def load_model(self, model_table_name, model_namespace):
LOGGER.info("@ load guest model from name/ns" + ", " + str(model_table_name) + ", " + str(model_namespace))
lr_model_name = model_table_name + "_lr_model"
local_model_name = model_table_name + "_local_model"
super(HeteroDNNLRHost, self).load_model(lr_model_name, model_namespace)
model_parameters = load_model_parameters(local_model_name, model_namespace)
self.local_model.restore_model(model_parameters)
def test_save_n_load_model_parameters(self):
model_parameters = {"Wh": np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]]),
"bh": np.array([1, 1, 1, 1]),
"Wo": np.array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]]),
"bo": np.array([0, 0, 0, 0]),
"model_meta": {"learning_rate": 0.01,
"input_dim": 100,
"hidden_dim": 64}
}
model_table_name = "table_name_" + str(uuid.uuid1())
model_table_ns = "table_ns_" + str(uuid.uuid1())
save_model_parameters(model_parameters, model_table_name, model_table_ns)
actual_model_parameters = load_model_parameters(model_table_name, model_table_ns)
assert len(model_parameters) == len(actual_model_parameters)
for k in actual_model_parameters.keys():
if k == "model_meta":
print(actual_model_parameters[k])
else:
assert_matrix(actual_model_parameters[k], model_parameters[k])
def load_model(self, model_table_name, model_namespace):
LOGGER.info("@ load host model from name/ns" + ", " +
str(model_table_name) + ", " + str(model_namespace))
model_parameters = load_model_parameters(model_table_name,
model_namespace)
self.host_model.restore_model(model_parameters)
