diff --git a/nn.test.js b/nn.test.js
index c5d7e7c..6519caf 100644
--- a/nn.test.js
+++ b/nn.test.js
@@ -1,113 +1,52 @@
 import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
-import { Split, Forward, Backward } from "./nn.ts";
-import { default as M } from "./m.ts";
-import { default as Methods } from "./m.ts";
+import { Split, Build, Label, Learn, Check } from "./nn.ts";
 
-let training = [];
-let stages = [];
+let data = [
+    [ 0.10,  0.05, 0, 1],
+    [ 0.00, -0.06, 0, 1],
+    [ 0.99,  0.85, 1, 0],
+    [ 1.20,  1.05, 1, 0]
+];
+let columns = [2, 3];
+let input, output;
 let layers = [];
 
-let typeA = [
-    [ 0.1,  0.05],
-    [ 0.0, -0.06]
-];
-let typeB = [
-    [ 0.99, 0.85],
-    [ 1.2,  1.05]
-];
-
-
-Deno.test("check forward/backward", ()=>
-{
-    let matrix1 = [
-    [-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
-    [-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
-    [0.738181366836224, 0.3389203747353555, 0.4920200816404332]
-    ];
-
-    let matrix2 = [
-    [0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
-    [-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
-    ];
-
-    let input = [
-    [ 0.1,  0.05],
-    [ 0.0, -0.06],
-    [ 0.99, 0.85],
-    [ 1.2,  1.05]
-    ];
-    let output = [
-    [1, 0],
-    [1, 0],
-    [0, 1],
-    [0, 1]
-    ];
-
-    let layers = [matrix1, matrix2];
-    let stages = [];
-    for(let i=0; i<1000; i++)
-    {
-        stages = Forward(Methods.Mutate.Pad(input), layers);
-        Backward(stages, layers, output, 0.1);
-    }
-
-    stages = Forward(input, layers);
-    console.log(stages[stages.length-1]);
-});
-
 Deno.test("NN.Split", ()=>
 {
-    let data = [
-        [3, 2, 1, 0, 1],
-        [6, 5, 4, 1, 0]
-    ]
-    let split = Split(data, [3, 4]);
-    console.log(split);
+    [input, output] = Split(data, columns);
+    assert(input);
+    assert(output);
+    assertEquals(input.length, output.length, "data split into equal input and output");
+
+    assertEquals(input[0].length, 3, "padded input");
+    assertEquals(output[0].length, 2, "unpadded output");
 });
 
-
-/*
-Deno.test("NN.Label", ()=>
+Deno.test("NN.Build", ()=>
 {
-    Label(training, typeA, [1, 0]);
-    Label(training, typeB, [0, 1]);
-    assertEquals(training.length, 2, "input and output sets created");
-    assertEquals(training[0].length, training[1].length, "both sets have same length");
-    assertEquals(training[0][0].length, 3, "padded input component");
-    assertEquals(training[1][0].length, 2, "unchanged label vector");
+    layers = Build(2, 5, 2);
+
+    assertEquals(layers.length, 2, "correct number of matrices");
+    assertEquals(layers[0][0].length, input[0].length, "input: padded input");
+    assertEquals(layers[0].length, 5, "input: unpadded output");
+
+    assertEquals(layers[1][0].length, 6, "hidden: padded input");
+    assertEquals(layers[1].length, output[0].length, "hidden: unpadded output");
 });
 
-Deno.test("NN.Forward", ()=>
-{
-    let layer1 = M.Create.Box([-1, -1, -1], [1, 1, 1], 2);
-    let layer2 = M.Create.Box([-1, -1, -1], [1, 1, 1], 1);
-    layers.push(layer1);
-    layers.push(layer2);
-
-    console.log(training[0]);
-    stages = Forward(training[0], layers);
-    console.log(stages);
-});
-
-Deno.test("NN.Backward", ()=>
-{
-    let copy1 = M.Create.Clone(layers[0]);
-    let copy2 = M.Create.Clone(layers[1]);
-
-    for(let i=0; i<100; i++)
-    {
-        Backward(stages, layers, training[1], 0.1);
-    }
-
-    assert(layers[0][0][0] != copy1[0][0], "first matrix has changed");
-    assert(layers[1][0][0] != copy2[0][0], "second matrix has changed");
-});
-
-
 Deno.test("NN.Label", ()=>
 {
-    let stages = Forward(training[0], layers);
-    console.log(stages[stages.length-1]);
+    let labels = Label(input, layers);
+    assertEquals(labels.length, output.length);
+    assertEquals(labels[0].length, output[0].length);
 });
 
-*/
\ No newline at end of file
+Deno.test("NN.Learn", ()=>
+{
+    let error = Learn(input, layers, output, 1000, 0.1);
+    assertEquals(error.length, output.length);
+    let total = 0;
+    let count = error.length*error[0].length;
+    error.forEach(row=> row.forEach(component=> total+=Math.abs(component)));
+    assert(total/count < 0.3);
+});
\ No newline at end of file
diff --git a/nn.ts b/nn.ts
index c7922a7..a09e8a1 100644
--- a/nn.ts
+++ b/nn.ts
@@ -38,10 +38,12 @@ const Split = (inTrainingSet:Cloud.M, inHeaderLabel:Cloud.V, inHeaderKeep:Cloud.
     {
         inTrainingSet[0].forEach( (item:number, index:number)=> inHeaderLabel.includes(index) ? false : inHeaderKeep.push(index) );
     }
-    inTrainingSet.forEach((row:Cloud.V) =>
+    inTrainingSet.forEach((row:Cloud.V):void =>
     {
-         data.push( [...inHeaderKeep.map((i:number)=>row[i]), 1] );
-        label.push( inHeaderLabel.map((i:number)=>row[i]) );
+        let vectorData = [ ...inHeaderKeep.map((i:number)=>row[i]), 1];
+        let vectorLabel = inHeaderLabel.map((i:number)=>row[i]) 
+         data.push( vectorData );
+        label.push( vectorLabel );
     });
     return [ data, label ];
 };
@@ -54,7 +56,6 @@ const Build = (...inLayers:Array<number>):N =>
     {
         output.push(rand( inLayers[i]+1, inLayers[i+1]));
     }
-    output.push( rand( inLayers[i-1], inLayers[i]) );
     return output;
 };
 const Label = (inData:Cloud.M, inLayers:N):Cloud.M =>
@@ -72,7 +73,6 @@ const Learn = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M, inIterations:number
     }
     return M.Batch.Subtract(stages[stages.length-1], inLabels);
 };
-const Error = M.Batch.Subtract;
+const Check = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M):Cloud.M => Learn(inData, inLayers, inLabels, 1, 0);
 
-export { Split, Build, Label, Learn, Error, Forward, Backward };
-export type { Cloud };
\ No newline at end of file
+export { Split, Build, Label, Learn, Check, Forward, Backward };
\ No newline at end of file