def test_megnet(self):
units_v = [13, 14, 16]
units_e = [16, 16, 17]
units_u = [13, 14, 18]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out], [units_v[-1], units_e[-1], units_u[-1]])
new_layer = MEGNetLayer.from_config(layer.get_config())
out2 = new_layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out2], [units_v[-1], units_e[-1], units_u[-1]])
int32 = 'int32'
x1 = np.random.rand(1, 5, 10)
x2 = np.random.rand(1, 6, 5)
x3 = np.random.rand(1, 2, 20)
x4 = np.array([0, 1, 2, 3, 3, 4]).reshape([1, -1])
x5 = np.array([1, 0, 3, 2, 4, 3]).reshape([1, -1])
x6 = np.array([[0, 0, 1, 1, 1]])
x7 = np.array([[0, 0, 1, 1, 1, 1]])
x1_ = Input(shape=(None, 10))
x2_ = Input(shape=(None, 5))
x3_ = Input(shape=(None, 20))
x4_ = Input(shape=(None,), dtype=int32)
x5_ = Input(shape=(None,), dtype=int32)
x6_ = Input(shape=(None,), dtype=int32)
x7_ = Input(shape=(None,), dtype=int32)
out = MEGNetLayer([10, 5], [20, 4], [30, 3])(
[x1_, x2_, x3_, x4_, x5_, x6_, x7_])
model = Model(inputs=[x1_, x2_, x3_, x4_, x5_, x6_, x7_], outputs=out)
model.compile('adam', 'mse')
ans = model.predict([x1, x2, x3, x4, x5, x6, x7])
self.assertEqual(ans[0].shape, (1, 5, 5))
def test_megnet(self):
units_v = [13, 14, 16]
units_e = [16, 16, 17]
units_u = [13, 14, 18]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out],
[units_v[-1], units_e[-1], units_u[-1]])
new_layer = MEGNetLayer.from_config(layer.get_config())
out2 = new_layer(self.x)
self.assertListEqual([i._keras_shape[-1] for i in out2],
[units_v[-1], units_e[-1], units_u[-1]])
def setUpClass(cls):
cls.n_feature = 5
cls.n_bond_features = 6
cls.n_global_features = 2
cls.inp = [
Input(shape=(None, cls.n_feature)),
Input(shape=(None, cls.n_bond_features)),
Input(shape=(None, cls.n_global_features)),
Input(shape=(None, ), dtype='int32'),
Input(shape=(None, ), dtype='int32'),
Input(shape=(None, ), dtype='int32'),
Input(shape=(None, ), dtype='int32'),
]
units_v = [2, 2]
units_e = [2, 2]
units_u = [
2,
]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(cls.inp)
cls.out = Dense(1)(out[2])
cls.model = Model(inputs=cls.inp, outputs=cls.out)
cls.model.compile(loss='mse', optimizer='adam')
cls.x = [
np.random.normal(size=(1, 4, cls.n_feature)),
np.random.normal(size=(1, 6, cls.n_bond_features)),
np.random.normal(size=(1, 2, cls.n_global_features)),
np.array([[0, 0, 1, 1, 2, 3]]),
np.array([[1, 1, 0, 0, 3, 2]]),
np.array([[0, 0, 1, 1]]),
np.array([[0, 0, 0, 0, 1, 1]]),
]
cls.y = np.random.normal(size=(1, 2, 1))
cls.train_gen = Generator(cls.x, cls.y)
def one_block(a, b, c, has_ff=True, block_index=0):
if has_ff:
x1_ = ff(a, name_prefix=f"block_{block_index}_atom_ff")
x2_ = ff(b, name_prefix=f"block_{block_index}_bond_ff")
x3_ = ff(c, name_prefix=f"block_{block_index}_state_ff")
else:
x1_ = a
x2_ = b
x3_ = c
out = MEGNetLayer(
[n1, n1, n2],
[n1, n1, n2],
[n1, n1, n2],
pool_method="mean",
activation=act,
kernel_regularizer=reg,
name=f"megnet_{block_index}",
)([x1_, x2_, x3_, x4, x5, x6, x7])
x1_temp = out[0]
x2_temp = out[1]
x3_temp = out[2]
if dropout:
x1_temp = Dropout(dropout, name=f"dropout_atom_{block_index}")(
x1_temp, training=dropout_training)
x2_temp = Dropout(dropout, name=f"dropout_bond_{block_index}")(
x2_temp, training=dropout_training)
x3_temp = Dropout(dropout, name=f"dropout_state_{block_index}")(
x3_temp, training=dropout_training)
return x1_temp, x2_temp, x3_temp
def test_reduce_lr_upon_nan(self):
with ScratchDir('.'):
callbacks = [ReduceLRUponNan(patience=100)]
self.assertAlmostEqual(float(kb.get_value(self.model.optimizer.lr)), 1e-3)
gen = Generator(self.x, np.array([1, np.nan]).reshape((1, 2, 1)))
self.model.fit_generator(gen, steps_per_epoch=1, epochs=1, callbacks=callbacks, verbose=0)
self.assertAlmostEqual(float(kb.get_value(self.model.optimizer.lr)), 0.5e-3)
inp = [
Input(shape=(None, self.n_feature)),
Input(shape=(None, self.n_bond_features)),
Input(shape=(None, self.n_global_features)),
Input(shape=(None,), dtype='int32'),
Input(shape=(None,), dtype='int32'),
Input(shape=(None,), dtype='int32'),
Input(shape=(None,), dtype='int32'),
]
units_v = [2, 2]
units_e = [2, 2]
units_u = [2, ]
layer = MEGNetLayer(units_v, units_e, units_u)
out = layer(inp)
out = Dense(1)(out[2])
model = Model(inputs=inp, outputs=out)
model.compile(loss='mse', optimizer='adam')
x = [np.random.normal(size=(1, 4, self.n_feature)),
np.random.normal(size=(1, 6, self.n_bond_features)),
np.random.normal(size=(1, 2, self.n_global_features)),
np.array([[0, 0, 1, 1, 2, 3]]),
np.array([[1, 1, 0, 0, 3, 2]]),
np.array([[0, 0, 1, 1]]),
np.array([[0, 0, 0, 0, 1, 1]]),
]
y = np.random.normal(size=(1, 2, 1))
train_gen = Generator(x, y)
callbacks = [ReduceLRUponNan(filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', patience=100),
ModelCheckpointMAE(filepath='./val_mae_{epoch:05d}_{val_mae:.6f}.hdf5', val_gen=train_gen,
steps_per_val=1)
]
# 1. involve training and saving
model.fit_generator(train_gen, steps_per_epoch=1, epochs=2, callbacks=callbacks, verbose=1)
# 2. throw nan loss, trigger ReduceLRUponNan
model.fit_generator(gen, steps_per_epoch=1, epochs=2, callbacks=callbacks, verbose=1)
# 3. Normal training, recover saved model from 1
model.fit_generator(train_gen, steps_per_epoch=1, epochs=2, callbacks=callbacks, verbose=1)
self.assertAlmostEqual(float(kb.get_value(model.optimizer.lr)), 0.25e-3)
def one_block(a, b, c, has_ff=True):
if has_ff:
x1_ = ff(a)
x2_ = ff(b)
x3_ = ff(c)
else:
x1_ = a
x2_ = b
x3_ = c
out = MEGNetLayer(
[n1, n1, n2], [n1, n1, n2], [n1, n1, n2],
pool_method='mean', activation=act, kernel_regularizer=reg)(
[x1_, x2_, x3_, x4, x5, x6, x7])
x1_temp = out[0]
x2_temp = out[1]
x3_temp = out[2]
if dropout:
x1_temp = Dropout(dropout)(x1_temp)
x2_temp = Dropout(dropout)(x2_temp)
x3_temp = Dropout(dropout)(x3_temp)
return x1_temp, x2_temp, x3_temp
def create_model():
n_atom_feature = 8
n_bond_feature = 1
n_global_feature = 0
Xavier_init = initializers.VarianceScaling(scale=1.0,
mode='fan_avg',
distribution='normal')
x1 = Input(shape=(None, n_atom_feature)) # atom feature placeholder
x2 = Input(shape=(None, n_bond_feature)) # bond feature placeholder
x3 = Input(shape=(None, n_global_feature)) # global feature placeholder
x4 = Input(shape=(None, ), dtype='int32') # bond index1 placeholder
x5 = Input(shape=(None, ), dtype='int32') # bond index2 placeholder
x6 = Input(shape=(None, ), dtype='int32') # atom_ind placeholder
x7 = Input(shape=(None, ), dtype='int32') # bond_ind placeholder
x8 = Input(shape=(None, ), dtype='int32'
) # Ga_index placeholder to gather Ga nodes to a single tensor
x9 = Input(shape=(None, ), dtype='int32'
) # As_index placeholder to gather As nodes to a single tensor
x10 = Input(
shape=(None, ), dtype='int32'
) # Ga_ind placeholder to sum of the atomic energies of Ga atoms for each single structure
x11 = Input(
shape=(None, ), dtype='int32'
) # As_ind placeholder to sum of the atomic energies of As atoms for each single structure
with kb.name_scope("embedding"):
embed_atom_fea = layers.Dense(16,
name="embedding_atom_fea",
activation='tanh',
kernel_initializer=Xavier_init)
embed_bond_fea = layers.Dense(16,
name="embedding_bond_fea",
activation='tanh',
kernel_initializer=Xavier_init)
GN_1 = MEGNetLayer([16, 16], [16, 16], [1],
pool_method='mean',
activation=softplus2)
GN_2 = MEGNetLayer([16, 16], [16, 16], [1],
pool_method='mean',
activation=softplus2)
x1_ = embed_atom_fea(x1)
x2_ = embed_bond_fea(x2)
out1 = GN_1([x1_, x2_, x3, x4, x5, x6, x7])
x1__ = layers.Add()([x1_, out1[0]])
x2__ = layers.Add()([x2_, out1[1]])
out2 = GN_2([x1__, x2__, out1[2], x4, x5, x6, x7])
x1___ = layers.Add()([x1__, out2[0]])
x2___ = layers.Add()([x2__, out2[1]])
Ga_idx = layers.Lambda(lambda x: tf.reshape(x, (-1, )),
name="Ga_idx")(x8)
As_idx = layers.Lambda(lambda x: tf.reshape(x, (-1, )),
name="As_idx")(x9)
Ga = layers.Lambda(lambda x: tf.gather(x, Ga_idx, axis=1),
name="Ga")(x1___)
As = layers.Lambda(lambda x: tf.gather(x, As_idx, axis=1),
name="As")(x1___)
Ga_grp = layers.Lambda(lambda x: tf.reshape(x, (-1, )),
name="Ga_grp")(x10)
As_grp = layers.Lambda(lambda x: tf.reshape(x, (-1, )),
name="As_grp")(x11)
#node = Set2Set(T=3, n_hidden=3)([x1___, x6])
edge = Set2Set(T=3, n_hidden=3)([x2___, x7])
edge = layers.Lambda(lambda x: kb.sum(x, axis=2, keepdims=True),
name="sum_edge")(edge)
zero = layers.Lambda(lambda x: tf.zeros_like(x),
name="zero_like_edge")(edge)
zero_edge = layers.Multiply(name="zero_edge")([edge, zero])
zero_glob = layers.Multiply(name="zero_glob")([out2[2], zero])
#final = layers.Concatenate(axis=-1)([node, edge, out2[2]])
with kb.name_scope("Ga"):
hidden_Ga1 = layers.Dense(10,
name="hidden_Ga1",
activation='tanh',
kernel_initializer=Xavier_init)
hidden_Ga2 = layers.Dense(10,
name="hidden_Ga2",
activation='tanh',
kernel_initializer=Xavier_init)
output_Ga = layers.Dense(1,
name="output_Ga",
activation=None,
kernel_initializer=Xavier_init)
E_Ga = hidden_Ga1(Ga)
E_Ga = hidden_Ga2(E_Ga)
E_Ga = output_Ga(E_Ga)
E_Ga = layers.Lambda(lambda x: tf.reshape(x, (-1, 1)),
name="reshape_E_Ga")(E_Ga)
sum_Ga = layers.Lambda(lambda x: tf.math.segment_sum(x, Ga_grp),
name="sum_Ga")(E_Ga)
#sum_Ga = layers.Lambda(lambda x: tf.reshape(x, glob.shape), name="reshape_sum_Ga")(sum_Ga)
sum_Ga = layers.Lambda(lambda x: tf.expand_dims(x, axis=0),
name="reshape_sum_Ga")(sum_Ga)
with kb.name_scope("As"):
hidden_As1 = layers.Dense(10,
name="hidden_As1",
activation='tanh',
kernel_initializer=Xavier_init)
hidden_As2 = layers.Dense(10,
name="hidden_As2",
activation='tanh',
kernel_initializer=Xavier_init)
output_As = layers.Dense(1,
name="output_As",
activation=None,
kernel_initializer=Xavier_init)
E_As = hidden_As1(As)
E_As = hidden_As2(E_As)
E_As = output_As(E_As)
E_As = layers.Lambda(lambda x: tf.reshape(x, (-1, 1)),
name="reshape_E_As")(E_As)
sum_As = layers.Lambda(lambda x: tf.math.segment_sum(x, As_grp),
name="sum_As")(E_As)
#sum_As = layers.Lambda(lambda x: tf.reshape(x, glob.shape), name="reshape_sum_As")(sum_As)
sum_As = layers.Lambda(lambda x: tf.expand_dims(x, axis=0),
name="reshape_sum_As")(sum_As)
total_E = layers.Add(name="total_E")([sum_Ga, sum_As])
final_E = layers.Add(name="final_E")([total_E, zero_edge, zero_glob])
# total_E = layers.Lambda(lambda x: tf.expand_dims(x, axis=0))(total_E)
return Model(inputs=[x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11],
outputs=final_E)