def test_basicrmsprop_max_scaling(): a = shared_floatx([1e-6, 1e-6]) cost = (a**2).sum() step_rule = BasicRMSProp(decay_rate=0.5, max_scaling=1e5) steps, updates = step_rule.compute_steps( OrderedDict([(a, tensor.grad(cost, a))])) f = theano.function([], [steps[a]], updates=updates) assert_allclose(f()[0], [0.2, 0.2])
def test_basicrmsprop_max_scaling(): a = shared_floatx([1e-6, 1e-6]) cost = (a ** 2).sum() step_rule = BasicRMSProp(decay_rate=0.5, max_scaling=1e5) steps, updates = step_rule.compute_steps( OrderedDict([(a, tensor.grad(cost, a))])) f = theano.function([], [steps[a]], updates=updates) assert_allclose(f()[0], [0.2, 0.2])
def test_basicrmsprop(): a = shared_floatx([3, 4]) cost = (a**2).sum() step_rule = BasicRMSProp(decay_rate=0.5, max_scaling=1e5) steps, updates = step_rule.compute_steps( OrderedDict([(a, tensor.grad(cost, a))])) f = theano.function([], [steps[a]], updates=updates) assert_allclose(f()[0], [1.41421356, 1.41421356]) a.set_value([2, 3]) assert_allclose(f()[0], [0.9701425, 1.02899151]) a.set_value([1, 1.5]) assert_allclose(f()[0], [0.6172134, 0.64699664])
def test_basicrmsprop(): a = shared_floatx([3, 4]) cost = (a ** 2).sum() step_rule = BasicRMSProp(decay_rate=0.5, max_scaling=1e5) steps, updates = step_rule.compute_steps( OrderedDict([(a, tensor.grad(cost, a))])) f = theano.function([], [steps[a]], updates=updates) assert_allclose(f()[0], [1.41421356, 1.41421356]) a.set_value([2, 3]) assert_allclose(f()[0], [0.9701425, 1.02899151]) a.set_value([1, 1.5]) assert_allclose(f()[0], [0.6172134, 0.64699664])