def test_wrap_network_with_self_interaction_layer_large_num_electrons(
self):
grids = jnp.linspace(-5, 5, 9, dtype=jnp.float32)
density = 100. * utils.gaussian(grids=grids, center=1.,
sigma=1.).astype(jnp.float32)
reshaped_density = density[jnp.newaxis, :, jnp.newaxis]
inner_network_init_fn, inner_network_apply_fn = neural_xc.build_unet(
num_filters_list=[2, 4], core_num_filters=4, activation='swish')
init_fn, apply_fn = neural_xc.wrap_network_with_self_interaction_layer(
network=(inner_network_init_fn, inner_network_apply_fn),
grids=grids,
interaction_fn=utils.exponential_coulomb)
output_shape, init_params = init_fn(random.PRNGKey(0),
input_shape=((-1, 9, 1)))
self.assertEqual(output_shape, (-1, 9, 1))
self.assertEqual(
apply_fn(init_params, reshaped_density).shape, (1, 9, 1))
np.testing.assert_allclose(
apply_fn(init_params, reshaped_density),
inner_network_apply_fn(
# The initial parameters of the inner network.
init_params[1][1],
reshaped_density))
def test_wrap_network_with_self_interaction_layer_one_electron(self):
grids = jnp.linspace(-5, 5, 9)
density = utils.gaussian(grids=grids, center=1., sigma=1.)
reshaped_density = density[jnp.newaxis, :, jnp.newaxis]
init_fn, apply_fn = neural_xc.wrap_network_with_self_interaction_layer(
network=neural_xc.build_unet(num_filters_list=[2, 4],
core_num_filters=4,
activation='swish'),
grids=grids,
interaction_fn=utils.exponential_coulomb)
output_shape, init_params = init_fn(random.PRNGKey(0),
input_shape=((-1, 9, 1)))
self.assertEqual(output_shape, (-1, 9, 1))
np.testing.assert_allclose(
apply_fn(init_params, reshaped_density),
-0.5 * scf.get_hartree_potential(
density=density,
grids=grids,
interaction_fn=utils.exponential_coulomb)[jnp.newaxis, :,
jnp.newaxis])