Loading deepchem/models/layers.py +36 −20 Original line number Diff line number Diff line Loading @@ -383,6 +383,22 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): hidden_layer and it hold results of the convolution while first two are unchanged input tensors. Example -------- See: MultiGraphConvolutionLayer for using in layers. vertices = 9 nodes = 5 edges = 5 units = 128 layer = GraphConvolutionLayer(units=units,edges=edges) adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices,nodes)) hidden1 = layer([adjacency_tensor,node_tensor]) output = layer(hidden1) model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) References ---------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model Loading Loading @@ -672,6 +688,26 @@ class GraphEncoderLayer(tf.keras.layers.Layer): This layer can be manually built by stacking graph convolution layers followed by graph aggregation. Example -------- def create_discriminator(adjacency_tensor, node_tensor): vertices = 9 edges = 5 dropout_rate = .0 adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices, nodes)) graph = GraphEncoderLayer(units = [(128,64),128], dropout_rate= dropout_rate, edges=edges)([adjacency_tensor,node_tensor]) dense = layers.Dense(units=128, activation='tanh')(graph) dense = layers.Dropout(dropout_rate)(dense) dense = layers.Dense(units=64, activation='tanh')(dense) dense = layers.Dropout(dropout_rate)(dense) output = layers.Dense(units=1)(dense) return keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) References ---------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model Loading Loading @@ -703,26 +739,6 @@ class GraphEncoderLayer(tf.keras.layers.Layer): Typically matches number of bond types used in the molecule. name: string, optional (default="") Name of the layer Example -------- def create_discriminator(adjacency_tensor, node_tensor): vertices = 9 edges = 5 dropout_rate = .0 adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices, nodes)) graph = GraphEncoderLayer(units = [(128,64),128], dropout_rate= dropout_rate, edges=edges)([adjacency_tensor,node_tensor]) dense = layers.Dense(units=128, activation='tanh')(graph) dense = layers.Dropout(dropout_rate)(dense) dense = layers.Dense(units=64, activation='tanh')(dense) dense = layers.Dropout(dropout_rate)(dense) output = layers.Dense(units=1)(dense) return keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) """ super(GraphEncoderLayer, self).__init__(name=name, **kwargs) Loading Loading
deepchem/models/layers.py +36 −20 Original line number Diff line number Diff line Loading @@ -383,6 +383,22 @@ class GraphConvolutionLayer(tf.keras.layers.Layer): hidden_layer and it hold results of the convolution while first two are unchanged input tensors. Example -------- See: MultiGraphConvolutionLayer for using in layers. vertices = 9 nodes = 5 edges = 5 units = 128 layer = GraphConvolutionLayer(units=units,edges=edges) adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices,nodes)) hidden1 = layer([adjacency_tensor,node_tensor]) output = layer(hidden1) model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) References ---------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model Loading Loading @@ -672,6 +688,26 @@ class GraphEncoderLayer(tf.keras.layers.Layer): This layer can be manually built by stacking graph convolution layers followed by graph aggregation. Example -------- def create_discriminator(adjacency_tensor, node_tensor): vertices = 9 edges = 5 dropout_rate = .0 adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices, nodes)) graph = GraphEncoderLayer(units = [(128,64),128], dropout_rate= dropout_rate, edges=edges)([adjacency_tensor,node_tensor]) dense = layers.Dense(units=128, activation='tanh')(graph) dense = layers.Dropout(dropout_rate)(dense) dense = layers.Dense(units=64, activation='tanh')(dense) dense = layers.Dropout(dropout_rate)(dense) output = layers.Dense(units=1)(dense) return keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) References ---------- .. [1] Nicola De Cao et al. "MolGAN: An implicit generative model Loading Loading @@ -703,26 +739,6 @@ class GraphEncoderLayer(tf.keras.layers.Layer): Typically matches number of bond types used in the molecule. name: string, optional (default="") Name of the layer Example -------- def create_discriminator(adjacency_tensor, node_tensor): vertices = 9 edges = 5 dropout_rate = .0 adjacency_tensor= layers.Input(shape=(vertices, vertices, edges)) node_tensor = layers.Input(shape=(vertices, nodes)) graph = GraphEncoderLayer(units = [(128,64),128], dropout_rate= dropout_rate, edges=edges)([adjacency_tensor,node_tensor]) dense = layers.Dense(units=128, activation='tanh')(graph) dense = layers.Dropout(dropout_rate)(dense) dense = layers.Dense(units=64, activation='tanh')(dense) dense = layers.Dropout(dropout_rate)(dense) output = layers.Dense(units=1)(dense) return keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output]) """ super(GraphEncoderLayer, self).__init__(name=name, **kwargs) Loading
