initial commit

ex4/submit.m

@@ -0,0 +1,63 @@
+function submit()
+  addpath('./lib');
+
+  conf.assignmentSlug = 'neural-network-learning';
+  conf.itemName = 'Neural Networks Learning';
+  conf.partArrays = { ...
+    { ...
+      '1', ...
+      { 'nnCostFunction.m' }, ...
+      'Feedforward and Cost Function', ...
+    }, ...
+    { ...
+      '2', ...
+      { 'nnCostFunction.m' }, ...
+      'Regularized Cost Function', ...
+    }, ...
+    { ...
+      '3', ...
+      { 'sigmoidGradient.m' }, ...
+      'Sigmoid Gradient', ...
+    }, ...
+    { ...
+      '4', ...
+      { 'nnCostFunction.m' }, ...
+      'Neural Network Gradient (Backpropagation)', ...
+    }, ...
+    { ...
+      '5', ...
+      { 'nnCostFunction.m' }, ...
+      'Regularized Gradient', ...
+    }, ...
+  };
+  conf.output = @output;
+
+  submitWithConfiguration(conf);
+end
+
+function out = output(partId, auxstring)
+  % Random Test Cases
+  X = reshape(3 * sin(1:1:30), 3, 10);
+  Xm = reshape(sin(1:32), 16, 2) / 5;
+  ym = 1 + mod(1:16,4)';
+  t1 = sin(reshape(1:2:24, 4, 3));
+  t2 = cos(reshape(1:2:40, 4, 5));
+  t  = [t1(:) ; t2(:)];
+  if partId == '1'
+    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
+    out = sprintf('%0.5f ', J);
+  elseif partId == '2'
+    [J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
+    out = sprintf('%0.5f ', J);
+  elseif partId == '3'
+    out = sprintf('%0.5f ', sigmoidGradient(X));
+  elseif partId == '4'
+    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
+    out = sprintf('%0.5f ', J);
+    out = [out sprintf('%0.5f ', grad)];
+  elseif partId == '5'
+    [J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
+    out = sprintf('%0.5f ', J);
+    out = [out sprintf('%0.5f ', grad)];
+  end 
+end