diff --git a/nn/data.js b/nn/data.js
new file mode 100644
index 0000000..8b96108
--- /dev/null
+++ b/nn/data.js
@@ -0,0 +1,23 @@
+var Tensor = require('../tensor.js');
+var U = module.exports = {}
+
+U.toTensor = function (dim, flatArray) {
+  return new Tensor(dim).fromFlatArray(flatArray)
+}
+
+U.tensor1d = function (array) {
+  return new Tensor([array.length]).fromFlatArray(array)
+}
+
+U.loadData = function (data) {
+  var tensorData = []
+  for (var i = 0; i < data.length; i++) {
+    var x = U.tensor1d(data[i].input)
+    var y = U.tensor1d(data[i].output)
+    tensorData.push({
+      input: x,
+      output: y
+    })
+  }
+  return tensorData
+}
diff --git a/nn/index.js b/nn/index.js
index 313a119..0244972 100644
--- a/nn/index.js
+++ b/nn/index.js
@@ -20,7 +20,8 @@ var pooling = require('./networks/pooling.js');
 var activation = require('./networks/activation.js');
 var perceptron = require('./networks/perceptron.js');
 var misc = require('./networks/misc.js');
+var data = require('./data.js');
 
 module.exports = utils.mergeObjects(module.exports,
-	lifting, composition, lifted, linear, convolution, pooling, activation, perceptron, misc
+	lifting, composition, lifted, linear, convolution, pooling, activation, perceptron, misc, data
 );
\ No newline at end of file
diff --git a/opt/optimize.js b/opt/optimize.js
index 350fd11..6a6e9f3 100644
--- a/opt/optimize.js
+++ b/opt/optimize.js
@@ -25,7 +25,7 @@ function optimize(fn, options) {
 		assert(rets.gradients !== undefined);
 		assert(rets.parameters !== undefined);
 		// TODO: assert that there exists a parameter for each gradient?
-		method(rets.gradients, rets.parameters, i);
+		var mrets = method(rets.gradients, rets.parameters, i);
 		if (verbose) {
 			console.log('[optimize] done iteration ' + (i+1) + '/' + iters);
 		}
@@ -78,6 +78,7 @@ function adTrain(fn, trainingData, lossFn, options) {
 		var trainingDatum = trainingData[idx];
 		var rets = fn(trainingDatum.input);
 		var loss = lossFn(rets.output, trainingDatum.output);
+		if (options.verbose) console.log('  loss=%d', loss.x);
 		return {
 			parameters: rets.parameters,
 			loss: loss
diff --git a/test/mlp.js.bak b/test/mlp.js.bak
new file mode 100644
index 0000000..38fabc6
--- /dev/null
+++ b/test/mlp.js.bak
@@ -0,0 +1,44 @@
+var Tensor = require('../tensor');
+var ad = require('../ad');
+var nn = require('../nn');
+var opt = require('../opt');
+
+var nInputs = 6
+var nHidden = [4, 4, 5]
+var nOutput = 2
+
+var net = nn.sequence([
+  nn.linear(nInputs, nHidden[0]),
+  nn.sigmoid,
+  nn.linear(nHidden[0], nHidden[1]),
+  nn.sigmoid,
+  nn.linear(nHidden[1], nOutput),
+  nn.softmax
+])
+
+var data = [
+  {input: [0.4, 0.5, 0.5, 0.,  0.,  0.], output: [1]},
+  {input: [0.5, 0.3, 0.5, 0.,  0.,  0.], output: [1]},
+  {input: [0.4, 0.5, 0.5, 0.,  0.,  0.], output: [1]},
+  {input: [0.,  0.,  0.5, 0.3, 0.5, 0.], output: [0]},
+  {input: [0.,  0.,  0.5, 0.4, 0.5, 0.], output: [0]},
+  {input: [0.,  0.,  0.5, 0.5, 0.5, 0.], output: [0]}
+]
+
+var trainingData = nn.loadData(data)
+
+console.log('nnTrain')
+opt.nnTrain(net, trainingData, opt.classificationLoss, {
+  batchSize: 1, // batch 超過 3 就無法成功， why ?
+  iterations: 1000,
+  method: opt.sgd({ stepSize: 1, stepSizeDecay: 0.999 }),
+  verbose: true
+})
+
+console.log('predict')
+// Predict class probabilities for new, unseen features
+for (let i = 0; i < trainingData.length; i++) {
+  var input = trainingData[i].input
+  var probs = net.eval(input)
+  console.log('input=%j\noutput=%j', input, probs)
+}
diff --git a/test/mlpReg.js b/test/mlpReg.js
new file mode 100644
index 0000000..a2bb309
--- /dev/null
+++ b/test/mlpReg.js
@@ -0,0 +1,43 @@
+var Tensor = require('../tensor');
+var ad = require('../ad');
+var nn = require('../nn');
+var opt = require('../opt');
+
+var nInputs = 6
+var nHidden = [4, 4, 5]
+var nOutput = 2
+
+var net = nn.sequence([
+  nn.linear(nInputs, nHidden[0]),
+  nn.sigmoid,
+  nn.linear(nHidden[0], nHidden[1]),
+  nn.sigmoid,
+  nn.linear(nHidden[1], nOutput),
+])
+
+var data = [
+  {input: [0.4, 0.5, 0.5, 0.,  0.,  0.], output: [1, 0]},
+  {input: [0.5, 0.3, 0.5, 0.,  0.,  0.], output: [1, 0]},
+  {input: [0.4, 0.5, 0.5, 0.,  0.,  0.], output: [1, 0]},
+  {input: [0.,  0.,  0.5, 0.3, 0.5, 0.], output: [0, 1]},
+  {input: [0.,  0.,  0.5, 0.4, 0.5, 0.], output: [0, 1]},
+  {input: [0.,  0.,  0.5, 0.5, 0.5, 0.], output: [0, 1]}
+]
+
+var trainingData = nn.loadData(data)
+
+console.log('nnTrain')
+opt.nnTrain(net, trainingData, opt.regressionLoss, {
+  batchSize: 1, // batch 超過 3 就無法成功， why ?
+  iterations: 1000,
+  method: opt.sgd({ stepSize: 1, stepSizeDecay: 0.999 }),
+  verbose: true
+})
+
+console.log('predict')
+// Predict class probabilities for new, unseen features
+for (let i = 0; i < trainingData.length; i++) {
+  var input = trainingData[i].input
+  var probs = net.eval(input)
+  console.log('input=%j\noutput=%j', input, probs)
+}
diff --git a/test/mlpXor.js b/test/mlpXor.js
new file mode 100644
index 0000000..694ee67
--- /dev/null
+++ b/test/mlpXor.js
@@ -0,0 +1,38 @@
+var Tensor = require('../tensor');
+var ad = require('../ad');
+var nn = require('../nn');
+var opt = require('../opt');
+
+var nInputs = 2
+var nHidden = 5
+var nClasses = 2
+
+var net = nn.sequence([
+  nn.linear(nInputs, nHidden),
+  nn.tanh,
+  nn.linear(nHidden, nClasses),
+  nn.softmax
+])
+
+var data = [{ input: [0, 0], output: [0] },
+            { input: [0, 1], output: [1] },
+            { input: [1, 0], output: [1] },
+            { input: [1, 1], output: [0] }]
+
+var trainingData = nn.loadData(data)
+
+console.log('nnTrain')
+opt.nnTrain(net, trainingData, opt.classificationLoss, {
+  batchSize: 2,
+  iterations: 500,
+  method: opt.sgd({ stepSize: 1, stepSizeDecay: 0.999 }),
+  verbose: true
+})
+
+console.log('predict')
+// Predict class probabilities for new, unseen features
+for (let i = 0; i < trainingData.length; i++) {
+  var input = trainingData[i].input
+  var probs = net.eval(input)
+  console.log('input=%j\nprobs=%j', input, probs)
+}