def test_dag_gather(self):
"""Test that DAGGather can be invoked."""
n_graph_feat = 2
n_outputs = 2
layer_sizes = [2]
init_method = 'one'
activation = 'sigmoid'
def sigmoid(x):
return 1 / (1 + np.exp(-x))
atom_features_np = np.array([[1, -1], [1, -1], [1, -1]])
membership_np = np.array([0, 0, 1])
expected_output = sigmoid(np.array([[1, 1], [1, 1]]))
with self.session() as sess:
atom_features_tf = tf.convert_to_tensor(atom_features_np,
dtype=tf.float32)
membership_tf = tf.convert_to_tensor(membership_np, dtype=tf.int32)
dag_gather = DAGGather(n_graph_feat=n_graph_feat,
n_outputs=n_outputs,
activation=activation,
init=init_method,
layer_sizes=layer_sizes)
dag_gather.create_tensor(
in_layers=[atom_features_tf, membership_tf, 0])
sess.run(tf.global_variables_initializer())
output = dag_gather.out_tensor.eval()
self.assertAllClose(output, expected_output)
self.assertEqual(output.shape, expected_output.shape)
def test_dag_gather(self):
"""Test that DAGGather can be invoked."""
n_graph_feat = 2
n_outputs = 2
layer_sizes = [2]
init_method = 'one'
activation = 'sigmoid'
def sigmoid(x):
return 1 / (1 + np.exp(-x))
atom_features_np = np.array([[1, -1], [1, -1], [1, -1]])
membership_np = np.array([0, 0, 1])
expected_output = sigmoid(np.array([[1, 1], [1, 1]]))
with self.session() as sess:
atom_features_tf = tf.convert_to_tensor(
atom_features_np, dtype=tf.float32)
membership_tf = tf.convert_to_tensor(membership_np, dtype=tf.int32)
dag_gather = DAGGather(
n_graph_feat=n_graph_feat,
n_outputs=n_outputs,
activation=activation,
init=init_method,
layer_sizes=layer_sizes)
dag_gather.create_tensor(in_layers=[atom_features_tf, membership_tf])
sess.run(tf.global_variables_initializer())
output = dag_gather.out_tensor.eval()
self.assertAllClose(output, expected_output)
self.assertEqual(output.shape, expected_output.shape)
def build_graph(self):
"""Building graph structures:
Features => DAGLayer => DAGGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.parents = Feature(shape=(None, self.max_atoms, self.max_atoms),
dtype=tf.int32)
self.calculation_orders = Feature(shape=(None, self.max_atoms),
dtype=tf.int32)
self.calculation_masks = Feature(shape=(None, self.max_atoms),
dtype=tf.bool)
self.membership = Feature(shape=(None, ), dtype=tf.int32)
self.n_atoms = Feature(shape=(), dtype=tf.int32)
dag_layer1 = DAGLayer(n_graph_feat=self.n_graph_feat,
n_atom_feat=self.n_atom_feat,
max_atoms=self.max_atoms,
batch_size=self.batch_size,
in_layers=[
self.atom_features, self.parents,
self.calculation_orders,
self.calculation_masks, self.n_atoms
])
dag_gather = DAGGather(n_graph_feat=self.n_graph_feat,
n_outputs=self.n_outputs,
max_atoms=self.max_atoms,
in_layers=[dag_layer1, self.membership])
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=[dag_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=[dag_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)
def test_DAGGather_pickle(): tg = TensorGraph() atom_features = Feature(shape=(None, 30)) membership = Feature(shape=(None,), dtype=tf.int32) Gather = DAGGather(in_layers=[atom_features, membership]) tg.add_output(Gather) tg.set_loss(Gather) tg.build() tg.save()
def build_graph(self):
"""Building graph structures:
Features => DAGLayer => DAGGather => Classification or Regression
"""
self.atom_features = Feature(shape=(None, self.n_atom_feat))
self.parents = Feature(shape=(None, self.max_atoms, self.max_atoms),
dtype=tf.int32)
self.calculation_orders = Feature(shape=(None, self.max_atoms),
dtype=tf.int32)
self.calculation_masks = Feature(shape=(None, self.max_atoms),
dtype=tf.bool)
self.membership = Feature(shape=(None, ), dtype=tf.int32)
self.n_atoms = Feature(shape=(), dtype=tf.int32)
dag_layer1 = DAGLayer(n_graph_feat=self.n_graph_feat,
n_atom_feat=self.n_atom_feat,
max_atoms=self.max_atoms,
layer_sizes=self.layer_sizes,
dropout=self.dropout,
batch_size=self.batch_size,
in_layers=[
self.atom_features, self.parents,
self.calculation_orders,
self.calculation_masks, self.n_atoms
])
dag_gather = DAGGather(n_graph_feat=self.n_graph_feat,
n_outputs=self.n_outputs,
max_atoms=self.max_atoms,
layer_sizes=self.layer_sizes_gather,
dropout=self.dropout,
in_layers=[dag_layer1, self.membership])
n_tasks = self.n_tasks
weights = Weights(shape=(None, n_tasks))
if self.mode == 'classification':
n_classes = self.n_classes
labels = Label(shape=(None, n_tasks, n_classes))
logits = Reshape(shape=(None, n_tasks, n_classes),
in_layers=[
Dense(in_layers=dag_gather,
out_channels=n_tasks * n_classes)
])
output = SoftMax(logits)
self.add_output(output)
loss = SoftMaxCrossEntropy(in_layers=[labels, logits])
weighted_loss = WeightedError(in_layers=[loss, weights])
self.set_loss(weighted_loss)
else:
labels = Label(shape=(None, n_tasks))
output = Reshape(
shape=(None, n_tasks),
in_layers=[Dense(in_layers=dag_gather, out_channels=n_tasks)])
self.add_output(output)
if self.uncertainty:
log_var = Reshape(shape=(None, n_tasks),
in_layers=[
Dense(in_layers=dag_gather,
out_channels=n_tasks)
])
var = Exp(log_var)
self.add_variance(var)
diff = labels - output
weighted_loss = weights * (diff * diff / var + log_var)
weighted_loss = ReduceSum(ReduceMean(weighted_loss, axis=[1]))
else:
weighted_loss = ReduceSum(
L2Loss(in_layers=[labels, output, weights]))
self.set_loss(weighted_loss)