def init_search(self):
if self.verbose:
print('Initializing search.')
graph = CnnGenerator(self.n_classes, self.input_shape).generate(
self.default_model_len, self.default_model_width)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
for child_graph in default_transform(graph):
child_id = self.model_count
self.model_count += 1
self.training_queue.append((child_graph, model_id, child_id))
self.descriptors.append(child_graph.extract_descriptor())
if self.verbose:
print('Initialization finished.')
def init_search(self):
if self.verbose:
print('\nInitializing search.')
graph = CnnGenerator(self.n_classes,
self.input_shape).generate(self.default_model_len,
self.default_model_width)
model_id = self.model_count
self.model_count += 1
self.training_queue.append((graph, -1, model_id))
self.descriptors.append(graph.extract_descriptor())
for child_graph in default_transform(graph):
child_id = self.model_count
self.model_count += 1
self.training_queue.append((child_graph, model_id, child_id))
self.descriptors.append(child_graph.extract_descriptor())
if self.verbose:
print('Initialization finished.')
from keras.datasets import cifar10
from autokeras.generator import DefaultClassifierGenerator
from autokeras.net_transformer import default_transform
from autokeras.preprocessor import OneHotEncoder
from autokeras.utils import ModelTrainer
if __name__ == '__main__':
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('Start Encoding')
encoder = OneHotEncoder()
encoder.fit(y_train)
y_train = encoder.transform(y_train)
y_test = encoder.transform(y_test)
print('Start Generating')
graphs = default_transform(
DefaultClassifierGenerator(10, x_train.shape[1:]).generate())
keras_model = graphs[0].produce_model()
print('Start Training')
ModelTrainer(keras_model, x_train, y_train, x_test, y_test,
True).train_model(max_no_improvement_num=100, batch_size=128)
print(keras_model.evaluate(x_test, y_test, True))
