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()
