added spring boot logging for errors

spring.log

@@ -0,0 +1 @@
+2024-11-13T16:03:24.015+01:00 ERROR 16956 --- [guessNumbersWithAI] [http-nio-8080-exec-3] c.g.controller.ViewController            : src\main\resources\net_params_size784_256_10_lr0.001_trainEps1002.txt (The system cannot find the file specified)

src/main/java/com/guessNumbersWithAI/controller/ViewController.java

@@ -2,17 +2,22 @@
 
 import com.guessNumbersWithAI.model.DrawnImages;
 import com.guessNumbersWithAI.model.NeuralNetwork;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 import org.springframework.stereotype.Controller;
 import org.springframework.ui.Model;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.ModelAttribute;
 import org.springframework.web.bind.annotation.PostMapping;
 
+import java.io.FileWriter;
 import java.util.ArrayList;
 
 @Controller
 public class ViewController {
 
+    Logger logger = LoggerFactory.getLogger(ViewController.class);
+
     @GetMapping("/write-number")
     public String hangleGetMapping(Model theModel){
 
@@ -29,31 +34,26 @@ public String hangleGetMapping(Model theModel){
     public String hanglePostMapping(@ModelAttribute("NeuralNetwork") NeuralNetwork ourNeuralNetwork,
                                     @ModelAttribute("DrawnImages") DrawnImages drawnImages){
 
-        // load network parameters from the file
-        ourNeuralNetwork.loadNetworkParameters();
+        try{
+            // load network parameters from the file
+            ourNeuralNetwork.loadNetworkParameters();
 
-        // create input vector by processing raw pixel image data; we turn 280x280 px image into 28x28 px image
-        double[] inputVector = drawnImages.processRawInput();
+            // create input vector by processing raw pixel image data; we turn 280x280 px image into 28x28 px image
+            double[] inputVector = drawnImages.processRawInput();
 
-        // pass input vector to network and get the prediction; the answer will be displayed on the html view page
-        ourNeuralNetwork.forward(inputVector);
-        System.out.println("Answer: " + ourNeuralNetwork.getAnswer());
+            // pass input vector to network and get the prediction; the answer will be displayed on the html view page
+            ourNeuralNetwork.forward(inputVector);
+            System.out.println("Answer: " + ourNeuralNetwork.getAnswer());
+
+            // save image to the H2 database for future neural networks training
 
-        // save image to the H2 database for future neural networks training
-        try{
             drawnImages.saveImageToDB();
-        }catch(Exception e){
-            System.out.println(e.getMessage());
-        }
 
-        // list all saved images; uncomment to view DB content in sdout
-        /*try{
-            System.out.println("Our Image database:");
-            ArrayList<String> storedImages = drawnImages.readAllImages();
-            for(String image: storedImages) System.out.println(image);
         }catch(Exception e){
+            logger.error(e.getMessage());
             System.out.println(e.getMessage());
-        }*/
+
+        }
 
         return "main-view";
     }

src/main/java/com/guessNumbersWithAI/model/NeuralNetwork.java

@@ -47,7 +47,7 @@ public NeuralNetwork() {
 
     // Forward propagation: the neural network gets user image input, that is converted to the input vector
     // and returns as an output the probabilities for different numbers
-    public void forward(double[] input){
+    public void forward(double[] input) throws RuntimeException{
         // forward propagation is not that hard, just do the following:
         // 1. Take matrix product and add biases [z] = [firstLayerWeights]*[input] + [firstLayerBiases];
         // 2. Obtain hidden activation values [y] by applying to [z] some activation function f([z]), i.e., mapping.
@@ -99,71 +99,65 @@ public void forward(double[] input){
 
 
     // loads the parameters of the chosen network
-    public void loadNetworkParameters(){
-
-        try{
-
-            // specify filename for the model that user selected
-            String filename = "src/main/resources/net_params_size784_256_10_lr0.001_trainEps100.txt";
-            // working on it
-            /*if(chosenNetworkModel.equals("PyTorch")){
-                filename = "src/main/resources/PyTorch_params.txt";
-            }else if(chosenNetworkModel.equals("MyModel")){
-                filename = "src/main/resources/net_params_size784_256_10_lr0.001_trainEps100.txt";
-            }*/
-
-            // load the file
-            File networkParamsFile = new File(filename);
-            Scanner reader = new Scanner(networkParamsFile);
-
-            // read the file data
-            while(reader.hasNextLine()){
-
-                // skip metadata
-                for(int skip = 0; skip < 4; skip++) reader.nextLine();
-
-                // load first layer weights
-                String firstLayerWeightsStr = reader.nextLine();
-                String[] firstLrWeightsStr = firstLayerWeightsStr.split(",");
-                for(int i = 0; i < hiddenSize; i++){
-                    for(int j = 0; j < inputSize; j++) {
-                        int index = i*inputSize + j;
-                        this.firstLayerWeights[i][j] = Double.valueOf(firstLrWeightsStr[index]);
-                    }
-                }
-
-                // load first layer biases
-                for(int skip = 0; skip < 2; skip++) reader.nextLine();
-                String firstLayerBiasesStr = reader.nextLine();
-                String[] firstLrBiasesStr = firstLayerBiasesStr.split(",");
-                for(int i = 0; i < hiddenSize; i++){
-                    firstLayerBiases[i] = Double.valueOf(firstLrBiasesStr[i]);
+    public void loadNetworkParameters() throws Exception{
+
+        // specify filename for the model that user selected
+        String filename = "src/main/resources/net_params_size784_256_10_lr0.001_trainEps100.txt";
+        // working on it
+        /*if(chosenNetworkModel.equals("PyTorch")){
+            filename = "src/main/resources/PyTorch_params.txt";
+        }else if(chosenNetworkModel.equals("MyModel")){
+            filename = "src/main/resources/net_params_size784_256_10_lr0.001_trainEps100.txt";
+        }*/
+
+        // load the file
+        File networkParamsFile = new File(filename);
+        Scanner reader = new Scanner(networkParamsFile);
+
+        // read the file data
+        while(reader.hasNextLine()){
+
+            // skip metadata
+            for(int skip = 0; skip < 4; skip++) reader.nextLine();
+
+            // load first layer weights
+            String firstLayerWeightsStr = reader.nextLine();
+            String[] firstLrWeightsStr = firstLayerWeightsStr.split(",");
+            for(int i = 0; i < hiddenSize; i++){
+                for(int j = 0; j < inputSize; j++) {
+                    int index = i*inputSize + j;
+                    this.firstLayerWeights[i][j] = Double.valueOf(firstLrWeightsStr[index]);
                 }
+            }
 
-                // load second layer weights
-                for(int skip = 0; skip < 2; skip++) reader.nextLine();
-                String secondLayerWeightsStr = reader.nextLine();
-                String[] secondLrWeightsStr = secondLayerWeightsStr.split(",");
-                for(int i = 0; i < outputSize; i++){
-                    for(int j = 0; j < hiddenSize; j++) {
-                        int index = i*hiddenSize + j;
-                        this.secondLayerWeights[i][j] = Double.valueOf(secondLrWeightsStr[index]);
-                    }
-                }
+            // load first layer biases
+            for(int skip = 0; skip < 2; skip++) reader.nextLine();
+            String firstLayerBiasesStr = reader.nextLine();
+            String[] firstLrBiasesStr = firstLayerBiasesStr.split(",");
+            for(int i = 0; i < hiddenSize; i++){
+                firstLayerBiases[i] = Double.valueOf(firstLrBiasesStr[i]);
+            }
 
-                // load first layer biases
-                for(int skip = 0; skip < 2; skip++) reader.nextLine();
-                String secondLayerBiasesStr = reader.nextLine();
-                String[] secondLrBiasesStr = secondLayerBiasesStr.split(",");
-                for(int i = 0; i < outputSize; i++){
-                    secondLayerBiases[i] = Double.valueOf(secondLrBiasesStr[i]);
+            // load second layer weights
+            for(int skip = 0; skip < 2; skip++) reader.nextLine();
+            String secondLayerWeightsStr = reader.nextLine();
+            String[] secondLrWeightsStr = secondLayerWeightsStr.split(",");
+            for(int i = 0; i < outputSize; i++){
+                for(int j = 0; j < hiddenSize; j++) {
+                    int index = i*hiddenSize + j;
+                    this.secondLayerWeights[i][j] = Double.valueOf(secondLrWeightsStr[index]);
                 }
             }
-            reader.close();
 
-        }catch(Exception e){
-            System.out.println(e.getMessage());
+            // load first layer biases
+            for(int skip = 0; skip < 2; skip++) reader.nextLine();
+            String secondLayerBiasesStr = reader.nextLine();
+            String[] secondLrBiasesStr = secondLayerBiasesStr.split(",");
+            for(int i = 0; i < outputSize; i++){
+                secondLayerBiases[i] = Double.valueOf(secondLrBiasesStr[i]);
+            }
         }
+        reader.close();
     }
 
     // getters and setters

src/main/resources/application.properties

@@ -1,5 +1,7 @@
 spring.application.name=guessNumbersWithAI
 
 spring.main.banner-mode=off
-logging.level.root=WARN
+
+logging.file.path=.
+logging.level.root=ERROR
 logging.level.org.hibernate=ERROR

src/test/java/com/guessNumbersWithAI/GuessNumbersWithAiApplicationTests.java

@@ -10,4 +10,6 @@ class GuessNumbersWithAiApplicationTests {
 	void contextLoads() {
 	}
 
+    // test to check if the new image has been added to DB
+
 }