Loading deepchem/models/tensorgraph/models/graph_models.py +958 −957 Original line number Diff line number Diff line Loading @@ -342,7 +342,8 @@ class DTNNTensorGraph(TensorGraph): if outputs is None: outputs = self.outputs if transformers != [] and not isinstance(outputs, collections.Sequence): raise ValueError("DTNN does not support single tensor output with transformers") raise ValueError( "DTNN does not support single tensor output with transformers") retval = super(DTNNTensorGraph, self).predict(dataset, outputs=outputs) if not isinstance(outputs, collections.Sequence): return retval Loading examples/chembl/chembl_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -30,7 +30,8 @@ n_feat = 75 # Batch size of models batch_size = 128 model = GraphConvTensorGraph(len(chembl_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(chembl_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=20) Loading examples/clintox/clintox_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -31,7 +31,8 @@ metric = dc.metrics.Metric( n_feat = 75 # Batch size of models batch_size = 50 model = GraphConvTensorGraph(len(clintox_tasks), batch_size=batch_size, mode='classification') model = GraphConvTensorGraph( len(clintox_tasks), batch_size=batch_size, mode='classification') # Fit trained model model.fit(train_dataset, nb_epoch=10) Loading examples/delaney/delaney_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -29,7 +29,8 @@ metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean) n_feat = 75 # Batch size of models batch_size = 128 model = GraphConvTensorGraph(len(delaney_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(delaney_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=20) Loading examples/hopv/hopv_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -53,7 +53,8 @@ model = dc.models.MultitaskGraphRegressor( beta1=.9, beta2=.999) model = GraphConvTensorGraph(len(hopv_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(hopv_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=25) Loading Loading
deepchem/models/tensorgraph/models/graph_models.py +958 −957 Original line number Diff line number Diff line Loading @@ -342,7 +342,8 @@ class DTNNTensorGraph(TensorGraph): if outputs is None: outputs = self.outputs if transformers != [] and not isinstance(outputs, collections.Sequence): raise ValueError("DTNN does not support single tensor output with transformers") raise ValueError( "DTNN does not support single tensor output with transformers") retval = super(DTNNTensorGraph, self).predict(dataset, outputs=outputs) if not isinstance(outputs, collections.Sequence): return retval Loading
examples/chembl/chembl_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -30,7 +30,8 @@ n_feat = 75 # Batch size of models batch_size = 128 model = GraphConvTensorGraph(len(chembl_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(chembl_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=20) Loading
examples/clintox/clintox_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -31,7 +31,8 @@ metric = dc.metrics.Metric( n_feat = 75 # Batch size of models batch_size = 50 model = GraphConvTensorGraph(len(clintox_tasks), batch_size=batch_size, mode='classification') model = GraphConvTensorGraph( len(clintox_tasks), batch_size=batch_size, mode='classification') # Fit trained model model.fit(train_dataset, nb_epoch=10) Loading
examples/delaney/delaney_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -29,7 +29,8 @@ metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean) n_feat = 75 # Batch size of models batch_size = 128 model = GraphConvTensorGraph(len(delaney_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(delaney_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=20) Loading
examples/hopv/hopv_graph_conv.py +2 −1 Original line number Diff line number Diff line Loading @@ -53,7 +53,8 @@ model = dc.models.MultitaskGraphRegressor( beta1=.9, beta2=.999) model = GraphConvTensorGraph(len(hopv_tasks), batch_size=batch_size, mode='regression') model = GraphConvTensorGraph( len(hopv_tasks), batch_size=batch_size, mode='regression') # Fit trained model model.fit(train_dataset, nb_epoch=25) Loading
