Update geneticAlgorithm.py

geneticAlgorithm.py

@@ -3,10 +3,10 @@ import pygad
 import numpy
 from imageMulticlassClassification import ImageMulticlassClassification
 
-def fitness_func(solution, solution_idx):
+def fitness_func(ga_instance, solution, solution_idx):
     try:
-        print("solution_idx :",solution_idx)
-        print("solution :",solution)
+        print("solution_idx :", solution_idx)
+        print("solution :", solution)
         neuronDense1 = [16, 32, 64, 128, 256, 512, 1024, 2048]
         neuronDense2 = [16, 32, 64, 128, 256, 512, 1024, 2048]
         Dropout1 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
@@ -15,30 +15,22 @@ def fitness_func(solution, solution_idx):
         Activations = ["relu", "sigmoid", "softplus", "softsign", "tanh", "selu", "gelu", "linear"]
         Optimizers = ["Adam", "RMSprop", "SGD", "Adadelta", "Adagrad", "Adamax", "Ftrl", "Nadam"]
         LossFunction = ["SparseCategoricalCrossentropy", "CategoricalCrossentropy", "BinaryCrossentropy", "MeanAbsoluteError", "MeanSquaredError", "SquaredHinge", "CategoricalHinge", "CosineSimilarity"]
+        
+        # Use the 'solution' array to access the genes.
         usedNeuronDense1 = neuronDense1[solution[0]]
-        print("==================================")
-        print(f"usedNeuronDense1 : {usedNeuronDense1}")
         usedNeuronDense2 = neuronDense2[solution[1]]
-        print(f"usedNeuronDense2 : {usedNeuronDense2}")
         usedDropout1 = Dropout1[solution[2]]
-        print(f"usedDropout1 : {usedDropout1}")
         usedDropout2 = Dropout2[solution[3]]
-        print(f"usedDropout2 : {usedDropout2}")
         usedBatchs = Batchs[solution[4]]
-        print(f"usedBatchs : {usedBatchs}")
         usedActivations = Activations[solution[5]]
-        print(f"usedActivations : {usedActivations}")
         usedOptimizers = Optimizers[solution[6]]
-        print(f"usedOptimizers : {usedOptimizers}")
         usedLossFunction = LossFunction[solution[7]]
-        print(f"usedLossFunction : {usedLossFunction}")
-        print("==================================")
 
-        imgWidth=50
-        imgHeight=50
-        batchSize=usedBatchs
-        IMC = ImageMulticlassClassification(imgWidth,imgHeight,batchSize)
-        IMC.data_MakeDataset(datasetUrl="https://huggingface.co/datasets/S1223/HandGestureDataset/resolve/main/HandGestureDataset.tgz",datasetDirectoryName="HandGestureDataset", ratioValidation=0.20)
+        imgWidth = 50
+        imgHeight = 50
+        batchSize = usedBatchs
+        IMC = ImageMulticlassClassification(imgWidth, imgHeight, batchSize)
+        IMC.data_MakeDataset(datasetUrl="https://huggingface.co/datasets/S1223/HandGestureDataset/resolve/main/HandGestureDataset.tgz", datasetDirectoryName="HandGestureDataset", ratioValidation=0.20)
         IMC.data_PreprocessingDataset()
         customModel = tf.keras.Sequential()
         customModel.add(tf.keras.layers.Conv2D(16, (3, 3), input_shape=(imgWidth, imgHeight, 3), activation=usedActivations))
@@ -51,28 +43,22 @@ def fitness_func(solution, solution_idx):
         customModel.add(tf.keras.layers.Dense(usedNeuronDense2, activation=usedActivations))
         customModel.add(tf.keras.layers.Dropout(usedDropout2))
         customModel.add(tf.keras.layers.Dense(10, activation="softmax"))
-        IMC.model_make(customModel) 
+        IMC.model_make(customModel)
         modelName = ""
         for x in solution:
             modelName += f"{str(x)}_"
-        IMC.training_model(epochs=10, modelName=modelName, optimizer=usedOptimizers, lossFunction=usedLossFunction)
-        IMC.evaluation(labelName=["0","1","2","3","4","5","6","7","8","9"])
+        IMC.training_model(epochs=50, modelName=modelName, optimizer=usedOptimizers, lossFunction=usedLossFunction)
+        IMC.evaluation(labelName=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"])
         output = float(IMC.history.history["val_accuracy"][-1])
-        # output = numpy.max(solution)
-        # print(output)
-        # print(type(output))
-        print("fitness :",output)
         fitness = output
         return fitness
     except Exception as e:
         print(str(e))
         return 0.00001
 
-function_inputs = [1,2,3,4,5,6,7,8]
+function_inputs = [1, 2, 3, 4, 5, 6, 7, 8]
 desired_output = 5
 
-fitness_function = fitness_func
-
 num_generations = 1
 num_parents_mating = 4
 
@@ -92,7 +78,7 @@ mutation_percent_genes = 'default'
 
 ga_instance = pygad.GA(num_generations=num_generations,
                        num_parents_mating=num_parents_mating,
-                       fitness_func=fitness_function,
+                       fitness_func=fitness_func,
                        sol_per_pop=sol_per_pop,
                        num_genes=num_genes,
                        init_range_low=init_range_low,
@@ -103,12 +89,13 @@ ga_instance = pygad.GA(num_generations=num_generations,
                        mutation_type=mutation_type,
                        mutation_percent_genes=mutation_percent_genes,
                        gene_type=[int, int, int, int, int, int, int, int],
-                       allow_duplicate_genes=False, 
-                       save_best_solutions=False, 
+                       allow_duplicate_genes=False,
+                       save_best_solutions=False,
                        save_solutions=False)
+
 print("Initial Population")
 print(ga_instance.initial_population)
 print(ga_instance.run())
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
 print("Parameters of the best solution : {solution}".format(solution=solution))
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
+print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))