def add_model(self, model, x_train, y_train, x_test, y_test):
"""add one model while will be trained to history list"""
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(),
metrics=['accuracy'])
if self.verbose:
model.summary()
ModelTrainer(model, x_train, y_train, x_test, y_test,
self.verbose).train_model()
loss, accuracy = model.evaluate(x_test, y_test, verbose=self.verbose)
model.save(os.path.join(self.path, str(self.model_count) + '.h5'))
self.history.append({
'model_id': self.model_count,
'loss': loss,
'accuracy': accuracy
})
self.history_configs.append(extract_config(model))
self.model_count += 1
pickle.dump(self, open(os.path.join(self.path, 'searcher'), 'wb'))
def add_model(self, model, x_train, y_train, x_test, y_test):
"""add one model while will be trained to history list
Returns:
History object.
"""
loss, accuracy = ModelTrainer(model, x_train, y_train, x_test, y_test,
False).train_model(**self.trainer_args)
accuracy += 0.005 * len(
Graph(model, False).extract_descriptor().skip_connections)
accuracy = min(accuracy, 1)
model.save(os.path.join(self.path, str(self.model_count) + '.h5'))
plot_model(model,
to_file=os.path.join(self.path,
str(self.model_count) + '.png'),
show_shapes=True)
model_id = self.model_count
ret = {'model_id': model_id, 'loss': loss, 'accuracy': accuracy}
self.history.append(ret)
self.history_configs.append(extract_config(model))
self.model_count += 1
self.descriptors[Graph(model, False).extract_descriptor()] = True
# Update best_model text file
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, 'best_model.txt'), 'w')
file.write('best model: ' + str(model_id))
file.close()
if self.verbose:
print('Model ID:', model_id)
print('Loss:', loss)
print('Accuracy', accuracy)
return ret
def run():
# Training parameters
batch_size = 32 # orig paper trained all networks with batch_size=128
epochs = 200
data_augmentation = True
num_classes = 10
# Subtracting pixel mean improves accuracy
subtract_pixel_mean = True
# Model parameter
# ----------------------------------------------------------------------------
# | | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
# Model | n | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
# |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
# ----------------------------------------------------------------------------
# ResNet20 | 3 (2)| 92.16 | 91.25 | ----- | ----- | 35 (---)
# ResNet32 | 5(NA)| 92.46 | 92.49 | NA | NA | 50 ( NA)
# ResNet44 | 7(NA)| 92.50 | 92.83 | NA | NA | 70 ( NA)
# ResNet56 | 9 (6)| 92.71 | 93.03 | 93.01 | NA | 90 (100)
# ResNet110 |18(12)| 92.65 | 93.39+-.16| 93.15 | 93.63 | 165(180)
# ResNet164 |27(18)| ----- | 94.07 | ----- | 94.54 | ---(---)
# ResNet1001| (111)| ----- | 92.39 | ----- | 95.08+-.14| ---(---)
# ---------------------------------------------------------------------------
n = 3
# Model version
# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
version = 1
# Computed depth from supplied model parameter n
if version == 1:
depth = n * 6 + 2
elif version == 2:
depth = n * 9 + 2
# Model name, depth and version
model_type = 'ResNet%dv%d' % (depth, version)
# Load the CIFAR10 data.
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# Input image dimensions.
input_shape = x_train.shape[1:]
# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_train_mean = np.mean(x_train, axis=0)
x_train -= x_train_mean
x_test -= x_train_mean
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
print('y_train shape:', y_train.shape)
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
if version == 2:
model = resnet_v2(input_shape=input_shape, depth=depth)
else:
model = resnet_v1(input_shape=input_shape, depth=depth)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=lr_schedule(0)),
metrics=['accuracy'])
model.summary()
print(model_type)
# Prepare model model saving directory.
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)
# Prepare callbacks for model saving and for learning rate adjustment.
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_acc',
verbose=1,
save_best_only=True)
lr_scheduler = LearningRateScheduler(lr_schedule)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
callbacks = [checkpoint, lr_reducer, lr_scheduler]
# # Run training, with or without data augmentation.
# if not data_augmentation:
# print('Not using data augmentation.')
# model.fit(x_train, y_train,
# batch_size=batch_size,
# epochs=epochs,
# validation_data=(x_test, y_test),
# shuffle=True,
# callbacks=callbacks)
# else:
# print('Using real-time data augmentation.')
# # This will do preprocessing and realtime data augmentation:
# datagen = ImageDataGenerator(
# # set input mean to 0 over the dataset
# featurewise_center=False,
# # set each sample mean to 0
# samplewise_center=False,
# # divide inputs by std of dataset
# featurewise_std_normalization=False,
# # divide each input by its std
# samplewise_std_normalization=False,
# # apply ZCA whitening
# zca_whitening=False,
# # randomly rotate images in the range (deg 0 to 180)
# rotation_range=0,
# # randomly shift images horizontally
# width_shift_range=0.1,
# # randomly shift images vertically
# height_shift_range=0.1,
# # randomly flip images
# horizontal_flip=True,
# # randomly flip images
# vertical_flip=False)
#
# # Compute quantities required for featurewise normalization
# # (std, mean, and principal components if ZCA whitening is applied).
# datagen.fit(x_train)
#
# # Fit the model on the batches generated by datagen.flow().
# model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
# validation_data=(x_test, y_test),
# epochs=epochs, verbose=1, workers=4,
# callbacks=callbacks)
ModelTrainer(model, x_train, y_train, x_test, y_test, False).train_model()
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
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))
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
ModelTrainer(model, x_train, y_train, x_test, y_test, True).train_model()
# model.fit(x_train, y_train,
# batch_size=batch_size,
# epochs=epochs,
# verbose=1,
# validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
def test_model_trainer():
model = DefaultClassifierGenerator(3,
(28, 28, 3)).generate().produce_model()
train_data, test_data = get_processed_data()
ModelTrainer(model, train_data, test_data, Accuracy,
False).train_model(max_iter_num=3)
from autokeras.preprocessor import OneHotEncoder, DataTransformer
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)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train, y_train)
test_data = data_transformer.transform_test(x_test, y_test)
print('Start Generating')
graphs = [DefaultClassifierGenerator(10, x_train.shape[1:]).generate()]
keras_model = graphs[0].produce_model()
print('Start Training')
loss, acc = ModelTrainer(keras_model,
train_data,
test_data,
True).train_model(max_no_improvement_num=100, batch_size=128)
print(loss, acc)
def test_model_trainer_classification():
model = CnnGenerator(3, (28, 28, 3)).generate().produce_model()
train_data, test_data = get_classification_dataloaders()
ModelTrainer(model, train_data, test_data, Accuracy, classification_loss, False).train_model(max_iter_num=3)
def test_model_trainer_regression():
model = CnnGenerator(1, (28, 28, 3)).generate().produce_model()
train_data, test_data = get_regression_dataloaders()
ModelTrainer(model, train_data, test_data, MSE, regression_loss, False).train_model(max_iter_num=3)