| | import tensorflow as tf |
| | import pygad |
| | import numpy |
| | from imageMulticlassClassification import ImageMulticlassClassification |
| |
|
| | def fitness_func(solution, solution_idx): |
| | try: |
| | 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] |
| | Dropout2 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] |
| | Batchs = [16, 32, 64, 128, 256, 512, 1024, 2048] |
| | 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"] |
| | 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) |
| | IMC.data_PreprocessingDataset() |
| | customModel = tf.keras.Sequential() |
| | customModel.add(tf.keras.layers.Conv2D(16, (3, 3), input_shape=(imgWidth, imgHeight, 3), activation=usedActivations)) |
| | customModel.add(tf.keras.layers.Conv2D(16, (3, 3), activation=usedActivations)) |
| | customModel.add(tf.keras.layers.Dropout(usedDropout1)) |
| | customModel.add(tf.keras.layers.MaxPooling2D((2, 2))) |
| | customModel.add(tf.keras.layers.Flatten()) |
| | customModel.add(tf.keras.layers.BatchNormalization()) |
| | customModel.add(tf.keras.layers.Dense(usedNeuronDense1, activation=usedActivations)) |
| | 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) |
| | modelName = "" |
| | for x in solution: |
| | modelName += f"{str(x)}_" |
| | 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]) |
| | |
| | |
| | |
| | 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] |
| | desired_output = 5 |
| |
|
| | fitness_function = fitness_func |
| |
|
| | num_generations = 1 |
| | num_parents_mating = 4 |
| |
|
| | sol_per_pop = 10 |
| | num_genes = len(function_inputs) |
| |
|
| | init_range_low = 0 |
| | init_range_high = 8 |
| |
|
| | parent_selection_type = "rws" |
| | keep_parents = 1 |
| |
|
| | crossover_type = "single_point" |
| |
|
| | mutation_type = "swap" |
| | mutation_percent_genes = 'default' |
| |
|
| | ga_instance = pygad.GA(num_generations=num_generations, |
| | num_parents_mating=num_parents_mating, |
| | fitness_func=fitness_function, |
| | sol_per_pop=sol_per_pop, |
| | num_genes=num_genes, |
| | init_range_low=init_range_low, |
| | init_range_high=init_range_high, |
| | parent_selection_type=parent_selection_type, |
| | keep_parents=keep_parents, |
| | crossover_type=crossover_type, |
| | 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, |
| | 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)) |
| |
|
| | |
| | |
| | |
