def test_gradient_descent(self):
"""Test creating a Gradient Descent optimizer."""
opt = optimizers.GradientDescent(learning_rate=0.01)
with self.session() as sess:
global_step = tf.Variable(0)
tfopt = opt._create_optimizer(global_step)
assert isinstance(tfopt, tf.train.GradientDescentOptimizer)
def test_gradient_descent_pytorch(self):
"""Test creating a Gradient Descent optimizer."""
opt = optimizers.GradientDescent(learning_rate=0.01)
params = [torch.nn.Parameter(torch.Tensor([1.0]))]
torchopt = opt._create_pytorch_optimizer(params)
assert isinstance(torchopt, torch.optim.SGD)
def test_gradient_descent_tf(self):
"""Test creating a Gradient Descent optimizer."""
opt = optimizers.GradientDescent(learning_rate=0.01)
global_step = tf.Variable(0)
tfopt = opt._create_tf_optimizer(global_step)
assert isinstance(tfopt, tf.keras.optimizers.SGD)
def test_gradient_descent_jax(self):
"""Test creating an Gradient Descent Optimizer."""
import optax
opt = optimizers.GradientDescent(learning_rate=0.01)
jaxopt = opt._create_jax_optimizer()
assert isinstance(jaxopt, optax.GradientTransformation)