def test_MODEL_loading(self):
nn = Neural_Network(preprocessor=self.pp3D)
model_path = os.path.join(self.tmp_dir3D.name, "my_model.hdf5")
nn.dump(model_path)
nn_new = Neural_Network(preprocessor=self.pp3D)
nn_new.load(model_path)
mode="min")
#-----------------------------------------------------#
# Run Pipeline for provided CV Fold #
#-----------------------------------------------------#
# Run pipeline for cross-validation fold
run_fold(fold,
model,
epochs=1000,
iterations=150,
evaluation_path=path_eval,
draw_figures=True,
callbacks=[cb_lr, cb_es, cb_tb, cb_cl, cb_mc],
save_models=False)
# Dump latest model to disk
model.dump(os.path.join(fold_subdir, "model.latest.hdf5"))
#-----------------------------------------------------#
# Inference for provided CV Fold #
#-----------------------------------------------------#
# Load best model weights during fitting
model.load(os.path.join(fold_subdir, "model.best.hdf5"))
# Obtain training and validation data set
training, validation = load_disk2fold(
os.path.join(fold_subdir, "sample_list.json"))
# Compute predictions
model.predict(validation, return_output=False)
def test_MODEL_storage(self):
nn = Neural_Network(preprocessor=self.pp3D)
model_path = os.path.join(self.tmp_dir3D.name, "my_model.hdf5")
nn.dump(model_path)
self.assertTrue(os.path.exists(model_path))