def main():
batch_size = 32 # the number of training examples in one forward/backward pass
num_classes = 10 # number of cifar-10 dataset classes
epochs = 3 # number of forward and backward passes of all the training examples
'''
dataset contains the hyper parameters for loading data and the dataset:
dataset = {
'batch_size': batch_size,
'num_classes': num_classes,
'epochs': epochs,
'x_train': x_train,
'x_test': x_test,
'y_train': y_train,
'y_test': y_test
}
'''
dataset = load_dataset(batch_size, num_classes, epochs)
num_population = 4
num_generation = 4
num_offspring = 2
# plot the best model obtained
optCNN = genetic_algorithm(num_population, num_generation, num_offspring,
dataset)
# plot the training and validation loss and accuracy
num_epoch = 3
model = optCNN.build_model()
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(dataset['x_train'],
dataset['y_train'],
batch_size=dataset['batch_size'],
epochs=num_epoch,
validation_data=(dataset['x_test'], dataset['y_test']),
shuffle=True)
optCNN.model = model # model
optCNN.fitness = history.history['val_loss'][-1] # fitness
print("\n\n-------------------------------------")
print("The initial CNN has been evolved successfully in the individual",
optCNN.name)
print("-------------------------------------\n")
daddy = load_network('parent_0')
model = tf.keras.models.load_model('parent_0.h5')
print("\n\n-------------------------------------")
print("Summary of initial CNN")
print(model.summary())
print("Fitness of initial CNN:", daddy.fitness)
print("\n\n-------------------------------------")
print("Summary of evolved individual")
print(optCNN.model.summary())
print("Fitness of the evolved individual:", optCNN.fitness)
print("-------------------------------------\n")
plot_training(history)
def asexual_reproduction(self, it, dataset):
# if the individual already exists, just load it
if os.path.isfile('net_' + str(it) + '.h5'):
print("\n-------------------------------------")
print("Loading individual net_" + str(it))
print("--------------------------------------\n")
individual = load_network('net_' + str(it))
model = tf.keras.models.load_model(individual.name + '.h5')
print("SUMMARY OF", individual.name)
print(model.summary())
print("FITNESS: ", individual.fitness)
return individual
# otherwise, create the individual by mutating the parent
individual = Network(it)
print("\n-------------------------------------")
print("\nCreating individual", individual.name)
print("--------------------------------------\n")
individual.block_list = deepcopy(
self.block_list) # copy the layer list from parent
print("----->Strong Mutation")
individual.block_mutation(dataset) # mutate a block
individual.layer_mutation(dataset) # mutate a layer
individual.parameters_mutation() # mutate some parameters
model = individual.build_model()
if model == -1:
return self.asexual_reproduction(it, dataset)
individual.train_and_evaluate(model, dataset)
return individual
def asexual_reproduction(self, it, dataset):
# 如果个体存在就读取它
if os.path.isfile('net_' + str(it) + '.h5'):
print("\n-------------------------------------")
print("Loading individual net_" + str(it))
print("--------------------------------------\n")
individual = load_network('net_' + str(it))
model = tf.keras.models.load_model(individual.name + '.h5')
print("SUMMARY OF", individual.name)
print(model.summary())
print("FITNESS: ", individual.fitness)
return individual
# 否则通过变异父代来产生个体
individual = Network(it)
print("\n-------------------------------------")
print("\nCreating individual", individual.name)
print("--------------------------------------\n")
individual.block_list = deepcopy(self.block_list)
print("----->Strong Mutation")
individual.block_mutation(dataset) # 块变异
individual.layer_mutation(dataset) # 层变异
individual.parameters_mutation() # 参数变异
model = individual.build_model()
if model == -1:
return self.asexual_reproduction(it, dataset)
individual.train_and_evaluate(model, dataset)
return individual
def compute_parent(dataset):
if os.path.isfile('parent_0.h5'):
daddy = load_network('parent_0')
model = tf.keras.models.load_model('parent_0.h5')
print("Loading parent_0")
print("SUMMARY OF", daddy.name)
print(model.summary())
print("FITNESS:", daddy.fitness)
return daddy
daddy = Network(0)
layerList1 = [
Convolutional(filters=32,
filter_size=(3, 3),
stride_size=(1, 1),
padding='same',
input_shape=dataset['x_train'].shape[1:]),
Convolutional(filters=32,
filter_size=(3, 3),
stride_size=(1, 1),
padding='valid',
input_shape=dataset['x_train'].shape[1:])
]
layerList2 = [
Pooling(pool_size=(2, 2), stride_size=(2, 2), padding='same')
]
daddy.block_list.append(Block(0, 0, layerList1, layerList2))
'''
layerList1 = [
Convolutional(filters=64, filter_size=(3, 3), stride_size=(1, 1), padding='same',
input_shape=dataset['x_train'].shape[1:]),
Convolutional(filters=64, filter_size=(3, 3), stride_size=(1, 1), padding='valid',
input_shape=dataset['x_train'].shape[1:])
]
layerList2 = [
Pooling(pool_size=(2, 2), stride_size=(2, 2), padding='same'),
Dropout(rate=0.25)
]
daddy.block_list.append(Block(1, 1, layerList1, layerList2))
layerList1 = [
Convolutional(filters=128, filter_size=(3, 3), stride_size=(1, 1), padding='same',
input_shape=dataset['x_train'].shape[1:]),
Convolutional(filters=128, filter_size=(3, 3), stride_size=(1, 1), padding='valid',
input_shape=dataset['x_train'].shape[1:])
]
layerList2 = [
Pooling(pool_size=(2, 2), stride_size=(2, 2), padding='same'),
Dropout(rate=0.25)
]
daddy.block_list.append(Block(1, 1, layerList1, layerList2))
'''
layerList1 = [
FullyConnected(units=128, num_classes=dataset['num_classes'])
]
layerList2 = []
daddy.block_list.append(Block(2, 1, layerList1, layerList2))
model = daddy.build_model()
daddy.train_and_evaluate(model, dataset)
return daddy
from model.network import network
from utilities import save_network, load_network
from networkx import to_numpy_array
net = network(10)
print('net gen')
tmp_net = to_numpy_array(net.network_plot)
print(tmp_net)
save_network(net)
print('save done')
net_cpy = load_network()
print(to_numpy_array(net_cpy))
print('load done')