def testNormalizeWhereMask(self):
x = jnp.array([5.5, 1.3, -4.2, 0.9])
m = jnp.array([True, False, True, True])
x_filtered = jnp.take(x, jnp.array([0, 2, 3]))
out_masked = jnp.take(nn.normalize(x, where=m), jnp.array([0, 2, 3]))
out_filtered = nn.normalize(x_filtered)
self.assertAllClose(out_masked, out_filtered)
def apply_fun(params, x, **kwargs): beta, gamma = params # TODO(phawkins): np.expand_dims should accept an axis tuple. # (https://github.com/numpy/numpy/issues/12290) ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x))) z = normalize(x, axis, epsilon=epsilon) if center and scale: return gamma[ed] * z + beta[ed] if center: return z + beta[ed] if scale: return gamma[ed] * z return z
