Loading deepchem/models/graph_models.py +1 −1 Original line number Original line Diff line number Diff line Loading @@ -635,7 +635,7 @@ class _GraphConvKerasModel(tf.keras.Model): if self.batch_norms[-1] is not None: if self.batch_norms[-1] is not None: dense = self.batch_norms[-1](dense, training=training) dense = self.batch_norms[-1](dense, training=training) if training and self.dropouts[-1] is not None: if training and self.dropouts[-1] is not None: dense = self.dropouts[1](dense, training=training) dense = self.dropouts[-1](dense, training=training) neural_fingerprint = self.graph_gather([dense, degree_slice, membership] + neural_fingerprint = self.graph_gather([dense, degree_slice, membership] + deg_adjs) deg_adjs) if self.mode == 'classification': if self.mode == 'classification': Loading Loading
deepchem/models/graph_models.py +1 −1 Original line number Original line Diff line number Diff line Loading @@ -635,7 +635,7 @@ class _GraphConvKerasModel(tf.keras.Model): if self.batch_norms[-1] is not None: if self.batch_norms[-1] is not None: dense = self.batch_norms[-1](dense, training=training) dense = self.batch_norms[-1](dense, training=training) if training and self.dropouts[-1] is not None: if training and self.dropouts[-1] is not None: dense = self.dropouts[1](dense, training=training) dense = self.dropouts[-1](dense, training=training) neural_fingerprint = self.graph_gather([dense, degree_slice, membership] + neural_fingerprint = self.graph_gather([dense, degree_slice, membership] + deg_adjs) deg_adjs) if self.mode == 'classification': if self.mode == 'classification': Loading
