def test_ReduceAtomToMol():
atom = layers.Input(name='atom', shape=(5, ), dtype='float32')
node_graph_indices = layers.Input(name='node_graph_indices',
shape=(1, ),
dtype='int32')
snode = Squeeze()(node_graph_indices)
reduce_layer = ReduceAtomToMol()
o = reduce_layer([atom, snode])
assert o._keras_shape == (None, 5)
model = GraphModel([atom, node_graph_indices], o)
x1 = np.random.rand(5, 5)
x2 = np.array([0, 0, 0, 1, 1])
out = model.predict_on_batch([x1, x2])
assert_allclose(x1[:3].sum(0), out[0])
assert_allclose(x1[3:].sum(0), out[1])
# 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
for _ in range(3):
atom_state, bond_state = message_block(atom_state, bond_state, connectivity)
atom_state = Dense(atom_features//2, activation='softplus')(atom_state)
atom_state = Dense(1)(atom_state)
atom_state = Add()([atom_state, atomwise_energy])
output = ReduceAtomToMol(reducer='mean')([atom_state, snode_graph_indices])
model = GraphModel([
node_graph_indices, atom_types, distance_rbf, connectivity], [output])
lr = 1E-4
epochs = 500
model.compile(optimizer=keras.optimizers.Adam(lr=lr, decay=1E-5), loss='mae')
model.summary()
if not os.path.exists(model_name):
os.makedirs(model_name)
with open('{}/schnet_preprocessor.p'.format(model_name), 'wb') as f:
pickle.dump(preprocessor, f)
atom_rnn_layer = GRUStep(atom_features)
message_layer = MessageLayer(reducer='sum')
message_steps = 3
# Perform the message passing
for _ in range(message_steps):
# Get the message updates to each atom
message = message_layer([atom_state, bond_matrix, connectivity])
# Update memory and atom states
atom_state = atom_rnn_layer([message, atom_state])
# atom_state = BatchNormalization(momentum=0.9)(atom_state)
atom_fingerprint = Dense(1024, activation='sigmoid')(atom_state)
mol_out = ReduceAtomToMol(reducer='sum')(
[atom_fingerprint, snode_graph_indices])
X = BatchNormalization(momentum=0.9)(mol_out)
X = Dense(512, activation='relu')(X)
X = BatchNormalization(momentum=0.9)(X)
X = Dense(256, activation='relu')(X)
X = Dense(num_output)(X)
model = GraphModel([node_graph_indices, atom_types, bond_types, connectivity],
[X])
epochs = 500
lr = 1E-3
decay = lr / epochs
def test_save_and_load_model(get_2d_sequence, tmpdir):
preprocessor, sequence = get_2d_sequence
node_graph_indices = Input(shape=(1, ),
name='node_graph_indices',
dtype='int32')
atom_types = Input(shape=(1, ), name='atom', dtype='int32')
bond_types = Input(shape=(1, ), name='bond', dtype='int32')
connectivity = Input(shape=(2, ), name='connectivity', dtype='int32')
squeeze = Squeeze()
snode_graph_indices = squeeze(node_graph_indices)
satom_types = squeeze(atom_types)
sbond_types = squeeze(bond_types)
atom_features = 5
atom_state = Embedding(preprocessor.atom_classes,
atom_features,
name='atom_embedding')(satom_types)
bond_matrix = Embedding2D(preprocessor.bond_classes,
atom_features,
name='bond_embedding')(sbond_types)
atom_rnn_layer = GRUStep(atom_features)
message_layer = MessageLayer(reducer='sum', dropout=0.1)
# Perform the message passing
for _ in range(2):
# Get the message updates to each atom
message = message_layer([atom_state, bond_matrix, connectivity])
# Update memory and atom states
atom_state = atom_rnn_layer([message, atom_state])
atom_fingerprint = Dense(64, activation='sigmoid')(atom_state)
mol_fingerprint = ReduceAtomToMol(reducer='sum')(
[atom_fingerprint, snode_graph_indices])
out = Dense(1)(mol_fingerprint)
model = GraphModel(
[node_graph_indices, atom_types, bond_types, connectivity], [out])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.compile(optimizer=keras.optimizers.Adam(lr=1E-4), loss='mse')
hist = model.fit_generator(sequence, epochs=1)
loss = model.evaluate_generator(sequence)
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = load_model(fname, custom_objects=custom_layers)
loss2 = model.evaluate_generator(sequence)
assert_allclose(loss, loss2)