def create_problem(load_data):
Problem = NaProblem(seed=2019)
Problem.load_data(load_data)
#Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_search_space, num_layers=10)
Problem.hyperparameters(
verbose=0,
batch_size=100,
learning_rate=0.001, #lr search: 0.01, lr post: 0.001
optimizer='adam',
num_epochs=50,
callbacks=dict(EarlyStopping=dict(
monitor='val_r2', mode='max', verbose=0, patience=5)))
Problem.loss('mse')
Problem.metrics(['r2'])
Problem.objective('val_r2__last')
Problem.post_training(num_epochs=1000,
metrics=['r2'],
callbacks=dict(ModelCheckpoint={
'monitor': 'val_r2',
'mode': 'max',
'save_best_only': True,
'verbose': 1
},
EarlyStopping={
'monitor': 'val_r2',
'mode': 'max',
'verbose': 1,
'patience': 50
},
TensorBoard=dict(log_dir='{}'.format(
time.time()), )))
if __name__ == '__main__':
print(Problem)
from pprint import pprint
pprint(Problem.space)
Problem.hyperparameters(
batch_size=64,
learning_rate=0.001,
optimizer='adam',
num_epochs=1,
)
Problem.loss('mse')
Problem.metrics(['r2'])
Problem.objective('val_r2__last')
Problem.post_training(num_epochs=1000,
metrics=['r2'],
model_checkpoint={
'monitor': 'val_r2',
'mode': 'max',
'save_best_only': True,
'verbose': 1
},
early_stopping={
'monitor': 'val_r2',
'mode': 'max',
'verbose': 1,
'patience': 20
})
if __name__ == '__main__':
print(Problem)
Problem.objective('r2__max') # or 'val_acc__last' ?
Problem.post_training(
repeat=1,
num_epochs=1000,
metrics=["mse", "r2"],
callbacks=dict()
# callbacks=dict(
# ModelCheckpoint={
# 'monitor': 'val_r2',
# 'mode': 'max',
# 'save_best_only': True,
# 'verbose': 1
# },
# EarlyStopping={
# 'monitor': 'val_r2',
# 'mode': 'max',
# 'verbose': 1,
# 'patience': 10
# },
# TensorBoard={
# 'log_dir':'tb_logs',
# 'histogram_freq':1,
# 'batch_size':64,
# 'write_graph':True,
# 'write_grads':True,
# 'write_images':True,
# 'update_freq':'epoch'
# })
)
# Just to print your problem, to test its definition and imports in the current python environment.
callbacks=dict(EarlyStopping=dict(
monitor='val_r2', mode='max', verbose=0, patience=5)))
Problem.loss('mse')
Problem.metrics(['r2'])
Problem.objective('val_r2__last')
Problem.post_training(num_epochs=1000,
metrics=['r2'],
callbacks=dict(ModelCheckpoint={
'monitor': 'val_r2',
'mode': 'max',
'save_best_only': True,
'verbose': 1
},
EarlyStopping={
'monitor': 'val_r2',
'mode': 'max',
'verbose': 1,
'patience': 50
},
TensorBoard=dict(log_dir='{}'.format(
time.time()), )))
if __name__ == '__main__':
print(Problem)
from pprint import pprint
pprint(Problem.space)