2);
z3=W3*a2+b3;
[a3,~]=fun_softmax(z3);
predicted_vector=a3;
end
prediction=fun_predicted_vector_2_label(predicted_vector,nmb_of_labels);
%%
% Compute the error and positive rates.
%
v=abs(true_label-prediction.label);
errt=sum(1.*(v>0))/dtsz;
pstvrt=(1-errt)*100;
pstvrt_model(color,module,subset)=pstvrt;
aa=(module-1)*nmb_of_labs_per_module; %data_load.label_table(2,1)-1;
training_performance=[true_label+aa;prediction.label+aa;prediction.distance];
out=fun_save_result(model,patch, module, subset,color,channels_names,training_performance);
end
end
module
end
reportname1 = sprintf('%s_1_performance.mat',model);
save(reportname1, 'pstvrt_model');
out=[];
end |