Loading deepchem/models/layers.py +23 −1 Original line number Diff line number Diff line Loading @@ -437,6 +437,12 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- tuple(tf.Tensor,tf.Tensor,tf.Tensor) First and second are original input tensors Third is the result of convolution """ ic = len(inputs) Loading Loading @@ -531,6 +537,11 @@ class GraphAggregationLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- aggregation tensor: tf.Tensor Result of aggregation function on input convolution tensor. """ i = self.d1(inputs) Loading Loading @@ -621,6 +632,11 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- convolution tensor: tf.Tensor Result of input tensors going through convolution a number of times. """ adjacency_tensor = inputs[0] Loading Loading @@ -714,6 +730,12 @@ class GraphEncoderLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- encoder tensor: tf.Tensor Tensor that been through number of convolutions followed by aggregation. """ output = self.multi_graph_convolution_layer(inputs) Loading deepchem/models/tests/test_molgan_layers.py +1 −1 Original line number Diff line number Diff line Loading @@ -67,7 +67,7 @@ class test_molgan_layers(unittest.TestCase): assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_graph_encoder_later(self): def test_graph_encoder_layer(self): vertices = 9 nodes = 5 edges = 5 Loading Loading
deepchem/models/layers.py +23 −1 Original line number Diff line number Diff line Loading @@ -437,6 +437,12 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- tuple(tf.Tensor,tf.Tensor,tf.Tensor) First and second are original input tensors Third is the result of convolution """ ic = len(inputs) Loading Loading @@ -531,6 +537,11 @@ class GraphAggregationLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- aggregation tensor: tf.Tensor Result of aggregation function on input convolution tensor. """ i = self.d1(inputs) Loading Loading @@ -621,6 +632,11 @@ class MultiGraphConvolutionLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- convolution tensor: tf.Tensor Result of input tensors going through convolution a number of times. """ adjacency_tensor = inputs[0] Loading Loading @@ -714,6 +730,12 @@ class GraphEncoderLayer(tf.keras.layers.Layer): training: bool Should this layer be run in training mode. Typically decided by main model, influences things like dropout. Returns -------- encoder tensor: tf.Tensor Tensor that been through number of convolutions followed by aggregation. """ output = self.multi_graph_convolution_layer(inputs) Loading
deepchem/models/tests/test_molgan_layers.py +1 −1 Original line number Diff line number Diff line Loading @@ -67,7 +67,7 @@ class test_molgan_layers(unittest.TestCase): assert layer.edges == 5 assert layer.dropout_rate == 0.0 def test_graph_encoder_later(self): def test_graph_encoder_layer(self): vertices = 9 nodes = 5 edges = 5 Loading
