Commit bec964f5 authored by Milosz Grabski's avatar Milosz Grabski
Browse files

fixed doctest errors

parent 370b0f12

deepchem/models/layers.py

+26 −19
Original line number Diff line number Diff line
@@ -387,17 +387,20 @@ class MolGANConvolutionLayer(tf.keras.layers.Layer): 
  --------

  See: MolGANMultiConvolutionLayer for using in layers.



  >>> from tensorflow.keras import Model

  >>> from tensorflow.keras.layers import Input

  >>> vertices = 9

  >>> nodes = 5

  >>> edges = 5

  >>> units = 128



  >>> layer = MolGANConvolutionLayer(units=units,edges=edges)

  >>> adjacency_tensor= tensorflow.keras.layers.Input(shape=(vertices, vertices, edges))

  >>> node_tensor = tensorflow.keras.layers.Input(shape=(vertices,nodes))

  >>> hidden1 = layer([adjacency_tensor,node_tensor])

  >>> output = layer(hidden1)

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

  >>> layer1 = MolGANConvolutionLayer(units=units,edges=edges)

  >>> layer2 = MolGANConvolutionLayer(units=units,edges=edges)

  >>> adjacency_tensor= Input(shape=(vertices, vertices, edges))

  >>> node_tensor = Input(shape=(vertices,nodes))

  >>> hidden1 = layer1([adjacency_tensor,node_tensor])

  >>> output = layer2(hidden1)

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



  References

  ----------
@@ -508,6 +511,8 @@ class MolGANAggregationLayer(tf.keras.layers.Layer): 


  Example

  --------

  >>> from tensorflow.keras import Model

  >>> from tensorflow.keras.layers import Input

  >>> vertices = 9

  >>> nodes = 5

  >>> edges = 5
@@ -516,12 +521,12 @@ class MolGANAggregationLayer(tf.keras.layers.Layer): 
  >>> layer_1 = MolGANConvolutionLayer(units=units,edges=edges)

  >>> layer_2 = MolGANConvolutionLayer(units=units,edges=edges)

  >>> layer_3 = MolGANAggregationLayer(units=128)

  >>> adjacency_tensor= tensorflow.keras.layers.Input(shape=(vertices, vertices, edges))

  >>> node_tensor = tensorflow.keras.layers.Input(shape=(vertices,nodes))

  >>> adjacency_tensor= Input(shape=(vertices, vertices, edges))

  >>> node_tensor = 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])

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



  References

  ----------
@@ -609,6 +614,8 @@ class MolGANMultiConvolutionLayer(tf.keras.layers.Layer): 


  Example

  --------

  >>> from tensorflow.keras import Model

  >>> from tensorflow.keras.layers import Input

  >>> vertices = 9

  >>> nodes = 5

  >>> edges = 5
@@ -616,11 +623,11 @@ class MolGANMultiConvolutionLayer(tf.keras.layers.Layer): 


  >>> layer_1 = MolGANMultiConvolutionLayer(units=(128,64))

  >>> layer_2 = MolGANAggregationLayer(units=128)

  >>> adjacency_tensor= tensorflow.keras.layers.Input(shape=(vertices, vertices, edges))

  >>> node_tensor = tensorflow.keras.layers.Input(shape=(vertices,nodes))

  >>> adjacency_tensor= Input(shape=(vertices, vertices, edges))

  >>> node_tensor = 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])

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



  References

  ----------
@@ -723,23 +730,23 @@ class MolGANEncoderLayer(tf.keras.layers.Layer): 


  Example

  --------



  >>> from tensorflow.keras import Model

  >>> from tensorflow.keras.layers import Input, Dropout,Dense

  >>> vertices = 9

  >>> edges = 5

  >>> nodes = 5

  >>> dropout_rate = .0

  >>> adjacency_tensor= tensorflow.keras.layers.Input(shape=(vertices, vertices, edges))

  >>> node_tensor = tensorflow.keras.layers.Input(shape=(vertices, nodes))

  >>> adjacency_tensor= Input(shape=(vertices, vertices, edges))

  >>> node_tensor = Input(shape=(vertices, nodes))



  >>> graph = MolGANEncoderLayer(units = [(128,64),128],

  >>>                           dropout_rate= dropout_rate,

  >>>                           edges=edges)([adjacency_tensor,node_tensor])

  >>> graph = MolGANEncoderLayer(units = [(128,64),128], dropout_rate= dropout_rate, edges=edges)([adjacency_tensor,node_tensor])

  >>> dense = Dense(units=128, activation='tanh')(graph)

  >>> dense = Dropout(dropout_rate)(dense)

  >>> dense = Dense(units=64, activation='tanh')(dense)

  >>> dense = Dropout(dropout_rate)(dense)

  >>> output = Dense(units=1)(dense)



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

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



  References

  ----------

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