def test_inference(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.TemporaryDirectory() as export_dir:
dualnet.bootstrap(working_dir, model_params.DummyMiniGoParams())
exported_model = os.path.join(export_dir, 'bootstrap-model')
dualnet.export_model(working_dir, exported_model)
n1 = dualnet.DualNetRunner(
exported_model, model_params.DummyMiniGoParams())
n1.run(go.Position(utils_test.BOARD_SIZE))
n2 = dualnet.DualNetRunner(
exported_model, model_params.DummyMiniGoParams())
n2.run(go.Position(utils_test.BOARD_SIZE))
def test_inference(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.TemporaryDirectory() as export_dir:
dualnet.bootstrap(working_dir, model_params.DummyMiniGoParams())
exported_model = os.path.join(export_dir, 'bootstrap-model')
dualnet.export_model(working_dir, exported_model)
n1 = dualnet.DualNetRunner(exported_model,
model_params.DummyMiniGoParams())
n1.run(go.Position(utils_test.BOARD_SIZE))
n2 = dualnet.DualNetRunner(exported_model,
model_params.DummyMiniGoParams())
n2.run(go.Position(utils_test.BOARD_SIZE))
def bootstrap(estimator_model_dir, trained_models_dir, params):
"""Initialize the model with random weights.
Args:
estimator_model_dir: tf.estimator model directory.
trained_models_dir: Dir to save the trained models. Here to export the first
bootstrapped generation.
params: A MiniGoParams instance of hyperparameters for the model.
"""
bootstrap_name = utils.generate_model_name(0)
_ensure_dir_exists(trained_models_dir)
bootstrap_model_path = os.path.join(trained_models_dir, bootstrap_name)
_ensure_dir_exists(estimator_model_dir)
print('Bootstrapping with working dir {}\n Model 0 exported to {}'.format(
estimator_model_dir, bootstrap_model_path))
dualnet.bootstrap(estimator_model_dir, params)
dualnet.export_model(estimator_model_dir, bootstrap_model_path)
def bootstrap(estimator_model_dir, trained_models_dir, params):
"""Initialize the model with random weights.
Args:
estimator_model_dir: tf.estimator model directory.
trained_models_dir: Dir to save the trained models. Here to export the first
bootstrapped generation.
params: A MiniGoParams instance of hyperparameters for the model.
"""
bootstrap_name = utils.generate_model_name(0)
_ensure_dir_exists(trained_models_dir)
bootstrap_model_path = os.path.join(trained_models_dir, bootstrap_name)
_ensure_dir_exists(estimator_model_dir)
print('Bootstrapping with working dir {}\n Model 0 exported to {}'.format(
estimator_model_dir, bootstrap_model_path))
dualnet.bootstrap(estimator_model_dir, params)
dualnet.export_model(estimator_model_dir, bootstrap_model_path)