def setUp(self):
tree = ((0, 1), ((2, 3), 4))
self.func = BinaryHierarchicalSoftmax(3, tree)
self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.t = numpy.array([0, 2])
self.gy = numpy.random.uniform(-1, 1, (1, 1)).astype(numpy.float32)
self.W = self.func.W.copy()
class TestBinaryHierarchicalSoftmax(TestCase):
def setUp(self):
tree = ((0, 1), ((2, 3), 4))
self.func = BinaryHierarchicalSoftmax(3, tree)
self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.t = numpy.array([0, 2])
self.gy = numpy.random.uniform(-1, 1, (1, 1)).astype(numpy.float32)
self.W = self.func.W.copy()
def test_sum(self):
x = numpy.array([[1.0, 2.0, 3.0]])
total = 0
for i in range(5):
t = numpy.array([i])
loss, = self.func.forward_cpu((x, t))
total += numpy.exp(-loss)
self.assertEquals(1.0, float(total))
def check_backward(self, x_data, t_data, y_grad, use_cudnn=True):
x = Variable(x_data)
t = Variable(t_data)
y = self.func(x, t)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, t.data))
gx, _, gW = numerical_grad(f, (x.data, t.data, func.W), (y.grad,), eps=1e-2)
assert_allclose(to_cpu(gx), to_cpu(x.grad))
assert_allclose(to_cpu(gW), to_cpu(func.gW))
def test_backward_cpu(self):
self.check_backward(self.x, self.t, self.gy)
def setUp(self):
tree = ((0, 1), ((2, 3), 4))
self.func = BinaryHierarchicalSoftmax(3, tree)
self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.t = numpy.array([0, 2])
self.gy = numpy.random.uniform(-1, 1, (1, 1)).astype(numpy.float32)
self.W = self.func.W.copy()
class TestBinaryHierarchicalSoftmax(TestCase):
def setUp(self):
tree = ((0, 1), ((2, 3), 4))
self.func = BinaryHierarchicalSoftmax(3, tree)
self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.t = numpy.array([0, 2])
self.gy = numpy.random.uniform(-1, 1, (1, 1)).astype(numpy.float32)
self.W = self.func.W.copy()
def test_sum(self):
x = numpy.array([[1.0, 2.0, 3.0]])
total = 0
for i in range(5):
t = numpy.array([i])
loss, = self.func.forward_cpu((x, t))
total += numpy.exp(-loss)
self.assertAlmostEqual(1.0, float(total))
def check_backward(self, x_data, t_data, y_grad, use_cudnn=True):
x = Variable(x_data)
t = Variable(t_data)
y = self.func(x, t)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data, t.data))
gx, _, gW = numerical_grad(f, (x.data, t.data, func.W), (y.grad, ),
eps=1e-2)
assert_allclose(to_cpu(gx), to_cpu(x.grad))
assert_allclose(to_cpu(gW), to_cpu(func.gW))
def test_backward_cpu(self):
self.check_backward(self.x, self.t, self.gy)