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cog-imperial · Dec 9, 2024 · 494149c · 494149c
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commit 494149c
Showing 1 changed file with 11 additions and 14 deletions.
diff --git a/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb b/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb
@@ -39,18 +39,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import torch\n",
-    "from torch.nn import Linear, ReLU, Sigmoid\n",
-    "from torch_geometric.nn import Sequential, GCNConv\n",
-    "from torch_geometric.nn import global_mean_pool\n",
-    "from omlt.io.torch_geometric import gnn_with_fixed_graph\n",
     "import pyomo.environ as pyo\n",
+    "import torch\n",
     "from omlt import OmltBlock\n",
+    "from omlt.io.torch_geometric import gnn_with_fixed_graph\n",
+    "from torch.nn import Linear, ReLU, Sigmoid\n",
+    "from torch_geometric.nn import GCNConv, Sequential, global_mean_pool\n",
     "\n",
     "\n",
     "def GCN_Sequential(activation, pooling):\n",
@@ -68,7 +67,7 @@
     "            activation(),\n",
     "            Linear(2, 1),\n",
     "        ],\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -485,19 +484,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import torch\n",
-    "from torch.nn import Linear, ReLU\n",
-    "from torch_geometric.nn import Sequential, SAGEConv\n",
-    "from torch_geometric.nn import global_add_pool\n",
-    "from omlt.io.torch_geometric import gnn_with_non_fixed_graph\n",
-    "\n",
     "import pyomo.environ as pyo\n",
+    "import torch\n",
     "from omlt import OmltBlock\n",
+    "from omlt.io.torch_geometric import gnn_with_non_fixed_graph\n",
+    "from torch.nn import Linear, ReLU\n",
+    "from torch_geometric.nn import SAGEConv, Sequential, global_add_pool\n",
     "\n",
     "\n",
     "def SAGE_Sequential(activation, pooling):\n",