class TestBatchNormalization(TestCase):
aggr_axes = 0
def setUp(self):
self.func = BatchNormalization(3)
self.func.gamma = numpy.random.uniform(
.5, 1, self.func.gamma.shape).astype(numpy.float32)
self.func.beta = numpy.random.uniform(
-1, 1, self.func.beta.shape).astype(numpy.float32)
self.func.ggamma.fill(0)
self.func.gbeta.fill(0)
self.gamma = self.func.gamma.copy().reshape(1, 3) # fixed on CPU
self.beta = self.func.beta.copy().reshape(1, 3) # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
def check_forward(self, x_data):
x = Variable(x_data)
y = self.func(x)
mean = self.x.mean(axis=self.aggr_axes, keepdims=True)
std = numpy.sqrt(self.x.var(axis=self.aggr_axes, keepdims=True) + self.func.eps)
y_expect = self.gamma * (self.x - mean) / std + self.beta
assert_allclose(y_expect, y.data)
def test_forward_cpu(self):
self.check_forward(self.x)
@attr.gpu
def test_forward_gpu(self):
self.func.to_gpu()
self.check_forward(to_gpu(self.x))
def check_backward(self, x_data, y_grad):
x = Variable(x_data)
y = self.func(x)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((x.data,))
gx, ggamma, gbeta = numerical_grad(f, (x.data, func.gamma, func.beta), (y.grad,), eps=1e-2)
assert_allclose(gx, x.grad, rtol=1e-3, atol=1e-4)
assert_allclose(ggamma, func.ggamma)
assert_allclose(gbeta, func.gbeta)
def test_backward_cpu(self):
self.check_backward(self.x, self.gy)
@attr.gpu
def test_backward_gpu(self):
self.func.to_gpu()
self.check_backward(to_gpu(self.x), to_gpu(self.gy))
def setUp(self):
self.func = BatchNormalization(3)
self.func.gamma = numpy.random.uniform(
.5, 1, self.func.gamma.shape).astype(numpy.float32)
self.func.beta = numpy.random.uniform(
-1, 1, self.func.beta.shape).astype(numpy.float32)
self.func.ggamma.fill(0)
self.func.gbeta.fill(0)
self.gamma = self.func.gamma.copy().reshape(1, 3) # fixed on CPU
self.beta = self.func.beta.copy().reshape(1, 3) # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
def setUp(self):
self.func = BatchNormalization(3)
self.func.gamma = numpy.random.uniform(
.5, 1, self.func.gamma.shape).astype(numpy.float32)
self.func.beta = numpy.random.uniform(
-1, 1, self.func.beta.shape).astype(numpy.float32)
self.func.ggamma.fill(0)
self.func.gbeta.fill(0)
self.gamma = self.func.gamma.copy().reshape(1, 3) # fixed on CPU
self.beta = self.func.beta.copy().reshape(1, 3) # fixed on CPU
self.x = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
self.gy = numpy.random.uniform(-1, 1, (7, 3)).astype(numpy.float32)
