fixed examples (4a9e661b) · Commits · 钟慕尧 / deepchem

deepchem/models/layers.py

+43 −43

Original line number	Diff line number	Diff line
		@@ -508,20 +508,20 @@ class MolGANAggregationLayer(tf.keras.layers.Layer):

		Example
		--------
		vertices = 9
		nodes = 5
		edges = 5
		units = 128

		layer_1 = MolGANConvolutionLayer(units=units,edges=edges)
		layer_2 = MolGANConvolutionLayer(units=units,edges=edges)
		layer_3 = MolGANAggregationLayer(units=128)
		adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		node_tensor = layers.Input(shape=(vertices,nodes))
		hidden_1 = layer_1([adjacency_tensor,node_tensor])
		hidden_2 = layer_2(hidden_1)
		output = layer_3(hidden_2[2])
		model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])
		>>> vertices = 9
		>>> nodes = 5
		>>> edges = 5
		>>> units = 128

		>>> layer_1 = MolGANConvolutionLayer(units=units,edges=edges)
		>>> layer_2 = MolGANConvolutionLayer(units=units,edges=edges)
		>>> layer_3 = MolGANAggregationLayer(units=128)
		>>> adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		>>> node_tensor = layers.Input(shape=(vertices,nodes))
		>>> hidden_1 = layer_1([adjacency_tensor,node_tensor])
		>>> hidden_2 = layer_2(hidden_1)
		>>> output = layer_3(hidden_2[2])
		>>> model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])

		References
		----------
		@@ -609,18 +609,18 @@ class MolGANMultiConvolutionLayer(tf.keras.layers.Layer):

		Example
		--------
		vertices = 9
		nodes = 5
		edges = 5
		units = 128

		layer_1 = MolGANMultiConvolutionLayer(units=(128,64))
		layer_2 = MolGANAggregationLayer(units=128)
		adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		node_tensor = layers.Input(shape=(vertices,nodes))
		hidden = layer_1([adjacency_tensor,node_tensor])
		output = layer_2(hidden)
		model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])
		>>> vertices = 9
		>>> nodes = 5
		>>> edges = 5
		>>> units = 128

		>>> layer_1 = MolGANMultiConvolutionLayer(units=(128,64))
		>>> layer_2 = MolGANAggregationLayer(units=128)
		>>> adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		>>> node_tensor = layers.Input(shape=(vertices,nodes))
		>>> hidden = layer_1([adjacency_tensor,node_tensor])
		>>> output = layer_2(hidden)
		>>> model = keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])

		References
		----------
		@@ -724,22 +724,22 @@ class MolGANEncoderLayer(tf.keras.layers.Layer):
		Example
		--------

		vertices = 9
		edges = 5
		dropout_rate = .0
		adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		node_tensor = layers.Input(shape=(vertices, nodes))

		graph = MolGANEncoderLayer(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)

		keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])
		>>> vertices = 9
		>>> edges = 5
		>>> dropout_rate = .0
		>>> adjacency_tensor= layers.Input(shape=(vertices, vertices, edges))
		>>> node_tensor = layers.Input(shape=(vertices, nodes))

		>>> graph = MolGANEncoderLayer(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)

		>>> keras.Model(inputs=[adjacency_tensor,node_tensor], outputs=[output])

		References
		----------

Admin message