def test_sgd_optimizer_momentum():
params = [np.zeros(shape) for shape in shapes]
lr = 0.1
for momentum in np.arange(0.5, 0.9, 0.1):
optimizer = SGDOptimizer(params, lr, momentum=momentum, nesterov=False)
velocities = [np.random.random(shape) for shape in shapes]
optimizer.velocities = velocities
grads = [np.random.random(shape) for shape in shapes]
updates = [momentum * velocity - lr * grad
for velocity, grad in zip(velocities, grads)]
expected = [param + update for param, update in zip(params, updates)]
optimizer.update_params(grads)
for exp, param in zip(expected, optimizer.params):
assert_array_equal(exp, param)
def test_sgd_optimizer_momentum():
params = [np.zeros(shape) for shape in shapes]
lr = 0.1
for momentum in np.arange(0.5, 0.9, 0.1):
optimizer = SGDOptimizer(params, lr, momentum=momentum, nesterov=False)
velocities = [np.random.random(shape) for shape in shapes]
optimizer.velocities = velocities
grads = [np.random.random(shape) for shape in shapes]
updates = [momentum * velocity - lr * grad
for velocity, grad in zip(velocities, grads)]
expected = [param + update for param, update in zip(params, updates)]
optimizer.update_params(grads)
for exp, param in zip(expected, optimizer.params):
assert_array_equal(exp, param)
def test_sgd_optimizer_nesterovs_momentum():
params = [np.zeros(shape) for shape in shapes]
lr = 0.1
rng = np.random.RandomState(0)
for momentum in np.arange(0.5, 0.9, 0.1):
optimizer = SGDOptimizer(params, lr, momentum=momentum, nesterov=True)
velocities = [rng.random_sample(shape) for shape in shapes]
optimizer.velocities = velocities
grads = [rng.random_sample(shape) for shape in shapes]
updates = [
momentum * velocity - lr * grad
for velocity, grad in zip(velocities, grads)
]
updates = [
momentum * update - lr * grad
for update, grad in zip(updates, grads)
]
expected = [param + update for param, update in zip(params, updates)]
optimizer.update_params(params, grads)
for exp, param in zip(expected, params):
assert_array_equal(exp, param)
