def testUniformDistribution(self):
shape = [100, 100]
expect_mean = 0.
expect_var = 1. / shape[0]
init = init_ops_v2.VarianceScaling(distribution="uniform")
with test_util.use_gpu():
x = self.evaluate(init(shape))
self.assertNear(np.mean(x), expect_mean, err=1e-2)
self.assertNear(np.var(x), expect_var, err=1e-2)
def testNormalDistribution(self):
shape = [100, 100]
expect_mean = 0.
expect_var = 1. / shape[0]
init = init_ops_v2.VarianceScaling(distribution="truncated_normal")
with test_util.use_gpu(), test.mock.patch.object(
random_ops, "truncated_normal",
wraps=random_ops.truncated_normal) as mock_truncated_normal:
x = self.evaluate(init(shape))
self.assertTrue(mock_truncated_normal.called)
self.assertNear(np.mean(x), expect_mean, err=1e-2)
self.assertNear(np.var(x), expect_var, err=1e-2)
def testInitializePartition(self):
partition_shape = (100, 100)
shape = [1000, 100]
expect_mean = 0.
expect_var = 1. / shape[0]
init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")
with test_util.use_gpu(), test.mock.patch.object(
random_ops, "random_normal",
wraps=random_ops.random_normal) as mock_random_normal:
x = self.evaluate(init(shape, partition_shape=partition_shape))
self.assertTrue(mock_random_normal.called)
self.assertEqual(x.shape, partition_shape)
self.assertNear(np.mean(x), expect_mean, err=1e-3)
self.assertNear(np.var(x), expect_var, err=1e-3)
