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 deephyper.problem import NaProblem from nas_1.polynome2.load_data import load_data from nas_1.polynome2.search_space import create_search_space from deephyper.nas.preprocessing import minmaxstdscaler, stdscaler import numpy as np import tensorflow as tf tf.config.run_functions_eagerly(True) # from deephyper.nas.train_utils import * from sklearn import metrics Problem = NaProblem(seed=2019) Problem.load_data(load_data) Problem.preprocessing(stdscaler) Problem.search_space(create_search_space, num_layers=6) Problem.hyperparameters( batch_size=128, learning_rate=0.001, optimizer='adam', num_epochs=20, callbacks=dict(EarlyStopping=dict( monitor='val_loss', # or 'val_r2' or 'val_acc' ? # mode='max', verbose=0, patience=5, restore_best_weights=True)))