def test_Weave_pickle():
tg = TensorGraph()
atom_feature = Feature(shape=(None, 75))
pair_feature = Feature(shape=(None, 14))
pair_split = Feature(shape=(None,), dtype=tf.int32)
atom_to_pair = Feature(shape=(None, 2), dtype=tf.int32)
weave = WeaveLayer(
in_layers=[atom_feature, pair_feature, pair_split, atom_to_pair])
tg.add_output(weave)
tg.set_loss(weave)
tg.build()
tg.save()
def build_graph(self):
"""Building graph structures:
Features => WeaveLayer => WeaveLayer => Dense => WeaveGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.pair_features = Feature(shape=(None, self.n_pair_feat))
combined = Combine_AP(in_layers=[self.atom_features, self.pair_features])
self.pair_split = Feature(shape=(None,), dtype=tf.int32)
self.atom_split = Feature(shape=(None,), dtype=tf.int32)
self.atom_to_pair = Feature(shape=(None, 2), dtype=tf.int32)
weave_layer1 = WeaveLayer(
n_atom_input_feat=self.n_atom_feat,
n_pair_input_feat=self.n_pair_feat,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
in_layers=[combined, self.pair_split, self.atom_to_pair])
weave_layer2 = WeaveLayer(
n_atom_input_feat=self.n_hidden,
n_pair_input_feat=self.n_hidden,
n_atom_output_feat=self.n_hidden,
n_pair_output_feat=self.n_hidden,
update_pair=False,
in_layers=[weave_layer1, self.pair_split, self.atom_to_pair])
separated = Separate_AP(in_layers=[weave_layer2])
dense1 = Dense(
out_channels=self.n_graph_feat,
activation_fn=tf.nn.tanh,
in_layers=[separated])
batch_norm1 = BatchNormalization(epsilon=1e-5, mode=1, in_layers=[dense1])
weave_gather = WeaveGather(
self.batch_size,
n_input=self.n_graph_feat,
gaussian_expand=True,
in_layers=[batch_norm1, self.atom_split])
costs = []
self.labels_fd = []
for task in range(self.n_tasks):
if self.mode == "classification":
classification = Dense(
out_channels=2, activation_fn=None, in_layers=[weave_gather])
softmax = SoftMax(in_layers=[classification])
self.add_output(softmax)
label = Label(shape=(None, 2))
self.labels_fd.append(label)
cost = SoftMaxCrossEntropy(in_layers=[label, classification])
costs.append(cost)
if self.mode == "regression":
regression = Dense(
out_channels=1, activation_fn=None, in_layers=[weave_gather])
self.add_output(regression)
label = Label(shape=(None, 1))
self.labels_fd.append(label)
cost = L2Loss(in_layers=[label, regression])
costs.append(cost)
if self.mode == "classification":
all_cost = Concat(in_layers=costs, axis=1)
elif self.mode == "regression":
all_cost = Stack(in_layers=costs, axis=1)
self.weights = Weights(shape=(None, self.n_tasks))
loss = WeightedError(in_layers=[all_cost, self.weights])
self.set_loss(loss)