update for keras 3.0

kgcnn/layers/aggr.py

@@ -28,6 +28,15 @@ def __init__(self, pooling_method: str = "scatter_sum", axis=0, **kwargs):
             axis (int): Axis to aggregate. Default is 0.
         """
         super(Aggregate, self).__init__(**kwargs)
+        # Shorthand notation
+        if pooling_method == "sum":
+            pooling_method = "scatter_sum"
+        if pooling_method == "max":
+            pooling_method = "scatter_max"
+        if pooling_method == "min":
+            pooling_method = "scatter_min"
+        if pooling_method == "mean":
+            pooling_method = "scatter_mean"
         self.pooling_method = pooling_method
         self.axis = axis
         if axis != 0:

kgcnn/literature/AttentiveFP/_make.py

@@ -74,14 +74,14 @@ def make_model(inputs: list = None,
     r"""Make `AttentiveFP <https://doi.org/10.1021/acs.jmedchem.9b00959>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.AttentiveFP.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, ...]`
     with '...' indicating mask or id tensors following the template below:
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/DMPNN/_make.py

@@ -84,7 +84,7 @@ def make_model(name: str = None,
     r"""Make `DMPNN <https://pubs.acs.org/doi/full/10.1021/acs.jcim.9b00237>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.DMPNN.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, reverse_indices, (graph_state), ...]`
     with '...' indicating mask or id tensors following the template below.
@@ -93,7 +93,7 @@ def make_model(name: str = None,
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/GAT/_make.py

@@ -74,14 +74,14 @@ def make_model(inputs: list = None,
     r"""Make `GAT <https://arxiv.org/abs/1710.10903>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.GAT.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, ...]`
     with '...' indicating mask or ID tensors following the template below:
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/GATv2/_make.py

@@ -76,14 +76,14 @@ def make_model(inputs: list = None,
     r"""Make `GATv2 <https://arxiv.org/abs/2105.14491>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.GATv2.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, ...]`
     with '...' indicating mask or ID tensors following the template below:
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/GCN/_make.py

@@ -71,15 +71,15 @@ def make_model(inputs: list = None,
     r"""Make `GCN <https://arxiv.org/abs/1609.02907>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.GCN.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, ...]`
     with '...' indicating mask or ID tensors following the template below.
     Edges are actually edge single weight values which are entries of the pre-scaled adjacency matrix.
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/GIN/_make.py

@@ -70,14 +70,14 @@ def make_model(inputs: list = None,
     r"""Make `GIN <https://arxiv.org/abs/1810.00826>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.GIN.model_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edge_indices, ...]`
     with '...' indicating mask or ID tensors following the template below:
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/GraphSAGE/_make.py

@@ -76,15 +76,15 @@ def make_model(inputs: list = None,
     r"""Make `GraphSAGE <http://arxiv.org/abs/1706.02216>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.GraphSAGE.model_default` .
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, edges, edge_indices, ...]`
     with '...' indicating mask or ID tensors following the template below.
     Edges are actually edge single weight values which are entries of the pre-scaled adjacency matrix.
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/PAiNN/_make.py

@@ -229,7 +229,7 @@ def make_crystal_model(inputs: list = None,
     r"""Make `PAiNN <https://arxiv.org/pdf/2102.03150.pdf>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.PAiNN.model_crystal_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, coordinates, edge_indices, image_translation, lattice, ...]`
     with '...' indicating mask or ID tensors following the template below.
@@ -238,7 +238,7 @@ def make_crystal_model(inputs: list = None,
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s

kgcnn/literature/Schnet/_make.py

@@ -228,14 +228,14 @@ def make_crystal_model(inputs: list = None,
     r"""Make `SchNet <https://arxiv.org/abs/1706.08566>`__ graph network via functional API.
     Default parameters can be found in :obj:`kgcnn.literature.Schnet.model_crystal_default`.
 
-    Model inputs:
+    **Model inputs**:
     Model uses the list template of inputs and standard output template.
     The supported inputs are  :obj:`[nodes, coordinates, edge_indices, image_translation, lattice, ...]`
     with '...' indicating mask or ID tensors following the template below.
 
     %s
 
-    Model outputs:
+    **Model outputs**:
     The standard output template:
 
     %s