def test_gradient_descent_stops():
# Test stopping conditions of gradient descent.
class ObjectiveSmallGradient:
def __init__(self):
self.it = -1
def __call__(self, _, compute_error=True):
self.it += 1
return (10 - self.it) / 10.0, np.array([1e-5])
def flat_function(_, compute_error=True):
return 0.0, np.ones(1)
# Gradient norm
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
ObjectiveSmallGradient(), np.zeros(1), 0, n_iter=100,
n_iter_without_progress=100, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=1e-5, verbose=2)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert error == 1.0
assert it == 0
assert("gradient norm" in out)
# Maximum number of iterations without improvement
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
flat_function, np.zeros(1), 0, n_iter=100,
n_iter_without_progress=10, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=0.0, verbose=2)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert error == 0.0
assert it == 11
assert("did not make any progress" in out)
# Maximum number of iterations
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
_, error, it = _gradient_descent(
ObjectiveSmallGradient(), np.zeros(1), 0, n_iter=11,
n_iter_without_progress=100, momentum=0.0, learning_rate=0.0,
min_gain=0.0, min_grad_norm=0.0, verbose=2)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert error == 0.0
assert it == 10
assert("Iteration 10" in out)
def jumped_gradient_descent(self, obj_func, params, **opt_args):
it = opt_args['it'] - 1
n_jump_without_progress = opt_args[
"n_iter_without_progress"] // self.jump_size + 1
remaining = opt_args['n_iter'] - it
n_jumps = remaining // self.jump_size + 1
ct_no_improvement = 0
kl_divergence_best = None
new_opt_args = opt_args.copy()
for jump in range(n_jumps):
new_opt_args['it'] = it + 1
new_opt_args['n_iter'] = it + self.jump_size
params, kl_divergence, it = _gradient_descent(
obj_func, params, **new_opt_args)
self.X_embedded_jumps.append(params.reshape(-1, self.n_components))
self.momentum_jumps.append(opt_args['momentum'])
if jump > 0:
print(
"\rJump {}/{}: best_kl={:.6f}\t current_kl={:.6f}".format(
jump + 1, n_jumps, kl_divergence_best, kl_divergence),
end="",
flush=True)
if kl_divergence_best is None or kl_divergence < kl_divergence_best:
kl_divergence_best = kl_divergence
ct_no_improvement = 0
else:
ct_no_improvement += 1
if ct_no_improvement >= n_jump_without_progress:
break
print()
return params, kl_divergence, it