def test_gradient_descent():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W ** 2)
algorithm = GradientDescent(cost=cost, parameters=[W])
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
def test_gradient_descent():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W**2)
algorithm = GradientDescent(cost=cost, parameters=[W])
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
def _test(f):
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W**2)
gradients = OrderedDict()
gradients[W] = tensor.grad(cost, W)
algorithm = GradientDescent(gradients=f(gradients))
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
def _test(f):
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W ** 2)
gradients = OrderedDict()
gradients[W] = tensor.grad(cost, W)
algorithm = GradientDescent(gradients=f(gradients))
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
def test_theano_profile_for_sgd_function():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W ** 2)
algorithm = GradientDescent(
cost=cost, parameters=[W], theano_func_kwargs={'profile': True})
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
assert isinstance(algorithm._function.profile, ProfileStats)
def test_theano_profile_for_sgd_function():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W ** 2)
algorithm = GradientDescent(
cost=cost, parameters=[W], theano_func_kwargs={'profile': True})
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict())
assert_allclose(W.get_value(), -0.5 * W_start_value)
assert isinstance(algorithm._function.profile, ProfileStats)
def test_gradient_descent_spurious_sources():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W ** 2)
algorithm = GradientDescent(cost=cost, parameters=[W])
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
assert_raises(lambda:
algorithm.process_batch(dict(example_id='test')))
algorithm = GradientDescent(cost=cost, parameters=[W],
on_unused_sources='ignore')
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict(example_id='test'))
assert_allclose(W.get_value(), -0.5 * W_start_value)
def test_gradient_descent_spurious_sources():
W = shared_floatx(numpy.array([[1, 2], [3, 4]]))
W_start_value = W.get_value()
cost = tensor.sum(W**2)
algorithm = GradientDescent(cost=cost, parameters=[W])
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
assert_raises(lambda: algorithm.process_batch(dict(example_id='test')))
algorithm = GradientDescent(cost=cost,
parameters=[W],
on_unused_sources='ignore')
algorithm.step_rule.learning_rate.set_value(0.75)
algorithm.initialize()
algorithm.process_batch(dict(example_id='test'))
assert_allclose(W.get_value(), -0.5 * W_start_value)
