{"language": "Python", "id": 196, "repo_owner": "danielegrattarola", "repo_name": "spektral", "head_branch": "master", "workflow_name": "Run examples", "workflow_filename": "examples.yml", "workflow_path": ".github/workflows/examples.yml", "contributor": "danielegrattarola", "sha_fail": "386bf6f0815368b78261be43bf90e203dfe9c13f", "sha_success": "bf6071475028cf9711b8264e8952deb2aee2e32b", "workflow": "name: Run examples\n\non: [push, pull_request]\n\njobs:\n  build:\n\n    runs-on: ubuntu-latest\n\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python 3.11\n      uses: actions/setup-python@v2\n      with:\n        python-version: 3.11\n    - name: Install dependencies\n      run: |\n        pip install ogb matplotlib\n    - name: Install Spektral\n      run: |\n        pip install .\n    - name: Just one epoch\n      run: |\n        sed -i -e 's/epochs = /epochs = 1 #/g' examples/node_prediction/*.py\n        sed -i -e 's/epochs = /epochs = 1 #/g' examples/graph_prediction/*.py\n        sed -i -e 's/epochs = /epochs = 1 #/g' examples/other/*.py\n    - name: Run all examples\n      run: |\n        cd examples/node_prediction/\n        for f in *.py; do\n          echo \"##### $f #####\"\n          python $f\n        done\n        cd ..\n        cd graph_prediction/\n        for f in *.py; do\n          echo \"##### $f #####\"\n          python $f\n        done\n        cd ..\n        cd other/\n        for f in *.py; do\n          echo \"##### $f #####\"\n          python $f\n        done\n        cd ..\n", "logs": [{"step_name": "build/7_Run all examples.txt", "log": "##[group]Run cd examples/node_prediction/\n\u001b[36;1mcd examples/node_prediction/\u001b[0m\n\u001b[36;1mfor f in *.py; do\u001b[0m\n\u001b[36;1m  echo \"##### $f #####\"\u001b[0m\n\u001b[36;1m  python $f\u001b[0m\n\u001b[36;1mdone\u001b[0m\n\u001b[36;1mcd ..\u001b[0m\n\u001b[36;1mcd graph_prediction/\u001b[0m\n\u001b[36;1mfor f in *.py; do\u001b[0m\n\u001b[36;1m  echo \"##### $f #####\"\u001b[0m\n\u001b[36;1m  python $f\u001b[0m\n\u001b[36;1mdone\u001b[0m\n\u001b[36;1mcd ..\u001b[0m\n\u001b[36;1mcd other/\u001b[0m\n\u001b[36;1mfor f in *.py; do\u001b[0m\n\u001b[36;1m  echo \"##### $f #####\"\u001b[0m\n\u001b[36;1m  python $f\u001b[0m\n\u001b[36;1mdone\u001b[0m\n\u001b[36;1mcd ..\u001b[0m\nshell: /usr/bin/bash -e {0}\nenv:\n  pythonLocation: /opt/hostedtoolcache/Python/3.11.7/x64\n  LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.11.7/x64/lib\n##[endgroup]\n##### citation_arma.py #####\n2024-01-21 16:14:04.697281: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:04.697333: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:04.698603: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:04.704823: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:05.946659: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n  warnings.warn(\nDownloading cora dataset.\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 1433)]               0         []                            \n                                                                                                  \n input_2 (InputLayer)        [(None, 2708)]               0         []                            \n                                                                                                  \n arma_conv (ARMAConv)        (None, 16)                   91744     ['input_1[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n dropout (Dropout)           (None, 16)                   0         ['arma_conv[0][0]']           \n                                                                                                  \n arma_conv_1 (ARMAConv)      (None, 7)                    231       ['dropout[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 91975 (359.28 KB)\nTrainable params: 91975 (359.28 KB)\nNon-trainable params: 0 (0.00 Byte)\n__________________________________________________________________________________________________\n\n1/1 [==============================] - ETA: 0s - loss: 0.1167 - acc: 0.1714\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 1s 938ms/step - loss: 0.1167 - acc: 0.1714 - val_loss: 0.3394 - val_acc: 0.3080\nEvaluating model.\n\n1/1 [==============================] - ETA: 0s - loss: 0.6695 - acc: 0.3360\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 12ms/step - loss: 0.6695 - acc: 0.3360\nDone.\nTest loss: 0.6695395708084106\nTest accuracy: 0.335999995470047\n##### citation_cheby.py #####\n2024-01-21 16:14:13.031828: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:13.031877: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:13.033233: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:13.039361: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:13.892745: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 1433)]               0         []                            \n                                                                                                  \n dropout (Dropout)           (None, 1433)                 0         ['input_1[0][0]']             \n                                                                                                  \n input_2 (InputLayer)        [(None, 2708)]               0         []                            \n                                                                                                  \n cheb_conv (ChebConv)        (None, 16)                   45856     ['dropout[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n dropout_1 (Dropout)         (None, 16)                   0         ['cheb_conv[0][0]']           \n                                                                                                  \n cheb_conv_1 (ChebConv)      (None, 7)                    224       ['dropout_1[0][0]',           \n                                                                     'input_2[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 46080 (180.00 KB)\nTrainable params: 46080 (180.00 KB)\nNon-trainable params: 0 (0.00 Byte)\n__________________________________________________________________________________________________\n\n1/1 [==============================] - ETA: 0s - loss: 1.9901 - acc: 0.1357\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 492ms/step - loss: 1.9901 - acc: 0.1357 - val_loss: 1.9229 - val_acc: 0.2740\nEvaluating model.\n\n1/1 [==============================] - ETA: 0s - loss: 1.9236 - acc: 0.2650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 15ms/step - loss: 1.9236 - acc: 0.2650\nDone.\nTest loss: 1.9235953092575073\nTest accuracy: 0.26499998569488525\n##### citation_gat.py #####\n2024-01-21 16:14:17.870770: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:17.870818: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:17.872219: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:17.878280: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:18.689165: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n  warnings.warn(\nPre-processing node features\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 1433)]               0         []                            \n                                                                                                  \n dropout (Dropout)           (None, 1433)                 0         ['input_1[0][0]']             \n                                                                                                  \n input_2 (InputLayer)        [(None, 2708)]               0         []                            \n                                                                                                  \n gat_conv (GATConv)          (None, 64)                   91904     ['dropout[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n dropout_1 (Dropout)         (None, 64)                   0         ['gat_conv[0][0]']            \n                                                                                                  \n gat_conv_1 (GATConv)        (None, 7)                    469       ['dropout_1[0][0]',           \n                                                                     'input_2[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 92373 (360.83 KB)\nTrainable params: 92373 (360.83 KB)\nNon-trainable params: 0 (0.00 Byte)\n__________________________________________________________________________________________________\n\n1/1 [==============================] - ETA: 0s - loss: 1.9498 - acc: 0.1429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 2s 2s/step - loss: 1.9498 - acc: 0.1429 - val_loss: 1.9496 - val_acc: 0.2020\nEvaluating model.\n\n1/1 [==============================] - ETA: 0s - loss: 1.9495 - acc: 0.2120\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 15ms/step - loss: 1.9495 - acc: 0.2120\nDone.\nTest loss: 1.9495328664779663\nTest accuracy: 0.21199998259544373\n##### citation_gat_custom.py #####\n2024-01-21 16:14:24.090507: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:24.090565: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:24.092222: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:24.098707: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:24.924070: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n  warnings.warn(\nPre-processing node features\nLoss tr: 1.9471, Acc tr: 0.3214, Loss va: 1.9493, Acc va: 0.1920, Loss te: 1.9494, Acc te: 0.1790\nImproved\nGAT (1 epochs)\nElapsed: 2.46s\n##### citation_gcn.py #####\n2024-01-21 16:14:30.891866: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:30.891915: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:30.893225: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:30.899400: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:31.735939: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\nPre-processing node features\n\n1/1 [==============================] - ETA: 0s - loss: 1.9535 - acc: 0.1286\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 1s 1s/step - loss: 1.9535 - acc: 0.1286 - val_loss: 1.9469 - val_acc: 0.4000\nEvaluating model.\n\n1/1 [==============================] - ETA: 0s - loss: 1.9470 - acc: 0.3910\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 9ms/step - loss: 1.9470 - acc: 0.3910\nDone.\nTest loss: 1.946979284286499\nTest accuracy: 0.390999972820282\n##### citation_gcn_custom.py #####\n2024-01-21 16:14:35.783612: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:35.783669: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:35.785139: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:35.791466: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:36.608646: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\nPre-processing node features\nSpektral - GCN (200 epochs)\nElapsed: 2.85s\nFinal loss = 0.617151141166687\n##### citation_simple_gc.py #####\n2024-01-21 16:14:43.435717: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:43.435759: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:43.437049: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:43.443159: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:44.284258: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/scipy/sparse/_index.py:145: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n  self._set_arrayXarray(i, j, x)\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 1433)]               0         []                            \n                                                                                                  \n input_2 (InputLayer)        [(None, 2708)]               0         []                            \n                                                                                                  \n gcn_conv (GCNConv)          (None, 7)                    10031     ['input_1[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 10031 (39.18 KB)\nTrainable params: 10031 (39.18 KB)\nNon-trainable params: 0 (0.00 Byte)\n__________________________________________________________________________________________________\n\n1/1 [==============================] - ETA: 0s - loss: 0.1008 - acc: 0.1357\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 355ms/step - loss: 0.1008 - acc: 0.1357 - val_loss: 0.2166 - val_acc: 0.6780\nEvaluating model.\n\n1/1 [==============================] - ETA: 0s - loss: 0.4094 - acc: 0.7070\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n1/1 [==============================] - 0s 12ms/step - loss: 0.4094 - acc: 0.7070\nDone.\nTest loss: 0.40943244099617004\nTest accuracy: 0.7070000171661377\n##### ogbn-arxiv_gcn.py #####\n2024-01-21 16:14:47.998765: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:14:47.998814: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:14:48.000133: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:14:48.006402: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:14:48.837666: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\nDownloading http://snap.stanford.edu/ogb/data/nodeproppred/arxiv.zip\n\n  0%|          | 0/81 [00:00<?, ?it/s]\nDownloaded 0.00 GB:   0%|          | 0/81 [00:00<?, ?it/s]\nDownloaded 0.00 GB:   1%|          | 1/81 [00:00<01:13,  1.09it/s]\nDownloaded 0.00 GB:   1%|          | 1/81 [00:01<01:13,  1.09it/s]\nDownloaded 0.00 GB:   2%|\u258f         | 2/81 [00:01<00:40,  1.95it/s]\nDownloaded 0.00 GB:   2%|\u258f         | 2/81 [00:01<00:40,  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96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 78/81 [00:03<00:00, 36.73it/s]\nDownloaded 0.08 GB:  96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 78/81 [00:03<00:00, 36.73it/s]\nDownloaded 0.08 GB: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 81/81 [00:03<00:00, 23.38it/s]\n\n  0%|          | 0/1 [00:00<?, ?it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1/1 [00:00<00:00, 21076.90it/s]\nExtracting dataset/arxiv.zip\nLoading necessary files...\nThis might take a while.\nProcessing graphs...\nSaving...\nModel: \"model\"\n__________________________________________________________________________________________________\n Layer (type)                Output Shape                 Param #   Connected to                  \n==================================================================================================\n input_1 (InputLayer)        [(None, 128)]                0         []                            \n                                                                                                  \n input_2 (InputLayer)        [(None, 169343)]             0         []                            \n                                                                                                  \n gcn_conv (GCNConv)          (None, 256)                  33024     ['input_1[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n batch_normalization (Batch  (None, 256)                  1024      ['gcn_conv[0][0]']            \n Normalization)                                                                                   \n                                                                                                  \n dropout (Dropout)           (None, 256)                  0         ['batch_normalization[0][0]'] \n                                                                                                  \n gcn_conv_1 (GCNConv)        (None, 256)                  65792     ['dropout[0][0]',             \n                                                                     'input_2[0][0]']             \n                                                                                                  \n batch_normalization_1 (Bat  (None, 256)                  1024      ['gcn_conv_1[0][0]']          \n chNormalization)                                                                                 \n                                                                                                  \n dropout_1 (Dropout)         (None, 256)                  0         ['batch_normalization_1[0][0]'\n                                                                    ]                             \n                                                                                                  \n gcn_conv_2 (GCNConv)        (None, 40)                   10280     ['dropout_1[0][0]',           \n                                                                     'input_2[0][0]']             \n                                                                                                  \n==================================================================================================\nTotal params: 111144 (434.16 KB)\nTrainable params: 110120 (430.16 KB)\nNon-trainable params: 1024 (4.00 KB)\n__________________________________________________________________________________________________\nEp. 1 - Loss: 5.289 - Acc: 0.109 - Val acc: 0.177 - Test acc: 0.241\nEvaluating model.\nDone! - Test acc: 0.241\n##### custom_dataset.py #####\n2024-01-21 16:15:07.319425: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:15:07.319469: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:15:07.320948: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:15:07.327116: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:15:08.139878: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/data/utils.py:221: UserWarning: you are shuffling a 'MyDataset' object which is not a subclass of 'Sequence'; `shuffle` is not guaranteed to behave correctly. E.g., non-numpy array/tensor objects with view semantics may contain duplicates after shuffling.\n  np.random.shuffle(a)\n/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n  warnings.warn(\nEp. 1 - Loss: 0.729 - Acc: 0.694 - Val loss: 0.334 - Val acc: 0.880\nNew best val_loss 0.334\nDone. Test loss: 0.4293. Test acc: 0.84\n##### general_gnn.py #####\n2024-01-21 16:15:13.211838: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-01-21 16:15:13.211888: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-01-21 16:15:13.213185: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2024-01-21 16:15:13.219400: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2024-01-21 16:15:14.044203: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\nDownloading PROTEINS dataset.\n\n  0%|                                                | 0.00/447k [00:00<?, ?B/s]\n  7%|\u2588\u2588\u258a                                     | 32.0k/447k [00:00<00:01, 249kB/s]\n 21%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                               | 96.0k/447k [00:00<00:00, 419kB/s]\n 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                    | 224k/447k [00:00<00:00, 777kB/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 447k/447k [00:00<00:00, 1.02MB/s]\nTraceback (most recent call last):\n  File \"/home/runner/work/spektral/spektral/examples/graph_prediction/general_gnn.py\", line 39, in <module>\n    data = TUDataset(\"PROTEINS\")\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/datasets/tudataset.py\", line 66, in __init__\n    super().__init__(**kwargs)\n  File \"/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/data/dataset.py\", line 118, in __init__\n    self.graphs = self.read()\n                  ^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/datasets/tudataset.py\", line 128, in read\n    [_normalize(xl_[:, None], \"ohe\") for xl_ in x_labs.T], -1\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/datasets/tudataset.py\", line 128, in <listcomp>\n    [_normalize(xl_[:, None], \"ohe\") for xl_ in x_labs.T], -1\n     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.11.7/x64/lib/python3.11/site-packages/spektral/datasets/tudataset.py\", line 219, in _normalize\n    fnorm = OneHotEncoder(sparse=False, categories=\"auto\")\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: OneHotEncoder.__init__() got an unexpected keyword argument 'sparse'\n##[error]Process completed with exit code 1.\n"}], "diff": "diff --git a/spektral/datasets/tudataset.py b/spektral/datasets/tudataset.py\nindex 3877535..f26b0de 100644\n--- a/spektral/datasets/tudataset.py\n+++ b/spektral/datasets/tudataset.py\n@@ -216,7 +216,7 @@ def _normalize(x, norm=None):\n     Apply one-hot encoding or z-score to a list of node features\n     \"\"\"\n     if norm == \"ohe\":\n-        fnorm = OneHotEncoder(sparse=False, categories=\"auto\")\n+        fnorm = OneHotEncoder(sparse_output=False, categories=\"auto\")\n     elif norm == \"zscore\":\n         fnorm = StandardScaler()\n     else:\n", "difficulty": 2, "changed_files": ["spektral/datasets/tudataset.py"], "commit_link": "https://github.com/danielegrattarola/spektral/tree/386bf6f0815368b78261be43bf90e203dfe9c13f"}