def test_forward3(self):
N, C = 20, 10
x, gamma, beta, mean, var = get_params(N, C)
cy = CF.fixed_batch_normalization(x, gamma, beta, mean, var)
with dezero.test_mode():
y = F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(array_allclose(y.data, cy.data))
def test_forward1(self):
N, C = 8, 1
x, gamma, beta, mean, var = get_params(N, C)
cy = CF.batch_normalization(x,
gamma,
beta,
running_mean=mean,
running_var=var)
y = F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(array_allclose(y.data, cy.data))
def __call__(self, x):
if self.avg_mean.data is None:
self._init_params(x)
return F.batch_nrom(x, self.gamma, self.beta, self.avg_mean.data,
self.avg_var.data)
def test_forward4(self):
N, C, H, W = 20, 10, 5, 5
x, gamma, beta, mean, var = get_params(N, C, H, W)
cy = CF.batch_normalization(x, gamma, beta)
y = F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(array_allclose(y.data, cy.data))
def test_type1(self):
N, C = 8, 3
x, gamma, beta, mean, var = get_params(N, C)
y = F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(y.data.dtype == np.float32)
def test_backward6(self):
params = 10, 20, 5, 5
x, gamma, beta, mean, var = get_params(*params, dtype=np.float64)
f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(gradient_check(f, beta))
def test_backward3(self):
N, C = 8, 3
x, gamma, beta, mean, var = get_params(N, C, dtype=np.float64)
f = lambda beta: F.batch_nrom(x, gamma, beta, mean, var)
self.assertTrue(gradient_check(f, beta))
