File size: 5,544 Bytes
d729bee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | 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])
# 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]
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))
# from whatsapp import FWA
# fwa = FWA(APIkey="b2d95af932eedb4de92b3496f338aa5f97b36ae0", NoSender="6285157853522", host="http://wa.fianjulio.web.id:81")
# print(fwa.sendTextMessage(phoneNumber="082136815488", message="Training selesai")) |