def message_block(atom_state, bond_state, connectivity):
source_atom_gather = GatherAtomToBond(1)
target_atom_gather = GatherAtomToBond(0)
source_atom = source_atom_gather([atom_state, connectivity])
target_atom = target_atom_gather([atom_state, connectivity])
# Edge update network
bond_state = Concatenate()([source_atom, target_atom, bond_state])
bond_state = Dense(2*atom_features, activation='softplus')(bond_state)
bond_state = Dense(atom_features)(bond_state)
# message function
bond_state = Dense(atom_features, activation='softplus')(bond_state)
bond_state = Dense(atom_features, activation='softplus')(bond_state)
source_atom = Dense(atom_features)(source_atom)
messages = Multiply()([source_atom, bond_state])
messages = ReduceBondToAtom(reducer='sum')([messages, connectivity])
# state transition function
messages = Dense(atom_features, activation='softplus')(messages)
messages = Dense(atom_features)(messages)
atom_state = Add()([atom_state, messages])
return atom_state, bond_state
def message_block(original_atom_state, original_bond_state, connectivity, i):
atom_state = BatchNormalization()(original_atom_state)
bond_state = BatchNormalization()(original_bond_state)
source_atom_gather = GatherAtomToBond(1)
target_atom_gather = GatherAtomToBond(0)
source_atom = source_atom_gather([atom_state, connectivity])
target_atom = target_atom_gather([atom_state, connectivity])
# Edge update network
new_bond_state = Concatenate(name='concat_{}'.format(i))([
source_atom, target_atom, bond_state])
new_bond_state = Dense(
2*atom_features, activation='relu')(new_bond_state)
new_bond_state = Dense(atom_features)(new_bond_state)
bond_state = Add()([original_bond_state, new_bond_state])
# message function
source_atom = Dense(atom_features)(source_atom)
messages = Multiply()([source_atom, bond_state])
messages = ReduceBondToAtom(reducer='sum')([messages, connectivity])
# state transition function
messages = Dense(atom_features, activation='relu')(messages)
messages = Dense(atom_features)(messages)
atom_state = Add()([original_atom_state, messages])
return atom_state, bond_state
def test_ReduceBondToAtom():
bond = layers.Input(name='bond', shape=(5, ), dtype='float32')
connectivity = layers.Input(name='connectivity',
shape=(2, ),
dtype='int32')
reduce_layer = ReduceBondToAtom(reducer='max')
o = reduce_layer([bond, connectivity])
assert o._keras_shape == (None, 5)
model = GraphModel([bond, connectivity], o)
x1 = np.random.rand(5, 5)
x2 = np.array([[0, 0, 0, 1, 1], [1, 1, 1, 1, 1]]).T
out = model.predict_on_batch([x1, x2])
assert_allclose(x1[:3].max(0), out[0])
assert_allclose(x1[3:].max(0), out[1])