Added docstrings (829e5157) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+44 −5

Original line number	Diff line number	Diff line
		@@ -3126,6 +3126,10 @@ class PassThroughLayer(Layer):


		class GraphCNNPoolLayer(Layer):
		"""
		GraphCNNPool Layer from Robust Spatial Filtering with Graph Convolutional Neural Networks
		https://arxiv.org/abs/1703.00792
		"""

		def __init__(self, num_vertices, **kwargs):
		self.num_vertices = num_vertices
		@@ -3133,15 +3137,25 @@ class GraphCNNPoolLayer(Layer):

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		"""

		Parameters
		----------
		in_layers
		set_tensors
		kwargs
		num_filters: int
		Number of filters to have in the output

		Returns
		-------
		in_layers: list of Layers or tensors
		[V, A, mask]
		V are the vertex features must be of shape (batch, vertex, channel)

		A are the adjacency matrixes for each graph
		Shape (batch, from_vertex, adj_matrix, to_vertex)

		mask is optional, to be used when not every graph has the
		same number of vertices

		Returns: tf.tensor
		Returns a tf.tensor with a graph convolution applied
		The shape will be (batch, vertex, self.num_filters)
		"""
		in_tensors = self._get_input_tensors(in_layers)
		if len(in_tensors) == 3:
		@@ -3210,8 +3224,33 @@ def GraphCNNPool(num_vertices, **kwargs):


		class GraphCNN(Layer):
		"""
		GraphCNN Layer from Robust Spatial Filtering with Graph Convolutional Neural Networks
		https://arxiv.org/abs/1703.00792
		"""

		def __init__(self, num_filters, **kwargs):
		"""

		Parameters
		----------
		num_filters: int
		Number of filters to have in the output

		in_layers: list of Layers or tensors
		[V, A, mask]
		V are the vertex features must be of shape (batch, vertex, channel)

		A are the adjacency matrixes for each graph
		Shape (batch, from_vertex, adj_matrix, to_vertex)

		mask is optional, to be used when not every graph has the
		same number of vertices

		Returns: tf.tensor
		Returns a tf.tensor with a graph convolution applied
		The shape will be (batch, vertex, self.num_filters)
		"""
		self.num_filters = num_filters
		super(GraphCNN, self).__init__(**kwargs)

deepchem/models/tensorgraph/models/graph_models.py

+4 −0

Original line number	Diff line number	Diff line
		@@ -487,6 +487,10 @@ class DAGTensorGraph(TensorGraph):


		class PetroskiSuchTensorGraph(TensorGraph):
		"""
		Model from Robust Spatial Filtering with Graph Convolutional Neural Networks
		https://arxiv.org/abs/1703.00792
		"""

		def __init__(self,
		n_tasks,

deepchem/models/tensorgraph/tests/test_layers.py

+3 −3

Original line number	Diff line number	Diff line
		@@ -725,7 +725,7 @@ class TestLayers(test_util.TensorFlowTestCase):
		assert output_tensor.eval() == 40.0

		def test_graphcnn(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		""" Test GraphCNN Layer From https://arxiv.org/abs/1703.00792"""
		V = np.random.uniform(size=(10, 100, 50)).astype(np.float32)
		adjs = np.random.uniform(size=(10, 100, 5, 100)).astype(np.float32)
		with self.test_session() as sess:
		@@ -736,7 +736,7 @@ class TestLayers(test_util.TensorFlowTestCase):
		assert result.shape == (10, 100, 6)

		def test_graphcnnpool(self):
		"""Test that layers can automatically reshape inconsistent inputs."""
		""" Test GraphCNNPool Layer From https://arxiv.org/abs/1703.00792"""
		V = np.random.uniform(size=(10, 100, 50)).astype(np.float32)
		adjs = np.random.uniform(size=(10, 100, 5, 100)).astype(np.float32)
		with self.test_session() as sess:

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