def testOptimizersWithDefaults(self): optimizers = [ tf.compat.v1.train.GradientDescentOptimizer, tf.compat.v1.train.AdadeltaOptimizer, tf.compat.v1.train.AdagradOptimizer, (tf.compat.v1.train.AdagradDAOptimizer, { 'global_step': '@get_global_step()' }), (tf.compat.v1.train.MomentumOptimizer, { 'momentum': 0.9 }), tf.compat.v1.train.AdamOptimizer, tf.compat.v1.train.FtrlOptimizer, tf.compat.v1.train.ProximalGradientDescentOptimizer, tf.compat.v1.train.ProximalAdagradOptimizer, tf.compat.v1.train.RMSPropOptimizer, ] constant_lr = lambda global_step: 0.01 for optimizer in optimizers: extra_bindings = {} if isinstance(optimizer, tuple): optimizer, extra_bindings = optimizer config.clear_config() config_lines = ['fake_train_model.optimizer = @%s' % optimizer.__name__] for param, val in extra_bindings.items(): config_lines.append('%s.%s = %s' % (optimizer.__name__, param, val)) config.parse_config(config_lines) # pylint: disable=no-value-for-parameter _, configed_optimizer = fake_train_model(constant_lr) # pylint: enable=no-value-for-parameter self.assertIsInstance(configed_optimizer, optimizer)
def testOptimizersWithDefaults(self): optimizers = [ torch.optim.Adadelta, torch.optim.Adagrad, torch.optim.Adam, torch.optim.SparseAdam, torch.optim.Adamax, torch.optim.ASGD, torch.optim.LBFGS, torch.optim.RMSprop, torch.optim.Rprop, torch.optim.SGD, ] for optimizer in optimizers: config.clear_config() config_str = """ fake_train_model.optimizer = @{optimizer} {optimizer}.lr = 0.001 """ config.parse_config( config_str.format(optimizer=optimizer.__name__)) configed_optimizer, _ = fake_train_model(config.REQUIRED) self.assertIsInstance(configed_optimizer, optimizer)
def setUp(self): tf.reset_default_graph() config.clear_config()
def tearDown(self): config.clear_config() super(TFConfigTest, self).tearDown()
def tearDown(self): config.clear_config() super(PyTorchConfigTest, self).tearDown()
def tearDown(self): config.clear_config()
def setUp(self): super().setUp() tf.compat.v1.reset_default_graph() config.clear_config()
def setUp(self): super().setUp() tf.compat.v1.disable_eager_execution() tf.compat.v1.reset_default_graph() config.clear_config()