def test_dtnn_extract(self):
"""Test that DTNNExtract can be invoked."""
num_samples = 20
num_features = 30
task_id = 15
test_tensor_input = np.random.randn(num_samples, num_features)
test_output = test_tensor_input[:, task_id:task_id + 1]
with self.session() as sess:
test_tensor = tf.convert_to_tensor(test_tensor_input)
dtnn_extract = DTNNExtract(task_id=task_id)
dtnn_extract.create_tensor(in_layers=[test_tensor])
sess.run(tf.global_variables_initializer())
out_tensor = dtnn_extract.out_tensor.eval()
self.assertAllClose(test_output, out_tensor)
self.assertEqual(out_tensor.shape, (num_samples, 1))
def test_dtnn_extract(self):
"""Test that DTNNExtract can be invoked."""
num_samples = 20
num_features = 30
task_id = 15
test_tensor_input = np.random.randn(num_samples, num_features)
test_output = test_tensor_input[:, task_id:task_id + 1]
with self.session() as sess:
test_tensor = tf.convert_to_tensor(test_tensor_input)
dtnn_extract = DTNNExtract(task_id=task_id)
dtnn_extract.create_tensor(in_layers=[test_tensor])
sess.run(tf.global_variables_initializer())
out_tensor = dtnn_extract.out_tensor.eval()
self.assertAllClose(test_output, out_tensor)
self.assertEqual(out_tensor.shape, (num_samples, 1))
def test_DTNNExtract_pickle(): tg = TensorGraph() atom_features = Feature(shape=(None, 30)) Ext = DTNNExtract(0, in_layers=[atom_features]) tg.add_output(Ext) tg.set_loss(Ext) tg.build() tg.save()
def build_graph(self):
"""Building graph structures:
Features => DTNNEmbedding => DTNNStep => DTNNStep => DTNNGather => Regression
"""
self.atom_number = Feature(shape=(None,), dtype=tf.int32)
self.distance = Feature(shape=(None, self.n_distance))
self.atom_membership = Feature(shape=(None,), dtype=tf.int32)
self.distance_membership_i = Feature(shape=(None,), dtype=tf.int32)
self.distance_membership_j = Feature(shape=(None,), dtype=tf.int32)
dtnn_embedding = DTNNEmbedding(
n_embedding=self.n_embedding, in_layers=[self.atom_number])
dtnn_layer1 = DTNNStep(
n_embedding=self.n_embedding,
n_distance=self.n_distance,
in_layers=[
dtnn_embedding, self.distance, self.distance_membership_i,
self.distance_membership_j
])
dtnn_layer2 = DTNNStep(
n_embedding=self.n_embedding,
n_distance=self.n_distance,
in_layers=[
dtnn_layer1, self.distance, self.distance_membership_i,
self.distance_membership_j
])
dtnn_gather = DTNNGather(
n_embedding=self.n_embedding,
layer_sizes=[self.n_hidden],
n_outputs=self.n_tasks,
output_activation=self.output_activation,
in_layers=[dtnn_layer2, self.atom_membership])
costs = []
self.labels_fd = []
for task in range(self.n_tasks):
regression = DTNNExtract(task, in_layers=[dtnn_gather])
self.add_output(regression)
label = Label(shape=(None, 1))
self.labels_fd.append(label)
cost = L2Loss(in_layers=[label, regression])
costs.append(cost)
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)