update for keras 3.0

Author: PatReis

kgcnn/layers/casting.py

@@ -402,7 +402,7 @@ def call(self, inputs: list, **kwargs):
         """
         attr, graph_id_attr, attr_id, attr_len = inputs
         if self.static_output_shape is not None:
-            target_shape = self.static_output_shape
+            target_shape = (ops.shape(attr_len)[0], self.static_output_shape[0])
         else:
             target_shape = (ops.shape(attr_len)[0], ops.amax(attr_len))
 

training/hyper/hyper_cora.py

@@ -12,7 +12,8 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False,
+                                         "static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 25, "output_dim": 1},
                 "gcn_args": {"units": 140, "use_bias": True, "activation": "relu"},
@@ -78,7 +79,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 25, "output_dim": 64},
                 "attention_args": {"units": 140, "use_bias": True, "use_edge_features": True,
@@ -146,7 +147,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 8, "output_dim": 64},
                 "attention_args": {"units": 70, "use_bias": True, "use_edge_features": True, "activation": "relu",
@@ -213,7 +214,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 25, "output_dim": 1},
                 "node_mlp_args": {"units": [70, 70], "use_bias": True, "activation": ["relu", "linear"]},
@@ -275,7 +276,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "depth": 4,
                 "dropout": 0.01,
@@ -336,7 +337,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (19793, 70)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 5, "output_dim": 64},
                 "input_graph_embedding": {"input_dim": 100, "output_dim": 64},

training/hyper/hyper_cora_lu.py

@@ -12,7 +12,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 8, "output_dim": 64},
                 "attention_args": {"units": 32, "use_bias": True, "use_edge_features": True, "activation": "relu",
@@ -79,7 +79,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 8, "output_dim": 64},
                 "attention_args": {"units": 32, "use_bias": True, "use_edge_features": True,
@@ -150,7 +150,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 25, "output_dim": 1},
                 "gcn_args": {"units": 64, "use_bias": True, "activation": "relu"},
@@ -217,7 +217,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 25, "output_dim": 1},
                 "node_mlp_args": {"units": [64, 32], "use_bias": True, "activation": ["relu", "linear"]},
@@ -281,7 +281,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"padded_disjoint": False, "static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "depth": 5,
                 "dropout": 0.05,
@@ -342,7 +342,7 @@
                     {"shape": (), "name": "total_nodes", "dtype": "int64"},
                     {"shape": (), "name": "total_edges", "dtype": "int64"}
                 ],
-                "cast_disjoint_kwargs": {"static_batched_node_output_shape": None},
+                "cast_disjoint_kwargs": {"static_batched_node_output_shape": (2708, 7)},
                 "input_node_embedding": {"input_dim": 95, "output_dim": 64},
                 "input_edge_embedding": {"input_dim": 5, "output_dim": 64},
                 "input_graph_embedding": {"input_dim": 100, "output_dim": 64},

training/results/CoraDataset/GAT/GAT_CoraDataset_score.yaml

@@ -1,20 +1,20 @@
-OS: posix_linux
+OS: nt_win32
 auc:
-- 0.999762237071991
-- 0.9993982911109924
-- 0.9996947050094604
-- 0.9997313618659973
-- 0.9998652338981628
+- 0.9997970461845398
+- 0.9997227787971497
+- 0.9996034502983093
+- 0.9997959733009338
+- 0.9992673397064209
 backend: tensorflow
 categorical_accuracy:
-- 0.9910950660705566
-- 0.9876216053962708
+- 0.9921056032180786
 - 0.9892004132270813
-- 0.9920430183410645
-- 0.9925482273101807
+- 0.9900845885276794
+- 0.9931164979934692
+- 0.9866751432418823
 cuda_available: 'False'
 data_unit: ''
-date_time: '2023-09-28 11:58:23'
+date_time: '2023-11-10 14:34:15'
 device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU'')]'
 device_memory: '[]'
 device_name: '[{}]'
@@ -33,117 +33,117 @@ learning_rate:
 - 2.9799999538226984e-05
 - 2.9799999538226984e-05
 loss:
-- 0.05441790074110031
-- 0.0716157853603363
-- 0.05998317897319794
-- 0.04724658280611038
-- 0.04499133676290512
+- 0.04833434894680977
+- 0.05927110090851784
+- 0.05427543446421623
+- 0.04719273000955582
+- 0.07413457334041595
 max_auc:
-- 0.999762237071991
-- 0.9993982911109924
-- 0.9996947050094604
-- 0.9997313618659973
-- 0.9998652338981628
+- 0.9997970461845398
+- 0.9997227787971497
+- 0.9996034502983093
+- 0.9997959733009338
+- 0.9992673397064209
 max_categorical_accuracy:
-- 0.9910950660705566
-- 0.9876847267150879
+- 0.9921056032180786
 - 0.9892004132270813
-- 0.9920430183410645
-- 0.9925482273101807
+- 0.9901477694511414
+- 0.9931164979934692
+- 0.9866751432418823
 max_learning_rate:
 - 0.0010000000474974513
 - 0.0010000000474974513
 - 0.0010000000474974513
 - 0.0010000000474974513
 - 0.0010000000474974513
 max_loss:
-- 3.398777961730957
-- 3.3987607955932617
-- 3.39878249168396
-- 3.398913860321045
-- 3.398898124694824
+- 3.398766279220581
+- 3.3987977504730225
+- 3.3987717628479004
+- 3.3988664150238037
+- 3.3989439010620117
 max_val_auc:
-- 0.9488013386726379
-- 0.9354034662246704
-- 0.9401569962501526
-- 0.9472397565841675
-- 0.9552340507507324
+- 0.9485095143318176
+- 0.9415806531906128
+- 0.9437487721443176
+- 0.9494364261627197
+- 0.9393430948257446
 max_val_categorical_accuracy:
-- 0.6304622292518616
-- 0.6145491003990173
-- 0.621874213218689
-- 0.628347635269165
-- 0.6599292755126953
+- 0.6430917382240295
+- 0.6221268177032471
+- 0.6423339247703552
+- 0.6339060664176941
+- 0.6237999200820923
 max_val_loss:
-- 0.8457090258598328
-- 0.845753014087677
-- 0.8460831046104431
-- 0.8454264998435974
-- 0.8444616198539734
+- 0.8462190628051758
+- 0.8460386991500854
+- 0.8453730344772339
+- 0.8446404337882996
+- 0.845479428768158
 min_auc:
 - 0.5
 - 0.5
 - 0.5
 - 0.5
 - 0.5
 min_categorical_accuracy:
-- 0.013073134236037731
-- 0.006378678604960442
-- 0.015725653618574142
-- 0.015472055412828922
-- 0.012693400494754314
+- 0.010989010334014893
+- 0.008210180327296257
+- 0.012188960798084736
+- 0.014461635611951351
+- 0.0143984854221344
 min_learning_rate:
 - 2.9799999538226984e-05
 - 2.9799999538226984e-05
 - 2.9799999538226984e-05
 - 2.9799999538226984e-05
 - 2.9799999538226984e-05
 min_loss:
-- 0.05441790074110031
-- 0.0716157853603363
-- 0.05998317897319794
-- 0.04724658280611038
-- 0.04499133676290512
+- 0.04833434894680977
+- 0.05927110090851784
+- 0.05427543446421623
+- 0.04719273000955582
+- 0.07413457334041595
 min_val_auc:
-- 0.5642143487930298
-- 0.5480632781982422
-- 0.566200315952301
-- 0.5362739562988281
-- 0.5903472304344177
+- 0.5619556903839111
+- 0.5503183007240295
+- 0.5602461099624634
+- 0.5655176639556885
+- 0.5490549802780151
 min_val_categorical_accuracy:
-- 0.06415761262178421
-- 0.0426875464618206
-- 0.07325082272291183
-- 0.041182417422533035
-- 0.03183425962924957
+- 0.044455669820308685
+- 0.022985603660345078
+- 0.04319272190332413
+- 0.04194037616252899
+- 0.05002526938915253
 min_val_loss:
-- 0.3355126976966858
-- 0.3776290714740753
-- 0.36266764998435974
-- 0.3395977318286896
-- 0.30311766266822815
+- 0.32970473170280457
+- 0.35624369978904724
+- 0.345664918422699
+- 0.33276599645614624
+- 0.3752051293849945
 model_class: make_model
 model_name: GAT
 model_version: ''
 multi_target_indices: null
 number_histories: 5
-seed: 42
+seed: 43
 time_list: null
 val_auc:
-- 0.8853158950805664
-- 0.8801058530807495
-- 0.885858416557312
-- 0.8841972351074219
-- 0.8946847915649414
+- 0.8906697034835815
+- 0.8801469802856445
+- 0.8841161727905273
+- 0.8852816820144653
+- 0.8822670578956604
 val_categorical_accuracy:
-- 0.6158120632171631
-- 0.600151538848877
-- 0.6115180850028992
-- 0.605103611946106
-- 0.6336533427238464
+- 0.6241475343704224
+- 0.6026774644851685
+- 0.6168224215507507
+- 0.6184942126274109
+- 0.6162203550338745
 val_loss:
-- 0.5910420417785645
-- 0.6129895448684692
-- 0.5975256562232971
-- 0.600094735622406
-- 0.5462737083435059
+- 0.5637747049331665
+- 0.6208769679069519
+- 0.5982853770256042
+- 0.5941001176834106
+- 0.6143744587898254

training/results/CoraDataset/GAT/GAT_hyper.json

@@ -1 +1 @@
-{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.GAT", "config": {"name": "GAT", "inputs": [{"shape": [null, 8710], "name": "node_attributes", "dtype": "float32"}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32"}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64"}, {"shape": [], "name": "total_nodes", "dtype": "int64"}, {"shape": [], "name": "total_edges", "dtype": "int64"}], "cast_disjoint_kwargs": {}, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 8, "output_dim": 64}, "attention_args": {"units": 70, "use_bias": true, "use_edge_features": true, "activation": "relu", "use_final_activation": false, "has_self_loops": true}, "pooling_nodes_args": {"pooling_method": "scatter_mean"}, "depth": 1, "attention_heads_num": 10, "attention_heads_concat": false, "verbose": 10, "output_embedding": "node", "output_mlp": {"use_bias": [true, true, false], "units": [70, 70, 70], "activation": ["relu", "relu", "softmax"]}}}, "training": {"fit": {"batch_size": 1, "epochs": 250, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.001, "learning_rate_stop": 1e-05, "epo_min": 200, "epo": 250, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "loss": "categorical_crossentropy", "weighted_metrics": ["categorical_accuracy", {"class_name": "AUC", "config": {"name": "auc"}}]}, "cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "multi_target_indices": null}, "data": {}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "CoraDataset", "module_name": "kgcnn.data.datasets.CoraDataset", "config": {}, "methods": [{"map_list": {"method": "make_undirected_edges"}}, {"map_list": {"method": "add_edge_self_loops"}}, {"map_list": {"method": "normalize_edge_weights_sym"}}, {"map_list": {"method": "count_nodes_and_edges"}}]}}
+{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.GAT", "config": {"name": "GAT", "inputs": [{"shape": [null, 8710], "name": "node_attributes", "dtype": "float32"}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32"}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64"}, {"shape": [], "name": "total_nodes", "dtype": "int64"}, {"shape": [], "name": "total_edges", "dtype": "int64"}], "cast_disjoint_kwargs": {"static_batched_node_output_shape": [19793, 70]}, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 8, "output_dim": 64}, "attention_args": {"units": 70, "use_bias": true, "use_edge_features": true, "activation": "relu", "use_final_activation": false, "has_self_loops": true}, "pooling_nodes_args": {"pooling_method": "scatter_mean"}, "depth": 1, "attention_heads_num": 10, "attention_heads_concat": false, "verbose": 10, "output_embedding": "node", "output_mlp": {"use_bias": [true, true, false], "units": [70, 70, 70], "activation": ["relu", "relu", "softmax"]}}}, "training": {"fit": {"batch_size": 1, "epochs": 250, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.001, "learning_rate_stop": 1e-05, "epo_min": 200, "epo": 250, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "loss": "categorical_crossentropy", "weighted_metrics": ["categorical_accuracy", {"class_name": "AUC", "config": {"name": "auc"}}]}, "cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "multi_target_indices": null}, "data": {}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "CoraDataset", "module_name": "kgcnn.data.datasets.CoraDataset", "config": {}, "methods": [{"map_list": {"method": "make_undirected_edges"}}, {"map_list": {"method": "add_edge_self_loops"}}, {"map_list": {"method": "normalize_edge_weights_sym"}}, {"map_list": {"method": "count_nodes_and_edges"}}]}}