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)
from deephyper.benchmark import NaProblem
from candlepb.Uno.structs.uno_mlp_1 import create_structure
from candlepb.Uno.uno_baseline_keras2 import load_data_multi_array
Problem = NaProblem()
Problem.load_data(load_data_multi_array)
# Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_structure, num_cells=3)
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,
from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.linearReg.load_data import load_data
from deephyper.benchmark.nas.linearRegMultiInputsGen.load_data import load_data
from deephyper.search.nas.model.baseline.simple import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler
Problem = NaProblem()
Problem.load_data(load_data)
Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_search_space)
Problem.hyperparameters(
batch_size=100,
learning_rate=0.1,
optimizer='adam',
num_epochs=10,
)
Problem.loss('mse')
Problem.metrics(['r2'])
Problem.objective('val_r2')
# Just to print your problem, to test its definition and imports in the current python environment.
if __name__ == '__main__':
print(Problem)
from deephyper.benchmark import NaProblem
from deephyper.benchmark.nas.linearRegMultiLoss.load_data import load_data
from deephyper.search.nas.model.baseline.simple_bi_model import create_search_space
from deephyper.search.nas.model.preprocessing import minmaxstdscaler
Problem = NaProblem(seed=2019)
Problem.load_data(load_data)
# Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_search_space, num_layers=10)
Problem.hyperparameters(batch_size=100,
learning_rate=0.1,
optimizer="adam",
num_epochs=20)
Problem.loss(
loss={
"output_0": "mse",
"output_1": "mse"
},
weights={
"output_0": 0.0,
"output_1": 1.0
},
)
Problem.metrics({"output_0": ["r2", "mse"], "output_1": "mse"})
from candlepb.NT3.models.candle_conv_mlp_baseline import create_structure
from candlepb.NT3.nt3_baseline_keras2 import load_data
from deephyper.benchmark import NaProblem
Problem = NaProblem()
Problem.load_data(load_data)
# Problem.preprocessing(minmaxstdscaler)
Problem.search_space(create_structure)
Problem.hyperparameters(
batch_size=20,
learning_rate=0.01,
optimizer='adam',
num_epochs=1,
)
Problem.loss('categorical_crossentropy')
Problem.metrics(['acc'])
Problem.objective('val_acc__last')
Problem.post_training(num_epochs=1000,
metrics=['acc'],
model_checkpoint={
'monitor': 'val_acc',
'mode': 'max',
'save_best_only': True,