def data():
'''
Data providing function:
This function is separated from model() so that hyperopt
won't reload data for each evaluation run.
'''
optModel = TrainOptimizer(
labelkey=('Sand', 'Branching', 'Mounding', 'Rock'),
train_image_path='../Images/Training_Patches/',
train_label_path='../Images/TrainingRef_Patches/',
train_out_file='NeMO_train.txt',
valid_image_path='../Images/Valid_Patches/',
valid_label_path='../Images/ValidRef_Patches/',
valid_out_file='NeMO_valid.txt',
pixel_mean=[127.5, 127.5, 127.5],
pixel_std=[127.5, 127.5, 127.5],
num_classes=4,
model=FCN,
model_name="NeMO_FCN")
train_generator, validation_generator = optModel.gen_data()
print("train_generator_size: ", train_generator.batch_size)
print("validation_generator_size: ", validation_generator.batch_size)
return train_generator, validation_generator
from NeMO_models import FCN
from NeMO_generator import NeMOImageGenerator, ImageSetLoader
from keras.callbacks import (
ReduceLROnPlateau,
CSVLogger,
EarlyStopping,
ModelCheckpoint,
TerminateOnNaN)
from NeMO_callbacks import CheckNumericsOps, WeightsSaver
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from hyperas.distributions import choice, uniform, conditional
from hyperas import optim
optModel = TrainOptimizer(labelkey=('Sand', 'Branching', 'Mounding', 'Rock'), train_image_path='../Images/Training_Patches/',
train_label_path = '../Images/TrainingRef_Patches/', train_out_file = 'NeMO_train.txt',
valid_image_path = '../Images/Valid_Patches/', valid_label_path = '../Images/ValidRef_Patches/',
valid_out_file = 'NeMO_valid.txt', pixel_mean = [127.5, 127.5, 127.5], pixel_std = [127.5, 127.5, 127.5],
num_classes = 4, model = FCN, model_name = "NeMO_FCN")
param_space=optModel.gen_param_space()
print(param_space)
print("lr=",param_space['lr'])
global _SESSION
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
_SESSION = tf.Session(config=config)
K.set_session(_SESSION)
datagen, train_loader, val_loader = optModel.gen_data()
def model(train_generator, validation_generator):
'''
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
'''
optModel = TrainOptimizer(
labelkey=('Sand', 'Branching', 'Mounding', 'Rock'),
train_image_path='../Images/Training_Patches/',
train_label_path='../Images/TrainingRef_Patches/',
train_out_file='NeMO_train.txt',
valid_image_path='../Images/Valid_Patches/',
valid_label_path='../Images/ValidRef_Patches/',
valid_out_file='NeMO_valid.txt',
pixel_mean=[127.5, 127.5, 127.5],
pixel_std=[127.5, 127.5, 127.5],
num_classes=4,
model=FCN,
model_name="NeMO_FCN")
model = optModel.model2opt()
choiceval = {{choice(['adam', 'rmsprop', 'sgd'])}}
if choiceval == 'adam':
adam = Adam(
lr={{choice([10**-6, 10**-5, 10**-4, 10**-3, 10**-2, 10**-1])}})
optim = adam
elif choiceval == 'rmsprop':
rmsprop = RMSprop(
lr={{choice([10**-6, 10**-5, 10**-4, 10**-3, 10**-2, 10**-1])}},
decay={{choice([1e-2, 1e-3, 1e-4])}})
optim = rmsprop
else:
sgd = SGD(
lr={{choice([10**-6, 10**-5, 10**-4, 10**-3, 10**-2, 10**-1])}},
decay={{choice([1e-2, 1e-3, 1e-4])}})
optim = sgd
model.compile(loss='categorical_crossentropy',
optimizer=optim,
metrics=['accuracy'])
globalvars.globalVar += 1
filepath = './output/weights_' + optModel.model_name + 'hyperas' + str(
globalvars.globalVar) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
csv_logger = CSVLogger('./output/hyperas_' + optModel.model_name +
'test_log.csv',
append=True,
separator=';')
tensor_board_logfile = './logs/' + optModel.model_name + str(
globalvars.globalVar)
tensor_board = TensorBoard(log_dir=tensor_board_logfile,
histogram_freq=0,
write_graph=True)
history = model.fit_generator(
train_generator,
steps_per_epoch=80,
epochs=3,
validation_data=validation_generator,
validation_steps=20,
verbose=0,
callbacks=[checkpoint, csv_logger, tensor_board])
h1 = history.history
acc_ = numpy.asarray(h1['acc'])
loss_ = numpy.asarray(h1['loss'])
val_loss_ = numpy.asarray(h1['val_loss'])
val_acc_ = numpy.asarray(h1['val_acc'])
parameters = space
opt = numpy.asarray(parameters["choiceval"])
if choiceval == 'adam':
lr = numpy.asarray(parameters["lr"])
decay = numpy.asarray(parameters["decay"])
elif choiceval == 'rmsprop':
lr = numpy.asarray(parameters["lr_1"])
decay = numpy.asarray(parameters["decay_1"])
elif choiceval == 'sgd':
lr = numpy.asarray(parameters["lr_2"])
decay = numpy.asarray(parameters["decay_2"])
acc_plot = './plots/accuracy_run_' + str(globalvars.globalVar) + ".png"
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('run: ' + str(globalvars.globalVar) + " opt: " + str(opt) +
" lr: " + str(lr) + " decay: " + str(decay))
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig(acc_plot)
plt.close()
los_plot = './plots/losses_run_' + str(globalvars.globalVar) + ".png"
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('run: ' + str(globalvars.globalVar) + " opt: " + str(opt) +
" lr: " + str(lr) + " decay: " + str(decay))
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig(los_plot)
plt.close()
print("parameters for run " + str(globalvars.globalVar) + ":")
print("-------------------------------")
print(parameters)
print("opt: ", opt)
print("lr: ", lr)
print("decay: ", decay)
print("val_accuracy: ", val_acc_)
acc_and_loss = numpy.column_stack((acc_, loss_, val_acc_, val_loss_))
save_file_model = './output/' + optModel.model_name + '_run_' + '_' + str(
globalvars.globalVar) + '.txt'
with open(save_file_model, 'w') as f:
numpy.savetxt(save_file_model, acc_and_loss, delimiter=",")
score, acc = model.evaluate_generator(generator=validation_generator,
steps=20,
verbose=0)
print('Test accuracy:', acc)
save_file_params = './output/params_run_' + '_' + str(
globalvars.globalVar) + '.txt'
rownames = numpy.array([
'Run', 'optimizer', 'learning_rate', 'decay', 'train_accuracy',
'train_loss', 'val_accuracy', 'val_loss', 'test_accuracy'
])
rowvals = (str(globalvars.globalVar), opt, lr, decay, acc_[-1], loss_[-1],
val_acc_[-1], val_loss_[-1], acc)
DAT = numpy.column_stack((rownames, rowvals))
numpy.savetxt(save_file_params, DAT, delimiter=",", fmt="%s")
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
DAT = numpy.column_stack((rownames, rowvals))
numpy.savetxt(save_file_params, DAT, delimiter=",", fmt="%s")
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
if __name__ == '__main__':
optModel = TrainOptimizer(
labelkey=('Sand', 'Branching', 'Mounding', 'Rock'),
train_image_path='../Images/Training_Patches/',
train_label_path='../Images/TrainingRef_Patches/',
train_out_file='NeMO_train.txt',
valid_image_path='../Images/Valid_Patches/',
valid_label_path='../Images/ValidRef_Patches/',
valid_out_file='NeMO_valid.txt',
pixel_mean=[127.5, 127.5, 127.5],
pixel_std=[127.5, 127.5, 127.5],
num_classes=4,
model=FCN,
model_name="NeMO_FCN")
trials = Trials()
best_run, best_model, space = optim.minimize(model=model,
data=data,
algo=tpe.suggest,
max_evals=3,
trials=trials,
eval_space=True,
return_space=True)