Loading deepchem/models/layers.py +28 −28 Original line number Diff line number Diff line Loading @@ -374,7 +374,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ Graph convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Not used directly but used by higher level layers like MultiGraphConvolutionLayer. Not used directly, higher level layers like MultiGraphConvolutionLayer. References ---------- Loading @@ -384,7 +384,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, edges=5, name="", Loading @@ -396,8 +396,8 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): --------- units: int Dimesion of dense layers used for convolution activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=Tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Dropout rate used by dropout layer edges: int, optional (default=5) Loading @@ -416,7 +416,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): self.dense1 = [Dense(units=self.units) for _ in range(edges - 1)] self.dense2 = Dense(units=self.units) self.dropout = Dropout(self.dropout_rate) self.activation = Activation(self.activation) self.activation_layer = Activation(self.activation) def call(self, inputs, training=False): """ Loading Loading @@ -451,7 +451,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): output = tf.matmul(adj, output) output = tf.reduce_sum(output, 1) + self.dense2(node_tensor) output = self.activation(output) output = self.activation_layer(output) output = self.dropout(output) return adjacency_tensor, node_tensor, output Loading Loading @@ -481,7 +481,7 @@ class GraphAggregationLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, name="", **kwargs): Loading @@ -492,8 +492,8 @@ class GraphAggregationLayer(tf.keras.layers.Layer): --------- units: int Dimesion of dense layers used for aggregation activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=Tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Used by dropout layer name: string, optional (default="") Loading Loading @@ -558,7 +558,7 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, edges=5, name="", Loading @@ -571,8 +571,8 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): units: list, min_length=2 List of dimensions used by consecutive convolution layers. The more values the more convolution layers invoked. activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Used by dropout layer edges: int, optional (default=0) Loading deepchem/models/tests/test_molgan_layers.py 0 → 100644 +96 −0 Original line number Diff line number Diff line import unittest from tensorflow import keras from tensorflow.keras.layers import Input from deepchem.models.layers import GraphConvolutionLayer, MultiGraphConvolutionLayer, GraphAggregationLayer, GraphEncoderLayer class test_molgan_layers(unittest.TestCase): """ Unit testing for MolGAN basic layers """ def test_graph_convolution_layer(self): vertices = 9 nodes = 5 edges = 5 units = 128 layer = GraphConvolutionLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) output = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[output]) assert model.output_shape == [((None, vertices, vertices, edges), (None, vertices, nodes), (None, vertices, units))] assert layer.units == units assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_aggregation_layer(self): vertices = 9 units = 128 layer = GraphAggregationLayer(units=units) hidden_tensor = Input(shape=(vertices, units)) output = layer(hidden_tensor) model = keras.Model(inputs=[hidden_tensor], outputs=[output]) assert model.output_shape == (None, units) assert layer.units == units assert layer.activation == 'tanh' assert layer.dropout_rate == 0.0 def test_multigraph_convolution_layer(self): vertices = 9 nodes = 5 edges = 5 first_convolution_unit = 128 second_convolution_unit = 64 units = [first_convolution_unit, second_convolution_unit] layer = MultiGraphConvolutionLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) hidden_tensor = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[hidden_tensor]) assert model.output_shape == (None, vertices, second_convolution_unit) assert layer.units == units assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_graph_encoder_later(self): vertices = 9 nodes = 5 edges = 5 first_convolution_unit = 128 second_convolution_unit = 64 aggregation_unit = 128 units = [(first_convolution_unit, second_convolution_unit), aggregation_unit] layer = GraphEncoderLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) output = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[output]) assert model.output_shape == (None, aggregation_unit) assert layer.graph_convolution_units == (first_convolution_unit, second_convolution_unit) assert layer.auxiliary_units == aggregation_unit assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 if __name__ == '__main__': unittest.main() Loading
deepchem/models/layers.py +28 −28 Original line number Diff line number Diff line Loading @@ -374,7 +374,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): """ Graph convolution layer used in MolGAN model. MolGAN is a WGAN type model for generation of small molecules. Not used directly but used by higher level layers like MultiGraphConvolutionLayer. Not used directly, higher level layers like MultiGraphConvolutionLayer. References ---------- Loading @@ -384,7 +384,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, edges=5, name="", Loading @@ -396,8 +396,8 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): --------- units: int Dimesion of dense layers used for convolution activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=Tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Dropout rate used by dropout layer edges: int, optional (default=5) Loading @@ -416,7 +416,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): self.dense1 = [Dense(units=self.units) for _ in range(edges - 1)] self.dense2 = Dense(units=self.units) self.dropout = Dropout(self.dropout_rate) self.activation = Activation(self.activation) self.activation_layer = Activation(self.activation) def call(self, inputs, training=False): """ Loading Loading @@ -451,7 +451,7 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): output = tf.matmul(adj, output) output = tf.reduce_sum(output, 1) + self.dense2(node_tensor) output = self.activation(output) output = self.activation_layer(output) output = self.dropout(output) return adjacency_tensor, node_tensor, output Loading Loading @@ -481,7 +481,7 @@ class GraphAggregationLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, name="", **kwargs): Loading @@ -492,8 +492,8 @@ class GraphAggregationLayer(tf.keras.layers.Layer): --------- units: int Dimesion of dense layers used for aggregation activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=Tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Used by dropout layer name: string, optional (default="") Loading Loading @@ -558,7 +558,7 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): def __init__(self, units, activation=None, activation="tanh", dropout_rate=0.0, edges=5, name="", Loading @@ -571,8 +571,8 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): units: list, min_length=2 List of dimensions used by consecutive convolution layers. The more values the more convolution layers invoked. activation: function, optional (default=None) Tanh is default option provided by GraphEncoderLayer activation: function, optional (default=tanh) activation function used across model, default is Tanh dropout_rate: float, optional (default=0.0) Used by dropout layer edges: int, optional (default=0) Loading
deepchem/models/tests/test_molgan_layers.py 0 → 100644 +96 −0 Original line number Diff line number Diff line import unittest from tensorflow import keras from tensorflow.keras.layers import Input from deepchem.models.layers import GraphConvolutionLayer, MultiGraphConvolutionLayer, GraphAggregationLayer, GraphEncoderLayer class test_molgan_layers(unittest.TestCase): """ Unit testing for MolGAN basic layers """ def test_graph_convolution_layer(self): vertices = 9 nodes = 5 edges = 5 units = 128 layer = GraphConvolutionLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) output = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[output]) assert model.output_shape == [((None, vertices, vertices, edges), (None, vertices, nodes), (None, vertices, units))] assert layer.units == units assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_aggregation_layer(self): vertices = 9 units = 128 layer = GraphAggregationLayer(units=units) hidden_tensor = Input(shape=(vertices, units)) output = layer(hidden_tensor) model = keras.Model(inputs=[hidden_tensor], outputs=[output]) assert model.output_shape == (None, units) assert layer.units == units assert layer.activation == 'tanh' assert layer.dropout_rate == 0.0 def test_multigraph_convolution_layer(self): vertices = 9 nodes = 5 edges = 5 first_convolution_unit = 128 second_convolution_unit = 64 units = [first_convolution_unit, second_convolution_unit] layer = MultiGraphConvolutionLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) hidden_tensor = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[hidden_tensor]) assert model.output_shape == (None, vertices, second_convolution_unit) assert layer.units == units assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_graph_encoder_later(self): vertices = 9 nodes = 5 edges = 5 first_convolution_unit = 128 second_convolution_unit = 64 aggregation_unit = 128 units = [(first_convolution_unit, second_convolution_unit), aggregation_unit] layer = GraphEncoderLayer(units=units, edges=edges) adjacency_tensor = Input(shape=(vertices, vertices, edges)) node_tensor = Input(shape=(vertices, nodes)) output = layer([adjacency_tensor, node_tensor]) model = keras.Model( inputs=[adjacency_tensor, node_tensor], outputs=[output]) assert model.output_shape == (None, aggregation_unit) assert layer.graph_convolution_units == (first_convolution_unit, second_convolution_unit) assert layer.auxiliary_units == aggregation_unit assert layer.activation == 'tanh' assert layer.edges == 5 assert layer.dropout_rate == 0.0 if __name__ == '__main__': unittest.main()
