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changeset 6:ff92c77006c7

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Added support for reading training examples from files.
author revcompgeek
date Tue, 06 May 2008 21:43:55 -0600
parents 810d58835f86
children b9fe92a2d8ad
files trunk/aid/astar.d trunk/aid/nn/multilayer/backprop.d trunk/aid/nn/outputFunctions.d trunk/aid/nn/perceptron.d trunk/backprop_test.d trunk/dsss.conf trunk/trainingexamples/binaryadd4.trnex trunk/trainingexamples/identity.trnex trunk/trainingexamples/parody2.trnex trunk/trainingexamples/parody3.trnex
diffstat 10 files changed, 216 insertions(+), 27 deletions(-) [+]
line wrap: on
line diff
--- a/trunk/aid/astar.d	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/aid/astar.d	Tue May 06 21:43:55 2008 -0600
@@ -7,6 +7,7 @@
 
 import mintl.arrayheap;
 import mintl.arraylist;
+import xenon.font;
 
 class Node(DATA) {
     int xloc;
--- a/trunk/aid/nn/multilayer/backprop.d	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/aid/nn/multilayer/backprop.d	Tue May 06 21:43:55 2008 -0600
@@ -1,3 +1,7 @@
+/**
+ * backprop.d
+ * Holds the backpropagation neural network.
+ */
 
 module aid.nn.multilevel.backprop;
 
--- a/trunk/aid/nn/outputFunctions.d	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/aid/nn/outputFunctions.d	Tue May 06 21:43:55 2008 -0600
@@ -1,3 +1,8 @@
+/**
+ * outputFunctions.d
+ * Holds all of the output functions used by the neural networks.
+ */
+
 module aid.nn.outputFunctions;
 
 import aid.nn.outputFunctions;
--- a/trunk/aid/nn/perceptron.d	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/aid/nn/perceptron.d	Tue May 06 21:43:55 2008 -0600
@@ -1,3 +1,8 @@
+/**
+ * perceptron.d
+ * Holds the simple perceptron class.
+ */
+
 module aid.nn.perceptron;
 
 import aid.nn.outputFunctions;
--- a/trunk/backprop_test.d	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/backprop_test.d	Tue May 06 21:43:55 2008 -0600
@@ -1,19 +1,110 @@
-
 module backprop_test;
 
-
 import aid.nn.multilayer.backprop;
 import aid.nn.outputFunctions;
 import aid.misc;
 import std.stdio;
 import std.random;
 import std.conv;
+import std.string;
+import std.stream;
 
-double[][] trainingInputs, trainingOutputs;
-uint numInputs;
+//double[][] trainingInputs, trainingOutputs;
+//uint numInputs;
 uint[] outputsArray;
 
-void initTrainingExample(int example) {
+struct TrainingExample {
+    char[] name;
+    uint numInputs,numOutputs;
+    uint[] numHidden;
+    double[][] inputs,outputs;
+}
+
+/// Reads the training example from a file
+TrainingExample readTrainingExample(InputStream f){
+    TrainingExample e;
+    char[][] temp,trnInputs,trnOutputs;
+    char[] line;
+    bool gotInputs,gotOutputs,gotHidden;
+    
+    int i;
+    
+    line = f.readLine();
+    temp = split(line);
+    if(tolower(temp[0]) != "name:") throw new Error("Expecting \"Name:\" on first line.");
+    e.name = join(temp[1..$]," ");
+    
+    bool stop = false;
+    while(!stop){
+        if(f.eof) throw new Error("Unexpected end of file.");
+        line = strip(tolower(f.readLine()));
+        temp = split(line);
+        if(temp.length == 0) continue;
+        switch(temp[0]){
+        case "inputs:":
+            if(gotInputs) throw new Error("Only one inputs line allowed.");
+            e.numInputs = toInt(temp[1]);
+            if(e.numInputs < 1) throw new Error("Inputs cannot be less than 1.");
+            gotInputs = true;
+            break;
+        case "outputs:":
+            if(gotOutputs) throw new Error("Only one outputs line allowed.");
+            e.numOutputs = toInt(temp[1]);
+            if(e.numOutputs < 1) throw new Error("Outputs cannot be less than 1.");
+            gotOutputs = true;
+            break;
+        case "hidden:":
+            if(gotHidden) throw new Error("Only one hidden line allowed.");
+            if(temp.length == 1) break;
+            temp = split(join(temp[1..$],""),",");
+            e.numHidden.length = temp.length;
+            for(i = 0; i<temp.length; i++){
+                e.numHidden[i] = toInt(temp[i]);
+                if(e.numHidden[i] < 1) throw new Error("Hidden layers cannot be less than 1.");
+            }
+            gotHidden = true;
+            break;
+        case "data:":
+            stop = true;
+            break;
+        default:
+            throw new Error(temp[0] ~ " not recognized.");
+        }
+    }
+    
+    if(!gotInputs) throw new Error("Missing inputs line.");
+    if(!gotOutputs) throw new Error("Missing outputs line.");
+    //if(!gotHidden) throw new Error("Missing hidden line."); // Hidden line not required
+    
+    //Data reading
+    e.inputs.length = 10; // This isn't as redundant as it looks.
+    e.inputs.length = 0;
+    e.outputs.length = 10;
+    e.outputs.length = 0;
+    while(!f.eof){
+        line = join(split(strip(f.readLine())),""); // remove whitespace
+        temp = split(line,";");
+        if(line.length == 0) continue; // blank line
+        if(temp.length != 2) throw new Error("Wrong number of semicolons in data.");
+        
+        trnInputs = split(temp[0],",");
+        trnOutputs = split(temp[1],",");
+        if(trnInputs.length != e.numInputs) throw new Error("Expecting " ~ toString(e.numInputs) ~ " inputs, not " ~ toString(trnInputs.length));
+        if(trnOutputs.length != e.numOutputs) throw new Error("Expecting " ~ toString(e.numOutputs) ~ " outputs, not " ~ toString(trnOutputs.length));
+        e.inputs.length = e.inputs.length + 1;
+        e.inputs[$-1].length = e.numInputs;
+        e.outputs.length = e.outputs.length + 1;
+        e.outputs[$-1].length = e.numOutputs;
+        for(i = 0; i<e.numInputs; i++)
+            e.inputs[$-1][i] = toDouble(trnInputs[i]);
+        for(i = 0; i<e.numOutputs; i++)
+            e.outputs[$-1][i] = toDouble(trnOutputs[i]);
+    }
+    
+    return e;
+}
+
+/*void initTrainingExample(int example) {
 	if(example == 0) {
 		numInputs = 3;
 		outputsArray = [2,1];
@@ -61,16 +152,18 @@
 		
 		trainingOutputs = trainingInputs;
 	}
-}
+}*/
 
 void main(char[][] args) {
 	double learningRate = 0.2, momentum = 0.3, randomSize = 0.1, errorMin = 0.05;
-	int trainingExample = 0, maxIters = 10000; // 0 to 2
-	bool quiet = false; // don't print output each time
+	int /*trainingExample = 0,*/ maxIters = 10000; // 0 to 2
+	bool quiet,printFull,printWeights;
 	int printEvery = 500; // output every ~ times
+    TrainingExample example;
 	
-	//try {
-	for(int i = 1; i < args.length; i++) {
+	char[] filename = args[1];
+    example = readTrainingExample(new File(filename));
+	for(int i = 2; i < args.length; i++) {
 		switch(args[i]) {
 			case "-s":
 			case "--seed":
@@ -78,8 +171,6 @@
 				break;
 			case "-l":
 			case "--learning-rate":
-				//if(args.length = i + 1)
-					//throw new Error("Wrong number of paramaters");
 				learningRate = toDouble(args[++i]);
 				break;
 			case "-m":
@@ -94,7 +185,7 @@
 			case "--error-min":
 				errorMin = toDouble(args[++i]);
 				break;
-			case "-n":
+			/*case "-n":
 			case "--example-number":
 				trainingExample = toInt(args[++i]);
 				if(trainingExample > 2 || trainingExample < 0)
@@ -114,7 +205,7 @@
 					default:
 						throw new Error("Wrong example name. Must be parity, xor or identity");
 				}
-				break;
+				break;*/
 			case "-q":
 			case "--quiet":
 				quiet = true;
@@ -128,6 +219,15 @@
 			case "--min-iterations":
 				maxIters = toInt(args[++i]);
 				break;
+            case "-f":
+            case "--full":
+            case "--print full":
+                printFull = true;
+                break;
+            case "-w":
+            case "--print-weights":
+                printWeights = true;
+                break;
 			default:
 				throw new Error("Unknown switch: " ~ args[i]);
 		}
@@ -136,11 +236,19 @@
 	//	throw new Error("Wrong number of paramaters");
 	//}
 	
-	initTrainingExample(trainingExample);
+	//initTrainingExample(trainingExample);
+	
+	writefln("Starting training with: " ~ example.name);
 	
-	Backprop nn = new Backprop(numInputs, outputsArray, [&sigmoid, &sigmoid], learningRate, momentum, randomSize, true);
+	OutputFunctionPtr[] functions;
+    functions.length = example.numHidden.length + 1;
+    for(int i = 0; i<functions.length; i++){
+        functions[i] = &sigmoid;
+    }
 	
-	double error = nn.calculateError(trainingInputs, trainingOutputs);
+	Backprop nn = new Backprop(example.numInputs, example.numHidden ~ example.numOutputs, functions, learningRate, momentum, randomSize, true);
+	
+	double error = nn.calculateError(example.inputs, example.outputs);
 	double[] output;
 	int iter = 0;
 	//writef("weights=");
@@ -151,27 +259,29 @@
 		if(iter % printEvery == 0) {
 			writefln("Iter: %d", iter);
 			if(!quiet) {
-				for(int i = 0; i < trainingInputs.length; i++) {
-					output = nn.evaluate(trainingInputs[i]);
+				for(int i = 0; i < example.inputs.length; i++) {
+					output = nn.evaluate(example.inputs[i]);
 					writef("  %d:", i);
 					printArray(output);
 				}
 			}
 			writefln("  Error: %f", error);
 		}
-		nn.train(trainingInputs, trainingOutputs, true);
-		error = nn.calculateError(trainingInputs, trainingOutputs);
+		nn.train(example.inputs, example.outputs, true);
+		error = nn.calculateError(example.inputs, example.outputs);
 		iter++;
 	}
 	writefln("Total Iters: %d", iter);
-	for(int i = 0; i < trainingInputs.length; i++) {
+	for(int i = 0; i < example.inputs.length; i++) {
 		writef("  %d:", i);
-		if(trainingExample == 2)
-			printArray(nn.evaluateFull(trainingInputs[i])[0]);
+		if(printFull)
+			printArray(nn.evaluateFull(example.inputs[i])[0]);
 		else
-			printArray(nn.evaluate(trainingInputs[i]));
+			printArray(nn.evaluate(example.inputs[i]));
 	}
 	writefln("  Error: %f", error);
-	writef("weights=");
-	printArray(nn.getWeights());
+	if(printWeights){
+        writef("weights=");
+        printArray(nn.getWeights());
+    }
 }
--- a/trunk/dsss.conf	Tue Apr 15 14:39:49 2008 -0600
+++ b/trunk/dsss.conf	Tue May 06 21:43:55 2008 -0600
@@ -3,7 +3,12 @@
 exclude=aid/maze/
 exclude+=aid/containers/
 [ga_code.d]
+noinstall
 [ga_maze.d]
+noinstall
 [mazegen.d]
+noinstall
 [perceptron_test.d]
+noinstall
 [backprop_test.d]
+noinstall
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/trainingexamples/binaryadd4.trnex	Tue May 06 21:43:55 2008 -0600
@@ -0,0 +1,24 @@
+Name: Binary Addition 2,2
+Inputs: 4
+Outputs: 3
+Hidden: 8
+Data:
+0,0,  0,0;  0.1,0.1,0.1
+0,1,  0,0;  0.1,0.1,0.9
+1,0,  0,0;  0.1,0.9,0.1
+1,1,  0,0;  0.1,0.9,0.9
+
+0,0,  0,1;  0.1,0.1,0.9
+0,1,  0,1;  0.1,0.9,0.1
+1,0,  0,1;  0.1,0.9,0.9
+1,1,  0,1;  0.9,0.1,0.1
+
+0,0,  1,0;  0.1,0.9,0.1
+0,1,  1,0;  0.1,0.9,0.9
+1,0,  1,0;  0.9,0.1,0.9
+1,1,  1,0;  0.9,0.9,0.1
+
+0,0,  1,1;  0.1,0.9,0.9
+0,1,  1,1;  0.9,0.1,0.1
+1,0,  1,1;  0.9,0.9,0.1
+1,1,  1,1;  0.9,0.9,0.9
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/trainingexamples/identity.trnex	Tue May 06 21:43:55 2008 -0600
@@ -0,0 +1,13 @@
+Name: Identity-Hidden representation
+Inputs: 8
+Outputs: 8
+Hidden: 3
+Data:
+0.9, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1;  0.9, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
+0.1, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1;  0.1, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1
+0.1, 0.1, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1;  0.1, 0.1, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1
+0.1, 0.1, 0.1, 0.9, 0.1, 0.1, 0.1, 0.1;  0.1, 0.1, 0.1, 0.9, 0.1, 0.1, 0.1, 0.1
+0.1, 0.1, 0.1, 0.1, 0.9, 0.1, 0.1, 0.1;  0.1, 0.1, 0.1, 0.1, 0.9, 0.1, 0.1, 0.1
+0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.1, 0.1;  0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.1, 0.1
+0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.1;  0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.1
+0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.9;  0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.9
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/trainingexamples/parody2.trnex	Tue May 06 21:43:55 2008 -0600
@@ -0,0 +1,9 @@
+Name: Parody 2
+Inputs: 2
+Outputs: 1
+Hidden: 2
+Data:
+0, 0;  0.1
+0, 1;  0.9
+1, 0;  0.9
+1, 1;  0.1
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trunk/trainingexamples/parody3.trnex	Tue May 06 21:43:55 2008 -0600
@@ -0,0 +1,13 @@
+Name: Parody 3
+Inputs: 3
+Outputs: 1
+Hidden: 2
+Data:
+0, 0, 0;  0.1
+0, 0, 1;  0.9
+0, 1, 0;  0.9
+0, 1, 1;  0.1
+1, 0, 0;  0.9
+1, 0, 1;  0.1
+1, 1, 0;  0.1
+1, 1, 1;  0.9
\ No newline at end of file