def test_full_problem(self):
from deephyper.nas.preprocessing import minmaxstdscaler
from deephyper.problem import NaProblem
pb = NaProblem()
def load_data(prop):
return ([[10]], [1]), ([10], [1])
pb.load_data(load_data, prop=1.0)
pb.preprocessing(minmaxstdscaler)
pb.search_space(OneLayerSpace)
pb.hyperparameters(
batch_size=64,
learning_rate=0.001,
optimizer="adam",
num_epochs=10,
loss_metric="mse",
)
with pytest.raises(NaProblemError):
pb.objective("r2")
pb.loss("mse")
pb.metrics(["r2"])
possible_objective = ["loss", "val_loss", "r2", "val_r2"]
for obj in possible_objective:
pb.objective(obj)
from deephyper.problem import NaProblem
from molnet.molnet.load_data import load_data
from molnet.molnet.search_space 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=3)
Problem.hyperparameters(
batch_size=32,
learning_rate=0.01,
optimizer='adam',
num_epochs=20,
callbacks=dict(EarlyStopping=dict(
monitor='val_r2', # or 'val_acc' ?
mode='max',
verbose=0,
patience=5)))
Problem.loss('mse') # or 'categorical_crossentropy' ?
Problem.metrics(['r2']) # or 'acc' ?
Problem.objective('val_r2__last') # or 'val_acc__last' ?
# Just to print your problem, to test its definition and imports in the current python environment.
from nas_problems.polynome2.load_data import load_data
from nas_problems.polynome2.search_space import create_search_space
from deephyper.problem import NaProblem
Problem = NaProblem()
Problem.load_data(load_data, size=1000)
Problem.search_space(create_search_space)
Problem.hyperparameters(batch_size=128,
learning_rate=0.001,
optimizer="rmsprop",
num_epochs=5)
Problem.loss("mse")
Problem.metrics(["r2"])
Problem.objective("val_r2__last")
Problem.post_training(
num_epochs=60,
metrics=["r2"],
model_checkpoint={
"monitor": "val_r2",
"mode": "max",
"save_best_only": True,
"verbose": 1,
},
early_stopping={
from deephyper.problem import NaProblem
from nas_big_data.combo.load_data import load_data_cache
from nas_big_data.combo.search_space_shared import create_search_space
Problem = NaProblem(seed=2019)
Problem.load_data(load_data_cache)
Problem.search_space(create_search_space, num_layers=5)
# schedules: https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules
Problem.hyperparameters(
lsr_batch_size=True,
lsr_learning_rate=True,
batch_size=Problem.add_hyperparameter((16, 2048, "log-uniform"),
"batch_size"),
learning_rate=Problem.add_hyperparameter(
(1e-4, 0.01, "log-uniform"),
"learning_rate",
),
optimizer=Problem.add_hyperparameter(
["sgd", "rmsprop", "adagrad", "adam", "adadelta", "adamax", "nadam"],
"optimizer"),
patience_ReduceLROnPlateau=Problem.add_hyperparameter(
(3, 30), "patience_ReduceLROnPlateau"),
patience_EarlyStopping=Problem.add_hyperparameter(
(3, 30), "patience_EarlyStopping"),
num_epochs=100,
verbose=0,
callbacks=dict(
