{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "qf-uHjOnuw5g"
},
"source": [
"# Node Classification with Graph Neural Networks\n",
"\n",
"**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)\n",
"\n",
"
\n",
" \n",
" \n",
" target \n",
" source \n",
" \n",
" \n",
" \n",
" \n",
" 1393 \n",
" 6741 \n",
" 51909 \n",
" \n",
" \n",
" 5347 \n",
" 696345 \n",
" 696342 \n",
" \n",
" \n",
" 444 \n",
" 1365 \n",
" 26850 \n",
" \n",
" \n",
" 5312 \n",
" 671269 \n",
" 1154124 \n",
" \n",
" \n",
" 4945 \n",
" 523574 \n",
" 653441 \n",
" \n",
" \n",
"
\n",
"
"
]
},
"metadata": {}
}
],
"source": [
"plt.figure(figsize=(10, 10))\n",
"colors = papers[\"subject\"].tolist()\n",
"cora_graph = nx.from_pandas_edgelist(citations.sample(n=1500))\n",
"subjects = list(papers[papers[\"paper_id\"].isin(list(cora_graph.nodes))][\"subject\"])\n",
"nx.draw_spring(cora_graph, node_size=15, node_color=subjects)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IZxdR7Tbuw5n"
},
"source": [
"### Split the dataset into stratified train and test sets"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "HD_AyFYzuw5o",
"outputId": "a267fd1c-9505-463b-de5a-13241a2f0f23",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train data shape: (1359, 1435)\n",
"Test data shape: (1349, 1435)\n"
]
}
],
"source": [
"train_data, test_data = [], []\n",
"\n",
"for _, group_data in papers.groupby(\"subject\"):\n",
" # Select around 50% of the dataset for training.\n",
" random_selection = np.random.rand(len(group_data.index)) <= 0.5\n",
" train_data.append(group_data[random_selection])\n",
" test_data.append(group_data[~random_selection])\n",
"\n",
"train_data = pd.concat(train_data).sample(frac=1)\n",
"test_data = pd.concat(test_data).sample(frac=1)\n",
"\n",
"print(\"Train data shape:\", train_data.shape)\n",
"print(\"Test data shape:\", test_data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aereCbwYuw5o"
},
"source": [
"## Implement Train and Evaluate Experiment"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "uhQTdD7Luw5o"
},
"outputs": [],
"source": [
"hidden_units = [32, 32]\n",
"learning_rate = 0.01\n",
"dropout_rate = 0.5\n",
"num_epochs = 300\n",
"batch_size = 256"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oT3JReaYuw5o"
},
"source": [
"This function compiles and trains an input model using the given training data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "WzI6Jb3Cuw5o"
},
"outputs": [],
"source": [
"\n",
"def run_experiment(model, x_train, y_train):\n",
" # Compile the model.\n",
" model.compile(\n",
" optimizer=keras.optimizers.Adam(learning_rate),\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"acc\")],\n",
" )\n",
" # Create an early stopping callback.\n",
" early_stopping = keras.callbacks.EarlyStopping(\n",
" monitor=\"val_acc\", patience=50, restore_best_weights=True\n",
" )\n",
" # Fit the model.\n",
" history = model.fit(\n",
" x=x_train,\n",
" y=y_train,\n",
" epochs=num_epochs,\n",
" batch_size=batch_size,\n",
" validation_split=0.15,\n",
" callbacks=[early_stopping],\n",
" )\n",
"\n",
" return history\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TFDbTi9Uuw5o"
},
"source": [
"This function displays the loss and accuracy curves of the model during training."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "L9705vXWuw5o"
},
"outputs": [],
"source": [
"\n",
"def display_learning_curves(history):\n",
" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
"\n",
" ax1.plot(history.history[\"loss\"])\n",
" ax1.plot(history.history[\"val_loss\"])\n",
" ax1.legend([\"train\", \"test\"], loc=\"upper right\")\n",
" ax1.set_xlabel(\"Epochs\")\n",
" ax1.set_ylabel(\"Loss\")\n",
"\n",
" ax2.plot(history.history[\"acc\"])\n",
" ax2.plot(history.history[\"val_acc\"])\n",
" ax2.legend([\"train\", \"test\"], loc=\"upper right\")\n",
" ax2.set_xlabel(\"Epochs\")\n",
" ax2.set_ylabel(\"Accuracy\")\n",
" plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "82uuRh8zuw5o"
},
"source": [
"## Implement Feedforward Network (FFN) Module\n",
"\n",
"We will use this module in the baseline and the GNN models."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "jacIqmBJuw5o"
},
"outputs": [],
"source": [
"\n",
"def create_ffn(hidden_units, dropout_rate, name=None):\n",
" fnn_layers = []\n",
"\n",
" for units in hidden_units:\n",
" fnn_layers.append(layers.BatchNormalization())\n",
" fnn_layers.append(layers.Dropout(dropout_rate))\n",
" fnn_layers.append(layers.Dense(units, activation=tf.nn.gelu))\n",
"\n",
" return keras.Sequential(fnn_layers, name=name)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PAa3nn_Muw5o"
},
"source": [
"## Build a Baseline Neural Network Model\n",
"\n",
"### Prepare the data for the baseline model"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "qCc-sdrYuw5o"
},
"outputs": [],
"source": [
"feature_names = set(papers.columns) - {\"paper_id\", \"subject\"}\n",
"num_features = len(feature_names)\n",
"num_classes = len(class_idx)\n",
"\n",
"# Create train and test features as a numpy array.\n",
"x_train = train_data[feature_names].to_numpy()\n",
"x_test = test_data[feature_names].to_numpy()\n",
"# Create train and test targets as a numpy array.\n",
"y_train = train_data[\"subject\"]\n",
"y_test = test_data[\"subject\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wBVZZuVFuw5o"
},
"source": [
"### Implement a baseline classifier\n",
"\n",
"We add five FFN blocks with skip connections, so that we generate a baseline model with\n",
"roughly the same number of parameters as the GNN models to be built later."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "Ehrjcq88uw5o",
"outputId": "4178aac5-0141-4299-aa33-54f67158ea75",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"baseline\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_features (InputLayer) [(None, 1433)] 0 [] \n",
" \n",
" ffn_block1 (Sequential) (None, 32) 52804 ['input_features[0][0]'] \n",
" \n",
" ffn_block2 (Sequential) (None, 32) 2368 ['ffn_block1[0][0]'] \n",
" \n",
" skip_connection2 (Add) (None, 32) 0 ['ffn_block1[0][0]', \n",
" 'ffn_block2[0][0]'] \n",
" \n",
" ffn_block3 (Sequential) (None, 32) 2368 ['skip_connection2[0][0]'] \n",
" \n",
" skip_connection3 (Add) (None, 32) 0 ['skip_connection2[0][0]', \n",
" 'ffn_block3[0][0]'] \n",
" \n",
" ffn_block4 (Sequential) (None, 32) 2368 ['skip_connection3[0][0]'] \n",
" \n",
" skip_connection4 (Add) (None, 32) 0 ['skip_connection3[0][0]', \n",
" 'ffn_block4[0][0]'] \n",
" \n",
" ffn_block5 (Sequential) (None, 32) 2368 ['skip_connection4[0][0]'] \n",
" \n",
" skip_connection5 (Add) (None, 32) 0 ['skip_connection4[0][0]', \n",
" 'ffn_block5[0][0]'] \n",
" \n",
" logits (Dense) (None, 7) 231 ['skip_connection5[0][0]'] \n",
" \n",
"==================================================================================================\n",
"Total params: 62,507\n",
"Trainable params: 59,065\n",
"Non-trainable params: 3,442\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"\n",
"def create_baseline_model(hidden_units, num_classes, dropout_rate=0.2):\n",
" inputs = layers.Input(shape=(num_features,), name=\"input_features\")\n",
" x = create_ffn(hidden_units, dropout_rate, name=f\"ffn_block1\")(inputs)\n",
" for block_idx in range(4):\n",
" # Create an FFN block.\n",
" x1 = create_ffn(hidden_units, dropout_rate, name=f\"ffn_block{block_idx + 2}\")(x)\n",
" # Add skip connection.\n",
" x = layers.Add(name=f\"skip_connection{block_idx + 2}\")([x, x1])\n",
" # Compute logits.\n",
" logits = layers.Dense(num_classes, name=\"logits\")(x)\n",
" # Create the model.\n",
" return keras.Model(inputs=inputs, outputs=logits, name=\"baseline\")\n",
"\n",
"\n",
"baseline_model = create_baseline_model(hidden_units, num_classes, dropout_rate)\n",
"baseline_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "THq7QbZuuw5p"
},
"source": [
"### Train the baseline classifier"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "i8xhdXhLuw5p",
"outputId": "81ecc86c-553f-4066-9d96-7f8315f156d6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"5/5 [==============================] - 9s 241ms/step - loss: 3.9877 - acc: 0.1628 - val_loss: 1.9331 - val_acc: 0.3039\n",
"Epoch 2/300\n",
"5/5 [==============================] - 0s 43ms/step - loss: 2.7708 - acc: 0.2797 - val_loss: 1.9075 - val_acc: 0.3039\n",
"Epoch 3/300\n",
"5/5 [==============================] - 0s 47ms/step - loss: 2.3218 - acc: 0.2805 - val_loss: 1.8959 - val_acc: 0.3039\n",
"Epoch 4/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 2.0632 - acc: 0.2589 - val_loss: 1.8854 - val_acc: 0.3039\n",
"Epoch 5/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 2.0229 - acc: 0.2693 - val_loss: 1.8705 - val_acc: 0.3186\n",
"Epoch 6/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 1.8422 - acc: 0.3498 - val_loss: 1.8317 - val_acc: 0.3186\n",
"Epoch 7/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 1.8224 - acc: 0.3446 - val_loss: 1.7924 - val_acc: 0.3137\n",
"Epoch 8/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 1.7301 - acc: 0.3818 - val_loss: 1.7686 - val_acc: 0.3186\n",
"Epoch 9/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 1.6839 - acc: 0.3905 - val_loss: 1.7474 - val_acc: 0.3824\n",
"Epoch 10/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 1.5933 - acc: 0.4182 - val_loss: 1.7325 - val_acc: 0.5000\n",
"Epoch 11/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 1.5208 - acc: 0.4416 - val_loss: 1.6904 - val_acc: 0.5637\n",
"Epoch 12/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 1.4094 - acc: 0.4779 - val_loss: 1.6166 - val_acc: 0.5882\n",
"Epoch 13/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 1.3891 - acc: 0.5056 - val_loss: 1.5268 - val_acc: 0.6176\n",
"Epoch 14/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 1.2614 - acc: 0.5385 - val_loss: 1.4427 - val_acc: 0.5931\n",
"Epoch 15/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 1.2329 - acc: 0.5541 - val_loss: 1.3582 - val_acc: 0.6029\n",
"Epoch 16/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 1.1548 - acc: 0.5879 - val_loss: 1.3007 - val_acc: 0.5980\n",
"Epoch 17/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 1.0747 - acc: 0.6156 - val_loss: 1.2364 - val_acc: 0.6078\n",
"Epoch 18/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 1.0347 - acc: 0.6338 - val_loss: 1.1821 - val_acc: 0.6029\n",
"Epoch 19/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.9909 - acc: 0.6476 - val_loss: 1.1422 - val_acc: 0.6127\n",
"Epoch 20/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.9321 - acc: 0.6727 - val_loss: 1.1380 - val_acc: 0.5980\n",
"Epoch 21/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.8806 - acc: 0.6952 - val_loss: 1.1562 - val_acc: 0.5882\n",
"Epoch 22/300\n",
"5/5 [==============================] - 0s 31ms/step - loss: 0.9009 - acc: 0.6866 - val_loss: 1.1645 - val_acc: 0.5588\n",
"Epoch 23/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.8441 - acc: 0.7134 - val_loss: 1.1378 - val_acc: 0.5784\n",
"Epoch 24/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.8512 - acc: 0.6987 - val_loss: 1.1286 - val_acc: 0.5588\n",
"Epoch 25/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.8056 - acc: 0.7126 - val_loss: 1.1090 - val_acc: 0.5882\n",
"Epoch 26/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.7451 - acc: 0.7481 - val_loss: 1.1056 - val_acc: 0.5931\n",
"Epoch 27/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.7152 - acc: 0.7489 - val_loss: 1.0772 - val_acc: 0.5980\n",
"Epoch 28/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.7040 - acc: 0.7645 - val_loss: 1.0367 - val_acc: 0.6127\n",
"Epoch 29/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.6505 - acc: 0.7732 - val_loss: 1.0280 - val_acc: 0.5980\n",
"Epoch 30/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.7039 - acc: 0.7472 - val_loss: 1.0092 - val_acc: 0.6127\n",
"Epoch 31/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.6493 - acc: 0.7688 - val_loss: 1.0089 - val_acc: 0.6520\n",
"Epoch 32/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.5978 - acc: 0.7939 - val_loss: 1.0317 - val_acc: 0.6520\n",
"Epoch 33/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.6692 - acc: 0.7740 - val_loss: 1.0171 - val_acc: 0.6471\n",
"Epoch 34/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.5893 - acc: 0.7931 - val_loss: 0.9704 - val_acc: 0.6716\n",
"Epoch 35/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.5925 - acc: 0.7965 - val_loss: 0.9673 - val_acc: 0.6814\n",
"Epoch 36/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.6425 - acc: 0.7766 - val_loss: 0.9685 - val_acc: 0.6667\n",
"Epoch 37/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.5378 - acc: 0.8052 - val_loss: 0.9789 - val_acc: 0.6569\n",
"Epoch 38/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.5122 - acc: 0.8208 - val_loss: 0.9915 - val_acc: 0.6569\n",
"Epoch 39/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.5312 - acc: 0.8216 - val_loss: 0.9867 - val_acc: 0.6667\n",
"Epoch 40/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.5929 - acc: 0.7983 - val_loss: 0.9863 - val_acc: 0.6471\n",
"Epoch 41/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.5716 - acc: 0.8061 - val_loss: 0.9802 - val_acc: 0.6667\n",
"Epoch 42/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.5572 - acc: 0.8000 - val_loss: 0.9275 - val_acc: 0.7059\n",
"Epoch 43/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.5157 - acc: 0.8260 - val_loss: 0.8715 - val_acc: 0.7108\n",
"Epoch 44/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.5296 - acc: 0.8173 - val_loss: 0.8744 - val_acc: 0.7157\n",
"Epoch 45/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.5115 - acc: 0.8190 - val_loss: 0.8999 - val_acc: 0.7059\n",
"Epoch 46/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 0.4989 - acc: 0.8139 - val_loss: 0.9466 - val_acc: 0.6961\n",
"Epoch 47/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.5062 - acc: 0.8087 - val_loss: 0.9473 - val_acc: 0.7059\n",
"Epoch 48/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.5483 - acc: 0.8113 - val_loss: 0.9249 - val_acc: 0.7157\n",
"Epoch 49/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 0.4602 - acc: 0.8416 - val_loss: 0.8981 - val_acc: 0.7010\n",
"Epoch 50/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.5005 - acc: 0.8286 - val_loss: 0.9008 - val_acc: 0.7059\n",
"Epoch 51/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4849 - acc: 0.8234 - val_loss: 0.8900 - val_acc: 0.7108\n",
"Epoch 52/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4877 - acc: 0.8320 - val_loss: 0.8740 - val_acc: 0.7157\n",
"Epoch 53/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4701 - acc: 0.8294 - val_loss: 0.8712 - val_acc: 0.7059\n",
"Epoch 54/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4987 - acc: 0.8225 - val_loss: 0.9121 - val_acc: 0.7206\n",
"Epoch 55/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4873 - acc: 0.8225 - val_loss: 0.9796 - val_acc: 0.7206\n",
"Epoch 56/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4970 - acc: 0.8225 - val_loss: 0.9184 - val_acc: 0.7157\n",
"Epoch 57/300\n",
"5/5 [==============================] - 0s 30ms/step - loss: 0.4482 - acc: 0.8364 - val_loss: 0.9048 - val_acc: 0.7451\n",
"Epoch 58/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4801 - acc: 0.8416 - val_loss: 0.8882 - val_acc: 0.7402\n",
"Epoch 59/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4519 - acc: 0.8381 - val_loss: 0.8901 - val_acc: 0.7255\n",
"Epoch 60/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4533 - acc: 0.8450 - val_loss: 0.8929 - val_acc: 0.7255\n",
"Epoch 61/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 0.4414 - acc: 0.8494 - val_loss: 0.8688 - val_acc: 0.7304\n",
"Epoch 62/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.4441 - acc: 0.8537 - val_loss: 0.8807 - val_acc: 0.7206\n",
"Epoch 63/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.3964 - acc: 0.8701 - val_loss: 0.9090 - val_acc: 0.7157\n",
"Epoch 64/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4273 - acc: 0.8476 - val_loss: 0.9209 - val_acc: 0.7304\n",
"Epoch 65/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.4113 - acc: 0.8589 - val_loss: 0.9030 - val_acc: 0.7108\n",
"Epoch 66/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4236 - acc: 0.8459 - val_loss: 0.9009 - val_acc: 0.7108\n",
"Epoch 67/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4252 - acc: 0.8390 - val_loss: 0.9072 - val_acc: 0.7206\n",
"Epoch 68/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4591 - acc: 0.8442 - val_loss: 0.9339 - val_acc: 0.7255\n",
"Epoch 69/300\n",
"5/5 [==============================] - 0s 25ms/step - loss: 0.4085 - acc: 0.8563 - val_loss: 0.9259 - val_acc: 0.7304\n",
"Epoch 70/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4276 - acc: 0.8459 - val_loss: 0.9365 - val_acc: 0.7353\n",
"Epoch 71/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4383 - acc: 0.8416 - val_loss: 0.9305 - val_acc: 0.7353\n",
"Epoch 72/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4472 - acc: 0.8450 - val_loss: 0.9177 - val_acc: 0.7255\n",
"Epoch 73/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.3996 - acc: 0.8589 - val_loss: 0.9518 - val_acc: 0.7451\n",
"Epoch 74/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3663 - acc: 0.8675 - val_loss: 1.0182 - val_acc: 0.7353\n",
"Epoch 75/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3893 - acc: 0.8701 - val_loss: 0.9830 - val_acc: 0.7402\n",
"Epoch 76/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3564 - acc: 0.8736 - val_loss: 0.9914 - val_acc: 0.7353\n",
"Epoch 77/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4075 - acc: 0.8511 - val_loss: 0.9825 - val_acc: 0.7206\n",
"Epoch 78/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.4139 - acc: 0.8519 - val_loss: 0.9822 - val_acc: 0.7304\n",
"Epoch 79/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4360 - acc: 0.8450 - val_loss: 0.9813 - val_acc: 0.7304\n",
"Epoch 80/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4511 - acc: 0.8511 - val_loss: 0.9526 - val_acc: 0.7206\n",
"Epoch 81/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4030 - acc: 0.8632 - val_loss: 0.9370 - val_acc: 0.7402\n",
"Epoch 82/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4122 - acc: 0.8528 - val_loss: 0.9534 - val_acc: 0.7353\n",
"Epoch 83/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.3929 - acc: 0.8597 - val_loss: 0.9705 - val_acc: 0.7255\n",
"Epoch 84/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3953 - acc: 0.8710 - val_loss: 0.9966 - val_acc: 0.7353\n",
"Epoch 85/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4159 - acc: 0.8502 - val_loss: 1.0189 - val_acc: 0.7304\n",
"Epoch 86/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4225 - acc: 0.8545 - val_loss: 1.0229 - val_acc: 0.7304\n",
"Epoch 87/300\n",
"5/5 [==============================] - 0s 33ms/step - loss: 0.4057 - acc: 0.8571 - val_loss: 1.0382 - val_acc: 0.7353\n",
"Epoch 88/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4380 - acc: 0.8545 - val_loss: 1.0426 - val_acc: 0.7206\n",
"Epoch 89/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3935 - acc: 0.8606 - val_loss: 1.0753 - val_acc: 0.7157\n",
"Epoch 90/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.3776 - acc: 0.8623 - val_loss: 1.0774 - val_acc: 0.7059\n",
"Epoch 91/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.3830 - acc: 0.8615 - val_loss: 1.0802 - val_acc: 0.7059\n",
"Epoch 92/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4191 - acc: 0.8615 - val_loss: 1.0667 - val_acc: 0.7059\n",
"Epoch 93/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4109 - acc: 0.8537 - val_loss: 1.0678 - val_acc: 0.7108\n",
"Epoch 94/300\n",
"5/5 [==============================] - 0s 30ms/step - loss: 0.4164 - acc: 0.8580 - val_loss: 1.0763 - val_acc: 0.7206\n",
"Epoch 95/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4070 - acc: 0.8580 - val_loss: 1.1029 - val_acc: 0.7010\n",
"Epoch 96/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3965 - acc: 0.8658 - val_loss: 1.1319 - val_acc: 0.6912\n",
"Epoch 97/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.4132 - acc: 0.8554 - val_loss: 1.1491 - val_acc: 0.6863\n",
"Epoch 98/300\n",
"5/5 [==============================] - 0s 28ms/step - loss: 0.3837 - acc: 0.8623 - val_loss: 1.1517 - val_acc: 0.6765\n",
"Epoch 99/300\n",
"5/5 [==============================] - 0s 30ms/step - loss: 0.3812 - acc: 0.8649 - val_loss: 1.1698 - val_acc: 0.6961\n",
"Epoch 100/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3780 - acc: 0.8823 - val_loss: 1.1919 - val_acc: 0.7059\n",
"Epoch 101/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.4065 - acc: 0.8545 - val_loss: 1.2029 - val_acc: 0.6912\n",
"Epoch 102/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3743 - acc: 0.8684 - val_loss: 1.1801 - val_acc: 0.6912\n",
"Epoch 103/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.3633 - acc: 0.8710 - val_loss: 1.1532 - val_acc: 0.7059\n",
"Epoch 104/300\n",
"5/5 [==============================] - 0s 29ms/step - loss: 0.3726 - acc: 0.8615 - val_loss: 1.1352 - val_acc: 0.6961\n",
"Epoch 105/300\n",
"5/5 [==============================] - 0s 27ms/step - loss: 0.3667 - acc: 0.8710 - val_loss: 1.1716 - val_acc: 0.7108\n",
"Epoch 106/300\n",
"5/5 [==============================] - 0s 26ms/step - loss: 0.4083 - acc: 0.8589 - val_loss: 1.1892 - val_acc: 0.6961\n",
"Epoch 107/300\n",
"5/5 [==============================] - 0s 30ms/step - loss: 0.4161 - acc: 0.8615 - val_loss: 1.1795 - val_acc: 0.7059\n"
]
}
],
"source": [
"history = run_experiment(baseline_model, x_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ATOZhB0nuw5p"
},
"source": [
"Let's plot the learning curves."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "mQROvewLuw5p",
"outputId": "ebfd7fd6-e5d1-430f-a754-80fe3d26b586",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 334
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"display_learning_curves(history)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ypUGNxPIuw5p"
},
"source": [
"Now we evaluate the baseline model on the test data split."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "B3khn5bOuw5p",
"outputId": "138da74c-f4ac-45eb-fddf-baa06131d071",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test accuracy: 71.46%\n"
]
}
],
"source": [
"_, test_accuracy = baseline_model.evaluate(x=x_test, y=y_test, verbose=0)\n",
"print(f\"Test accuracy: {round(test_accuracy * 100, 2)}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QNGZph2Uuw5p"
},
"source": [
"### Examine the baseline model predictions\n",
"\n",
"Let's create new data instances by randomly generating binary word vectors with respect to\n",
"the word presence probabilities."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "m_oxrIoYuw5p"
},
"outputs": [],
"source": [
"\n",
"def generate_random_instances(num_instances):\n",
" token_probability = x_train.mean(axis=0)\n",
" instances = []\n",
" for _ in range(num_instances):\n",
" probabilities = np.random.uniform(size=len(token_probability))\n",
" instance = (probabilities <= token_probability).astype(int)\n",
" instances.append(instance)\n",
"\n",
" return np.array(instances)\n",
"\n",
"\n",
"def display_class_probabilities(probabilities):\n",
" for instance_idx, probs in enumerate(probabilities):\n",
" print(f\"Instance {instance_idx + 1}:\")\n",
" for class_idx, prob in enumerate(probs):\n",
" print(f\"- {class_values[class_idx]}: {round(prob * 100, 2)}%\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z4-HnpUOuw5p"
},
"source": [
"Now we show the baseline model predictions given these randomly generated instances."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "ckfAsU7-uw5p",
"outputId": "1b66bdb5-8680-4ae8-fa40-00eb05aa6e21",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Instance 1:\n",
"- Case_Based: 38.59%\n",
"- Genetic_Algorithms: 14.82%\n",
"- Neural_Networks: 17.45%\n",
"- Probabilistic_Methods: 12.43%\n",
"- Reinforcement_Learning: 6.52%\n",
"- Rule_Learning: 4.01%\n",
"- Theory: 6.18%\n",
"Instance 2:\n",
"- Case_Based: 3.64%\n",
"- Genetic_Algorithms: 3.5%\n",
"- Neural_Networks: 82.19%\n",
"- Probabilistic_Methods: 3.02%\n",
"- Reinforcement_Learning: 0.7%\n",
"- Rule_Learning: 0.4%\n",
"- Theory: 6.55%\n",
"Instance 3:\n",
"- Case_Based: 0.64%\n",
"- Genetic_Algorithms: 1.91%\n",
"- Neural_Networks: 81.94%\n",
"- Probabilistic_Methods: 9.91%\n",
"- Reinforcement_Learning: 2.06%\n",
"- Rule_Learning: 0.14%\n",
"- Theory: 3.39%\n",
"Instance 4:\n",
"- Case_Based: 59.59%\n",
"- Genetic_Algorithms: 17.17%\n",
"- Neural_Networks: 8.78%\n",
"- Probabilistic_Methods: 5.04%\n",
"- Reinforcement_Learning: 1.82%\n",
"- Rule_Learning: 2.23%\n",
"- Theory: 5.37%\n",
"Instance 5:\n",
"- Case_Based: 5.11%\n",
"- Genetic_Algorithms: 27.71%\n",
"- Neural_Networks: 36.32%\n",
"- Probabilistic_Methods: 4.32%\n",
"- Reinforcement_Learning: 24.83%\n",
"- Rule_Learning: 0.69%\n",
"- Theory: 1.02%\n",
"Instance 6:\n",
"- Case_Based: 8.12%\n",
"- Genetic_Algorithms: 10.27%\n",
"- Neural_Networks: 66.62%\n",
"- Probabilistic_Methods: 2.28%\n",
"- Reinforcement_Learning: 7.94%\n",
"- Rule_Learning: 0.64%\n",
"- Theory: 4.14%\n",
"Instance 7:\n",
"- Case_Based: 0.52%\n",
"- Genetic_Algorithms: 0.62%\n",
"- Neural_Networks: 94.87%\n",
"- Probabilistic_Methods: 2.35%\n",
"- Reinforcement_Learning: 0.69%\n",
"- Rule_Learning: 0.05%\n",
"- Theory: 0.9%\n"
]
}
],
"source": [
"new_instances = generate_random_instances(num_classes)\n",
"logits = baseline_model.predict(new_instances)\n",
"probabilities = keras.activations.softmax(tf.convert_to_tensor(logits)).numpy()\n",
"display_class_probabilities(probabilities)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lAl7NyNDuw5p"
},
"source": [
"## Build a Graph Neural Network Model\n",
"\n",
"### Prepare the data for the graph model\n",
"\n",
"Preparing and loading the graphs data into the model for training is the most challenging\n",
"part in GNN models, which is addressed in different ways by the specialised libraries.\n",
"In this example, we show a simple approach for preparing and using graph data that is suitable\n",
"if your dataset consists of a single graph that fits entirely in memory.\n",
"\n",
"The graph data is represented by the `graph_info` tuple, which consists of the following\n",
"three elements:\n",
"\n",
"1. `node_features`: This is a `[num_nodes, num_features]` NumPy array that includes the\n",
"node features. In this dataset, the nodes are the papers, and the `node_features` are the\n",
"word-presence binary vectors of each paper.\n",
"2. `edges`: This is `[num_edges, num_edges]` NumPy array representing a sparse\n",
"[adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix#:~:text=In%20graph%20theory%20and%20computer,with%20zeros%20on%20its%20diagonal.)\n",
"of the links between the nodes. In this example, the links are the citations between the papers.\n",
"3. `edge_weights` (optional): This is a `[num_edges]` NumPy array that includes the edge weights, which *quantify*\n",
"the relationships between nodes in the graph. In this example, there are no weights for the paper citations."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "RTvQbkepuw5p",
"outputId": "63a79437-6175-48b5-9986-91af1ec93e25",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Edges shape: (2, 5429)\n",
"Nodes shape: (2708, 1433)\n"
]
}
],
"source": [
"# Create an edges array (sparse adjacency matrix) of shape [2, num_edges].\n",
"edges = citations[[\"source\", \"target\"]].to_numpy().T\n",
"# Create an edge weights array of ones.\n",
"edge_weights = tf.ones(shape=edges.shape[1])\n",
"# Create a node features array of shape [num_nodes, num_features].\n",
"node_features = tf.cast(\n",
" papers.sort_values(\"paper_id\")[feature_names].to_numpy(), dtype=tf.dtypes.float32\n",
")\n",
"# Create graph info tuple with node_features, edges, and edge_weights.\n",
"graph_info = (node_features, edges, edge_weights)\n",
"\n",
"print(\"Edges shape:\", edges.shape)\n",
"print(\"Nodes shape:\", node_features.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4TLKlGhvuw5p"
},
"source": [
"### Implement a graph convolution layer\n",
"\n",
"We implement a graph convolution module as a [Keras Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer?version=nightly).\n",
"Our `GraphConvLayer` performs the following steps:\n",
"\n",
"1. **Prepare**: The input node representations are processed using a FFN to produce a *message*. You can simplify\n",
"the processing by only applying linear transformation to the representations.\n",
"2. **Aggregate**: The messages of the neighbours of each node are aggregated with\n",
"respect to the `edge_weights` using a *permutation invariant* pooling operation, such as *sum*, *mean*, and *max*,\n",
"to prepare a single aggregated message for each node. See, for example, [tf.math.unsorted_segment_sum](https://www.tensorflow.org/api_docs/python/tf/math/unsorted_segment_sum)\n",
"APIs used to aggregate neighbour messages.\n",
"3. **Update**: The `node_repesentations` and `aggregated_messages`—both of shape `[num_nodes, representation_dim]`—\n",
"are combined and processed to produce the new state of the node representations (node embeddings).\n",
"If `combination_type` is `gru`, the `node_repesentations` and `aggregated_messages` are stacked to create a sequence,\n",
"then processed by a GRU layer. Otherwise, the `node_repesentations` and `aggregated_messages` are added\n",
"or concatenated, then processed using a FFN.\n",
"\n",
"\n",
"The technique implemented use ideas from [Graph Convolutional Networks](https://arxiv.org/abs/1609.02907),\n",
"[GraphSage](https://arxiv.org/abs/1706.02216), [Graph Isomorphism Network](https://arxiv.org/abs/1810.00826),\n",
"[Simple Graph Networks](https://arxiv.org/abs/1902.07153), and\n",
"[Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493).\n",
"Two other key techniques that are not covered are [Graph Attention Networks](https://arxiv.org/abs/1710.10903)\n",
"and [Message Passing Neural Networks](https://arxiv.org/abs/1704.01212)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "_3MIL-zRuw5q"
},
"outputs": [],
"source": [
"\n",
"class GraphConvLayer(layers.Layer):\n",
" def __init__(\n",
" self,\n",
" hidden_units,\n",
" dropout_rate=0.2,\n",
" aggregation_type=\"mean\",\n",
" combination_type=\"concat\",\n",
" normalize=False,\n",
" *args,\n",
" **kwargs,\n",
" ):\n",
" super(GraphConvLayer, self).__init__(*args, **kwargs)\n",
"\n",
" self.aggregation_type = aggregation_type\n",
" self.combination_type = combination_type\n",
" self.normalize = normalize\n",
"\n",
" self.ffn_prepare = create_ffn(hidden_units, dropout_rate)\n",
" if self.combination_type == \"gated\":\n",
" self.update_fn = layers.GRU(\n",
" units=hidden_units,\n",
" activation=\"tanh\",\n",
" recurrent_activation=\"sigmoid\",\n",
" dropout=dropout_rate,\n",
" return_state=True,\n",
" recurrent_dropout=dropout_rate,\n",
" )\n",
" else:\n",
" self.update_fn = create_ffn(hidden_units, dropout_rate)\n",
"\n",
" def prepare(self, node_repesentations, weights=None):\n",
" # node_repesentations shape is [num_edges, embedding_dim].\n",
" messages = self.ffn_prepare(node_repesentations)\n",
" if weights is not None:\n",
" messages = messages * tf.expand_dims(weights, -1)\n",
" return messages\n",
"\n",
" def aggregate(self, node_indices, neighbour_messages):\n",
" # node_indices shape is [num_edges].\n",
" # neighbour_messages shape: [num_edges, representation_dim].\n",
" num_nodes = tf.math.reduce_max(node_indices) + 1\n",
" if self.aggregation_type == \"sum\":\n",
" aggregated_message = tf.math.unsorted_segment_sum(\n",
" neighbour_messages, node_indices, num_segments=num_nodes\n",
" )\n",
" elif self.aggregation_type == \"mean\":\n",
" aggregated_message = tf.math.unsorted_segment_mean(\n",
" neighbour_messages, node_indices, num_segments=num_nodes\n",
" )\n",
" elif self.aggregation_type == \"max\":\n",
" aggregated_message = tf.math.unsorted_segment_max(\n",
" neighbour_messages, node_indices, num_segments=num_nodes\n",
" )\n",
" else:\n",
" raise ValueError(f\"Invalid aggregation type: {self.aggregation_type}.\")\n",
"\n",
" return aggregated_message\n",
"\n",
" def update(self, node_repesentations, aggregated_messages):\n",
" # node_repesentations shape is [num_nodes, representation_dim].\n",
" # aggregated_messages shape is [num_nodes, representation_dim].\n",
" if self.combination_type == \"gru\":\n",
" # Create a sequence of two elements for the GRU layer.\n",
" h = tf.stack([node_repesentations, aggregated_messages], axis=1)\n",
" elif self.combination_type == \"concat\":\n",
" # Concatenate the node_repesentations and aggregated_messages.\n",
" h = tf.concat([node_repesentations, aggregated_messages], axis=1)\n",
" elif self.combination_type == \"add\":\n",
" # Add node_repesentations and aggregated_messages.\n",
" h = node_repesentations + aggregated_messages\n",
" else:\n",
" raise ValueError(f\"Invalid combination type: {self.combination_type}.\")\n",
"\n",
" # Apply the processing function.\n",
" node_embeddings = self.update_fn(h)\n",
" if self.combination_type == \"gru\":\n",
" node_embeddings = tf.unstack(node_embeddings, axis=1)[-1]\n",
"\n",
" if self.normalize:\n",
" node_embeddings = tf.nn.l2_normalize(node_embeddings, axis=-1)\n",
" return node_embeddings\n",
"\n",
" def call(self, inputs):\n",
" \"\"\"Process the inputs to produce the node_embeddings.\n",
"\n",
" inputs: a tuple of three elements: node_repesentations, edges, edge_weights.\n",
" Returns: node_embeddings of shape [num_nodes, representation_dim].\n",
" \"\"\"\n",
"\n",
" node_repesentations, edges, edge_weights = inputs\n",
" # Get node_indices (source) and neighbour_indices (target) from edges.\n",
" node_indices, neighbour_indices = edges[0], edges[1]\n",
" # neighbour_repesentations shape is [num_edges, representation_dim].\n",
" neighbour_repesentations = tf.gather(node_repesentations, neighbour_indices)\n",
"\n",
" # Prepare the messages of the neighbours.\n",
" neighbour_messages = self.prepare(neighbour_repesentations, edge_weights)\n",
" # Aggregate the neighbour messages.\n",
" aggregated_messages = self.aggregate(node_indices, neighbour_messages)\n",
" # Update the node embedding with the neighbour messages.\n",
" return self.update(node_repesentations, aggregated_messages)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VR4RLN3Duw5q"
},
"source": [
"### Implement a graph neural network node classifier\n",
"\n",
"The GNN classification model follows the [Design Space for Graph Neural Networks](https://arxiv.org/abs/2011.08843) approach,\n",
"as follows:\n",
"\n",
"1. Apply preprocessing using FFN to the node features to generate initial node representations.\n",
"2. Apply one or more graph convolutional layer, with skip connections, to the node representation\n",
"to produce node embeddings.\n",
"3. Apply post-processing using FFN to the node embeddings to generat the final node embeddings.\n",
"4. Feed the node embeddings in a Softmax layer to predict the node class.\n",
"\n",
"Each graph convolutional layer added captures information from a further level of neighbours.\n",
"However, adding many graph convolutional layer can cause oversmoothing, where the model\n",
"produces similar embeddings for all the nodes.\n",
"\n",
"Note that the `graph_info` passed to the constructor of the Keras model, and used as a *property*\n",
"of the Keras model object, rather than input data for training or prediction.\n",
"The model will accept a **batch** of `node_indices`, which are used to lookup the\n",
"node features and neighbours from the `graph_info`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "2pKIeKFeuw5q"
},
"outputs": [],
"source": [
"\n",
"class GNNNodeClassifier(tf.keras.Model):\n",
" def __init__(\n",
" self,\n",
" graph_info,\n",
" num_classes,\n",
" hidden_units,\n",
" aggregation_type=\"sum\",\n",
" combination_type=\"concat\",\n",
" dropout_rate=0.2,\n",
" normalize=True,\n",
" *args,\n",
" **kwargs,\n",
" ):\n",
" super(GNNNodeClassifier, self).__init__(*args, **kwargs)\n",
"\n",
" # Unpack graph_info to three elements: node_features, edges, and edge_weight.\n",
" node_features, edges, edge_weights = graph_info\n",
" self.node_features = node_features\n",
" self.edges = edges\n",
" self.edge_weights = edge_weights\n",
" # Set edge_weights to ones if not provided.\n",
" if self.edge_weights is None:\n",
" self.edge_weights = tf.ones(shape=edges.shape[1])\n",
" # Scale edge_weights to sum to 1.\n",
" self.edge_weights = self.edge_weights / tf.math.reduce_sum(self.edge_weights)\n",
"\n",
" # Create a process layer.\n",
" self.preprocess = create_ffn(hidden_units, dropout_rate, name=\"preprocess\")\n",
" # Create the first GraphConv layer.\n",
" self.conv1 = GraphConvLayer(\n",
" hidden_units,\n",
" dropout_rate,\n",
" aggregation_type,\n",
" combination_type,\n",
" normalize,\n",
" name=\"graph_conv1\",\n",
" )\n",
" # Create the second GraphConv layer.\n",
" self.conv2 = GraphConvLayer(\n",
" hidden_units,\n",
" dropout_rate,\n",
" aggregation_type,\n",
" combination_type,\n",
" normalize,\n",
" name=\"graph_conv2\",\n",
" )\n",
" # Create a postprocess layer.\n",
" self.postprocess = create_ffn(hidden_units, dropout_rate, name=\"postprocess\")\n",
" # Create a compute logits layer.\n",
" self.compute_logits = layers.Dense(units=num_classes, name=\"logits\")\n",
"\n",
" def call(self, input_node_indices):\n",
" # Preprocess the node_features to produce node representations.\n",
" x = self.preprocess(self.node_features)\n",
" # Apply the first graph conv layer.\n",
" x1 = self.conv1((x, self.edges, self.edge_weights))\n",
" # Skip connection.\n",
" x = x1 + x\n",
" # Apply the second graph conv layer.\n",
" x2 = self.conv2((x, self.edges, self.edge_weights))\n",
" # Skip connection.\n",
" x = x2 + x\n",
" # Postprocess node embedding.\n",
" x = self.postprocess(x)\n",
" # Fetch node embeddings for the input node_indices.\n",
" node_embeddings = tf.gather(x, input_node_indices)\n",
" # Compute logits\n",
" return self.compute_logits(node_embeddings)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uC9yk68xuw5q"
},
"source": [
"Let's test instantiating and calling the GNN model.\n",
"Notice that if you provide `N` node indices, the output will be a tensor of shape `[N, num_classes]`,\n",
"regardless of the size of the graph."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "LzyN3Qosuw5q",
"outputId": "af617ed4-5bd5-4b28-941b-648a92ccc071",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GNN output shape: tf.Tensor(\n",
"[[-0.13177337 -0.07248863 0.0012523 -0.04644531 -0.01136824 0.16705114\n",
" -0.09815124]\n",
" [-0.05193972 0.00513246 0.00021477 -0.10366923 -0.0017973 -0.01399267\n",
" 0.07024869]\n",
" [-0.10507286 -0.01733486 -0.03712284 -0.11573985 0.00332271 0.0986744\n",
" 0.04108698]], shape=(3, 7), dtype=float32)\n",
"Model: \"gnn_model\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" preprocess (Sequential) (2708, 32) 52804 \n",
" \n",
" graph_conv1 (GraphConvLayer multiple 5888 \n",
" ) \n",
" \n",
" graph_conv2 (GraphConvLayer multiple 5888 \n",
" ) \n",
" \n",
" postprocess (Sequential) (2708, 32) 2368 \n",
" \n",
" logits (Dense) multiple 231 \n",
" \n",
"=================================================================\n",
"Total params: 67,179\n",
"Trainable params: 63,481\n",
"Non-trainable params: 3,698\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"gnn_model = GNNNodeClassifier(\n",
" graph_info=graph_info,\n",
" num_classes=num_classes,\n",
" hidden_units=hidden_units,\n",
" dropout_rate=dropout_rate,\n",
" name=\"gnn_model\",\n",
")\n",
"\n",
"print(\"GNN output shape:\", gnn_model([1, 10, 100]))\n",
"\n",
"gnn_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UStm2R2Fuw5q"
},
"source": [
"### Train the GNN model\n",
"\n",
"Note that we use the standard *supervised* cross-entropy loss to train the model.\n",
"However, we can add another *self-supervised* loss term for the generated node embeddings\n",
"that makes sure that neighbouring nodes in graph have similar representations, while faraway\n",
"nodes have dissimilar representations."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "dq5uE_onuw5r",
"outputId": "602a2e7d-a077-4fd2-db86-de12b53807c6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"5/5 [==============================] - 6s 361ms/step - loss: 2.4049 - acc: 0.1593 - val_loss: 1.8813 - val_acc: 0.2941\n",
"Epoch 2/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 2.0346 - acc: 0.2260 - val_loss: 1.8516 - val_acc: 0.3039\n",
"Epoch 3/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 1.9225 - acc: 0.2580 - val_loss: 1.8463 - val_acc: 0.3039\n",
"Epoch 4/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 1.8829 - acc: 0.2788 - val_loss: 1.8417 - val_acc: 0.3039\n",
"Epoch 5/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 1.8548 - acc: 0.2970 - val_loss: 1.8429 - val_acc: 0.3039\n",
"Epoch 6/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 1.8370 - acc: 0.3091 - val_loss: 1.8442 - val_acc: 0.3039\n",
"Epoch 7/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 1.8263 - acc: 0.3074 - val_loss: 1.8387 - val_acc: 0.3039\n",
"Epoch 8/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 1.8049 - acc: 0.3126 - val_loss: 1.8304 - val_acc: 0.3039\n",
"Epoch 9/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 1.8093 - acc: 0.3152 - val_loss: 1.8219 - val_acc: 0.3039\n",
"Epoch 10/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 1.7837 - acc: 0.3108 - val_loss: 1.8095 - val_acc: 0.3039\n",
"Epoch 11/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 1.7719 - acc: 0.3152 - val_loss: 1.7937 - val_acc: 0.3039\n",
"Epoch 12/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 1.7441 - acc: 0.3368 - val_loss: 1.7652 - val_acc: 0.3088\n",
"Epoch 13/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 1.7357 - acc: 0.3299 - val_loss: 1.7392 - val_acc: 0.3922\n",
"Epoch 14/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 1.7007 - acc: 0.3481 - val_loss: 1.7002 - val_acc: 0.4314\n",
"Epoch 15/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 1.6706 - acc: 0.3602 - val_loss: 1.6302 - val_acc: 0.4510\n",
"Epoch 16/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 1.6245 - acc: 0.3931 - val_loss: 1.6004 - val_acc: 0.4069\n",
"Epoch 17/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 1.5866 - acc: 0.3948 - val_loss: 1.5531 - val_acc: 0.4412\n",
"Epoch 18/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 1.4979 - acc: 0.4338 - val_loss: 1.4856 - val_acc: 0.4510\n",
"Epoch 19/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 1.4507 - acc: 0.4675 - val_loss: 1.4949 - val_acc: 0.4167\n",
"Epoch 20/300\n",
"5/5 [==============================] - 1s 257ms/step - loss: 1.4059 - acc: 0.4719 - val_loss: 1.5813 - val_acc: 0.3725\n",
"Epoch 21/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 1.3321 - acc: 0.4952 - val_loss: 1.6631 - val_acc: 0.3627\n",
"Epoch 22/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 1.3128 - acc: 0.5195 - val_loss: 1.7177 - val_acc: 0.3676\n",
"Epoch 23/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 1.2667 - acc: 0.5524 - val_loss: 1.6987 - val_acc: 0.3775\n",
"Epoch 24/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 1.2669 - acc: 0.5203 - val_loss: 1.6268 - val_acc: 0.3971\n",
"Epoch 25/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 1.2485 - acc: 0.5403 - val_loss: 1.4838 - val_acc: 0.4510\n",
"Epoch 26/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 1.1966 - acc: 0.5723 - val_loss: 1.5029 - val_acc: 0.4510\n",
"Epoch 27/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 1.1022 - acc: 0.6043 - val_loss: 1.5276 - val_acc: 0.4608\n",
"Epoch 28/300\n",
"5/5 [==============================] - 1s 187ms/step - loss: 1.1065 - acc: 0.6173 - val_loss: 1.5731 - val_acc: 0.4461\n",
"Epoch 29/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 1.0728 - acc: 0.6061 - val_loss: 1.5169 - val_acc: 0.4559\n",
"Epoch 30/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 1.0876 - acc: 0.6225 - val_loss: 1.2813 - val_acc: 0.5392\n",
"Epoch 31/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 1.0040 - acc: 0.6537 - val_loss: 1.2041 - val_acc: 0.5735\n",
"Epoch 32/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.9992 - acc: 0.6450 - val_loss: 1.1049 - val_acc: 0.6078\n",
"Epoch 33/300\n",
"5/5 [==============================] - 1s 173ms/step - loss: 0.9860 - acc: 0.6537 - val_loss: 1.1961 - val_acc: 0.5686\n",
"Epoch 34/300\n",
"5/5 [==============================] - 1s 173ms/step - loss: 0.9848 - acc: 0.6563 - val_loss: 1.3086 - val_acc: 0.5245\n",
"Epoch 35/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.9239 - acc: 0.6693 - val_loss: 1.2786 - val_acc: 0.5539\n",
"Epoch 36/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.9072 - acc: 0.6935 - val_loss: 1.0850 - val_acc: 0.5931\n",
"Epoch 37/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.8918 - acc: 0.6840 - val_loss: 1.0755 - val_acc: 0.5833\n",
"Epoch 38/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.8703 - acc: 0.6970 - val_loss: 1.0355 - val_acc: 0.6029\n",
"Epoch 39/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.8415 - acc: 0.7048 - val_loss: 0.9496 - val_acc: 0.6765\n",
"Epoch 40/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.8296 - acc: 0.7056 - val_loss: 0.8793 - val_acc: 0.7010\n",
"Epoch 41/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.8458 - acc: 0.7134 - val_loss: 0.9923 - val_acc: 0.6520\n",
"Epoch 42/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.8110 - acc: 0.7229 - val_loss: 1.0891 - val_acc: 0.6275\n",
"Epoch 43/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.8377 - acc: 0.7221 - val_loss: 0.9886 - val_acc: 0.6814\n",
"Epoch 44/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.7936 - acc: 0.7437 - val_loss: 0.8972 - val_acc: 0.7255\n",
"Epoch 45/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.7714 - acc: 0.7411 - val_loss: 0.8586 - val_acc: 0.7451\n",
"Epoch 46/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.7547 - acc: 0.7498 - val_loss: 0.9186 - val_acc: 0.7108\n",
"Epoch 47/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.7364 - acc: 0.7515 - val_loss: 0.9711 - val_acc: 0.6961\n",
"Epoch 48/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.7423 - acc: 0.7532 - val_loss: 0.9558 - val_acc: 0.7059\n",
"Epoch 49/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.7506 - acc: 0.7377 - val_loss: 0.9389 - val_acc: 0.7059\n",
"Epoch 50/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.6888 - acc: 0.7714 - val_loss: 0.9840 - val_acc: 0.6912\n",
"Epoch 51/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.6659 - acc: 0.7775 - val_loss: 1.0961 - val_acc: 0.6618\n",
"Epoch 52/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.6713 - acc: 0.7524 - val_loss: 0.9963 - val_acc: 0.7059\n",
"Epoch 53/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.7235 - acc: 0.7619 - val_loss: 0.9678 - val_acc: 0.7353\n",
"Epoch 54/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.6868 - acc: 0.7723 - val_loss: 1.0091 - val_acc: 0.7157\n",
"Epoch 55/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.7015 - acc: 0.7636 - val_loss: 1.0444 - val_acc: 0.7108\n",
"Epoch 56/300\n",
"5/5 [==============================] - 1s 174ms/step - loss: 0.6922 - acc: 0.7861 - val_loss: 0.9894 - val_acc: 0.7353\n",
"Epoch 57/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.6551 - acc: 0.7922 - val_loss: 0.8535 - val_acc: 0.7500\n",
"Epoch 58/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.6433 - acc: 0.7887 - val_loss: 0.8266 - val_acc: 0.7206\n",
"Epoch 59/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.6735 - acc: 0.7628 - val_loss: 0.8012 - val_acc: 0.7108\n",
"Epoch 60/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.6792 - acc: 0.7662 - val_loss: 0.8651 - val_acc: 0.7402\n",
"Epoch 61/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.6242 - acc: 0.7887 - val_loss: 1.0126 - val_acc: 0.6961\n",
"Epoch 62/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.6317 - acc: 0.7905 - val_loss: 1.0114 - val_acc: 0.6912\n",
"Epoch 63/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.5999 - acc: 0.7974 - val_loss: 0.8870 - val_acc: 0.7500\n",
"Epoch 64/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.5994 - acc: 0.7905 - val_loss: 0.8599 - val_acc: 0.7549\n",
"Epoch 65/300\n",
"5/5 [==============================] - 1s 187ms/step - loss: 0.6105 - acc: 0.7965 - val_loss: 0.8065 - val_acc: 0.7794\n",
"Epoch 66/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.6287 - acc: 0.7965 - val_loss: 0.7526 - val_acc: 0.7794\n",
"Epoch 67/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.6064 - acc: 0.7905 - val_loss: 0.7649 - val_acc: 0.7696\n",
"Epoch 68/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.6080 - acc: 0.8130 - val_loss: 0.7816 - val_acc: 0.7745\n",
"Epoch 69/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.6386 - acc: 0.7965 - val_loss: 0.7804 - val_acc: 0.7696\n",
"Epoch 70/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.5274 - acc: 0.8225 - val_loss: 0.8096 - val_acc: 0.7696\n",
"Epoch 71/300\n",
"5/5 [==============================] - 2s 346ms/step - loss: 0.5985 - acc: 0.8121 - val_loss: 0.8077 - val_acc: 0.7647\n",
"Epoch 72/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.5420 - acc: 0.8268 - val_loss: 0.8192 - val_acc: 0.7794\n",
"Epoch 73/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.5899 - acc: 0.8069 - val_loss: 0.7991 - val_acc: 0.7892\n",
"Epoch 74/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.5833 - acc: 0.8156 - val_loss: 0.7684 - val_acc: 0.8088\n",
"Epoch 75/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.5624 - acc: 0.8165 - val_loss: 0.7718 - val_acc: 0.7990\n",
"Epoch 76/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.5903 - acc: 0.8087 - val_loss: 0.7688 - val_acc: 0.7794\n",
"Epoch 77/300\n",
"5/5 [==============================] - 1s 174ms/step - loss: 0.5405 - acc: 0.8242 - val_loss: 0.7883 - val_acc: 0.7598\n",
"Epoch 78/300\n",
"5/5 [==============================] - 1s 172ms/step - loss: 0.5398 - acc: 0.8338 - val_loss: 0.8015 - val_acc: 0.7598\n",
"Epoch 79/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.5143 - acc: 0.8372 - val_loss: 0.8468 - val_acc: 0.7598\n",
"Epoch 80/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.5629 - acc: 0.8286 - val_loss: 0.8751 - val_acc: 0.7745\n",
"Epoch 81/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.5439 - acc: 0.8242 - val_loss: 0.7621 - val_acc: 0.7843\n",
"Epoch 82/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4988 - acc: 0.8320 - val_loss: 0.7245 - val_acc: 0.7990\n",
"Epoch 83/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.5240 - acc: 0.8268 - val_loss: 0.8250 - val_acc: 0.7745\n",
"Epoch 84/300\n",
"5/5 [==============================] - 1s 174ms/step - loss: 0.5938 - acc: 0.8208 - val_loss: 0.9195 - val_acc: 0.7598\n",
"Epoch 85/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.5021 - acc: 0.8476 - val_loss: 0.7720 - val_acc: 0.7794\n",
"Epoch 86/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.5197 - acc: 0.8234 - val_loss: 0.7107 - val_acc: 0.7892\n",
"Epoch 87/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.5217 - acc: 0.8260 - val_loss: 0.7232 - val_acc: 0.7892\n",
"Epoch 88/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.4999 - acc: 0.8381 - val_loss: 0.7817 - val_acc: 0.7941\n",
"Epoch 89/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.5809 - acc: 0.8156 - val_loss: 0.8480 - val_acc: 0.7745\n",
"Epoch 90/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4845 - acc: 0.8459 - val_loss: 0.8378 - val_acc: 0.7451\n",
"Epoch 91/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.4934 - acc: 0.8381 - val_loss: 0.7080 - val_acc: 0.7990\n",
"Epoch 92/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.5112 - acc: 0.8260 - val_loss: 0.7277 - val_acc: 0.7990\n",
"Epoch 93/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4868 - acc: 0.8372 - val_loss: 0.7614 - val_acc: 0.7843\n",
"Epoch 94/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4688 - acc: 0.8372 - val_loss: 0.8042 - val_acc: 0.7696\n",
"Epoch 95/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.4915 - acc: 0.8433 - val_loss: 0.7409 - val_acc: 0.7794\n",
"Epoch 96/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.5070 - acc: 0.8407 - val_loss: 0.6990 - val_acc: 0.8137\n",
"Epoch 97/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.5326 - acc: 0.8433 - val_loss: 0.6828 - val_acc: 0.8039\n",
"Epoch 98/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4777 - acc: 0.8580 - val_loss: 0.7448 - val_acc: 0.7794\n",
"Epoch 99/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.4801 - acc: 0.8450 - val_loss: 0.7936 - val_acc: 0.7647\n",
"Epoch 100/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.4554 - acc: 0.8494 - val_loss: 0.7298 - val_acc: 0.7794\n",
"Epoch 101/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4668 - acc: 0.8511 - val_loss: 0.7212 - val_acc: 0.8088\n",
"Epoch 102/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4965 - acc: 0.8476 - val_loss: 0.6985 - val_acc: 0.8137\n",
"Epoch 103/300\n",
"5/5 [==============================] - 1s 185ms/step - loss: 0.4310 - acc: 0.8623 - val_loss: 0.7274 - val_acc: 0.7990\n",
"Epoch 104/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4866 - acc: 0.8442 - val_loss: 0.7516 - val_acc: 0.7892\n",
"Epoch 105/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.5240 - acc: 0.8416 - val_loss: 0.7333 - val_acc: 0.7892\n",
"Epoch 106/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4402 - acc: 0.8641 - val_loss: 0.7121 - val_acc: 0.8088\n",
"Epoch 107/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4543 - acc: 0.8511 - val_loss: 0.7209 - val_acc: 0.8137\n",
"Epoch 108/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.4249 - acc: 0.8675 - val_loss: 0.7102 - val_acc: 0.8235\n",
"Epoch 109/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.4437 - acc: 0.8623 - val_loss: 0.6899 - val_acc: 0.8088\n",
"Epoch 110/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.4610 - acc: 0.8545 - val_loss: 0.6650 - val_acc: 0.8088\n",
"Epoch 111/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4177 - acc: 0.8623 - val_loss: 0.6573 - val_acc: 0.8186\n",
"Epoch 112/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.5059 - acc: 0.8459 - val_loss: 0.6861 - val_acc: 0.8039\n",
"Epoch 113/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4425 - acc: 0.8554 - val_loss: 0.7153 - val_acc: 0.7941\n",
"Epoch 114/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.4507 - acc: 0.8649 - val_loss: 0.6896 - val_acc: 0.7892\n",
"Epoch 115/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4438 - acc: 0.8459 - val_loss: 0.6756 - val_acc: 0.7941\n",
"Epoch 116/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4410 - acc: 0.8606 - val_loss: 0.7190 - val_acc: 0.7990\n",
"Epoch 117/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4759 - acc: 0.8623 - val_loss: 0.7020 - val_acc: 0.7794\n",
"Epoch 118/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4689 - acc: 0.8554 - val_loss: 0.6666 - val_acc: 0.7941\n",
"Epoch 119/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4192 - acc: 0.8753 - val_loss: 0.6846 - val_acc: 0.8137\n",
"Epoch 120/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4292 - acc: 0.8719 - val_loss: 0.7038 - val_acc: 0.8186\n",
"Epoch 121/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4110 - acc: 0.8684 - val_loss: 0.7041 - val_acc: 0.7990\n",
"Epoch 122/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.4522 - acc: 0.8589 - val_loss: 0.7177 - val_acc: 0.7843\n",
"Epoch 123/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.3910 - acc: 0.8736 - val_loss: 0.6952 - val_acc: 0.8039\n",
"Epoch 124/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.3967 - acc: 0.8874 - val_loss: 0.6713 - val_acc: 0.8137\n",
"Epoch 125/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.3636 - acc: 0.8797 - val_loss: 0.6928 - val_acc: 0.8039\n",
"Epoch 126/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4428 - acc: 0.8632 - val_loss: 0.6785 - val_acc: 0.8039\n",
"Epoch 127/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4599 - acc: 0.8563 - val_loss: 0.6620 - val_acc: 0.8088\n",
"Epoch 128/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4261 - acc: 0.8693 - val_loss: 0.6475 - val_acc: 0.8235\n",
"Epoch 129/300\n",
"5/5 [==============================] - 1s 174ms/step - loss: 0.4307 - acc: 0.8615 - val_loss: 0.6481 - val_acc: 0.8137\n",
"Epoch 130/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.4500 - acc: 0.8615 - val_loss: 0.6422 - val_acc: 0.8088\n",
"Epoch 131/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4484 - acc: 0.8511 - val_loss: 0.6391 - val_acc: 0.8137\n",
"Epoch 132/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4360 - acc: 0.8684 - val_loss: 0.6310 - val_acc: 0.8284\n",
"Epoch 133/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4087 - acc: 0.8753 - val_loss: 0.6484 - val_acc: 0.8137\n",
"Epoch 134/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4862 - acc: 0.8476 - val_loss: 0.6482 - val_acc: 0.7990\n",
"Epoch 135/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4409 - acc: 0.8563 - val_loss: 0.6615 - val_acc: 0.7696\n",
"Epoch 136/300\n",
"5/5 [==============================] - 1s 186ms/step - loss: 0.4310 - acc: 0.8511 - val_loss: 0.6790 - val_acc: 0.7941\n",
"Epoch 137/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4593 - acc: 0.8528 - val_loss: 0.7133 - val_acc: 0.7892\n",
"Epoch 138/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.3991 - acc: 0.8727 - val_loss: 0.6844 - val_acc: 0.7941\n",
"Epoch 139/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4003 - acc: 0.8675 - val_loss: 0.6996 - val_acc: 0.7745\n",
"Epoch 140/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.3938 - acc: 0.8771 - val_loss: 0.6966 - val_acc: 0.7941\n",
"Epoch 141/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4248 - acc: 0.8684 - val_loss: 0.7128 - val_acc: 0.7892\n",
"Epoch 142/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.3955 - acc: 0.8736 - val_loss: 0.7160 - val_acc: 0.7598\n",
"Epoch 143/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.4109 - acc: 0.8736 - val_loss: 0.7118 - val_acc: 0.7892\n",
"Epoch 144/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.4185 - acc: 0.8771 - val_loss: 0.6567 - val_acc: 0.8137\n",
"Epoch 145/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4493 - acc: 0.8615 - val_loss: 0.6244 - val_acc: 0.8137\n",
"Epoch 146/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.4044 - acc: 0.8701 - val_loss: 0.6244 - val_acc: 0.8088\n",
"Epoch 147/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4137 - acc: 0.8788 - val_loss: 0.6642 - val_acc: 0.7843\n",
"Epoch 148/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4050 - acc: 0.8667 - val_loss: 0.6638 - val_acc: 0.7941\n",
"Epoch 149/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.3542 - acc: 0.8970 - val_loss: 0.6557 - val_acc: 0.7990\n",
"Epoch 150/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.3614 - acc: 0.8762 - val_loss: 0.6541 - val_acc: 0.7990\n",
"Epoch 151/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.3905 - acc: 0.8771 - val_loss: 0.6550 - val_acc: 0.8039\n",
"Epoch 152/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.3629 - acc: 0.8727 - val_loss: 0.6687 - val_acc: 0.7941\n",
"Epoch 153/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.3993 - acc: 0.8814 - val_loss: 0.6799 - val_acc: 0.7794\n",
"Epoch 154/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.3621 - acc: 0.8840 - val_loss: 0.6749 - val_acc: 0.7892\n",
"Epoch 155/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3906 - acc: 0.8805 - val_loss: 0.6742 - val_acc: 0.7843\n",
"Epoch 156/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.3714 - acc: 0.8788 - val_loss: 0.6614 - val_acc: 0.7990\n",
"Epoch 157/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3995 - acc: 0.8814 - val_loss: 0.6657 - val_acc: 0.8039\n",
"Epoch 158/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.3422 - acc: 0.8840 - val_loss: 0.6847 - val_acc: 0.7941\n",
"Epoch 159/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.4034 - acc: 0.8667 - val_loss: 0.6824 - val_acc: 0.7892\n",
"Epoch 160/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.3856 - acc: 0.8805 - val_loss: 0.6882 - val_acc: 0.7843\n",
"Epoch 161/300\n",
"5/5 [==============================] - 1s 175ms/step - loss: 0.4233 - acc: 0.8719 - val_loss: 0.7202 - val_acc: 0.7696\n",
"Epoch 162/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.4090 - acc: 0.8779 - val_loss: 0.7033 - val_acc: 0.7745\n",
"Epoch 163/300\n",
"5/5 [==============================] - 1s 177ms/step - loss: 0.3960 - acc: 0.8727 - val_loss: 0.6586 - val_acc: 0.7892\n",
"Epoch 164/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.4250 - acc: 0.8675 - val_loss: 0.6743 - val_acc: 0.8039\n",
"Epoch 165/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.4160 - acc: 0.8719 - val_loss: 0.6857 - val_acc: 0.7941\n",
"Epoch 166/300\n",
"5/5 [==============================] - 1s 176ms/step - loss: 0.3734 - acc: 0.8771 - val_loss: 0.7236 - val_acc: 0.7745\n",
"Epoch 167/300\n",
"5/5 [==============================] - 1s 180ms/step - loss: 0.3969 - acc: 0.8649 - val_loss: 0.7200 - val_acc: 0.7794\n",
"Epoch 168/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.3847 - acc: 0.8727 - val_loss: 0.7000 - val_acc: 0.7990\n",
"Epoch 169/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.3836 - acc: 0.8823 - val_loss: 0.6865 - val_acc: 0.8088\n",
"Epoch 170/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.3666 - acc: 0.9004 - val_loss: 0.6808 - val_acc: 0.8039\n",
"Epoch 171/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.3533 - acc: 0.8996 - val_loss: 0.7376 - val_acc: 0.7843\n",
"Epoch 172/300\n",
"5/5 [==============================] - 1s 184ms/step - loss: 0.3558 - acc: 0.8944 - val_loss: 0.7428 - val_acc: 0.7843\n",
"Epoch 173/300\n",
"5/5 [==============================] - 1s 185ms/step - loss: 0.3856 - acc: 0.8857 - val_loss: 0.7325 - val_acc: 0.7892\n",
"Epoch 174/300\n",
"5/5 [==============================] - 1s 183ms/step - loss: 0.3539 - acc: 0.8848 - val_loss: 0.7974 - val_acc: 0.7794\n",
"Epoch 175/300\n",
"5/5 [==============================] - 1s 179ms/step - loss: 0.4276 - acc: 0.8753 - val_loss: 0.7713 - val_acc: 0.7892\n",
"Epoch 176/300\n",
"5/5 [==============================] - 1s 174ms/step - loss: 0.4007 - acc: 0.8753 - val_loss: 0.7243 - val_acc: 0.7843\n",
"Epoch 177/300\n",
"5/5 [==============================] - 1s 182ms/step - loss: 0.3777 - acc: 0.8866 - val_loss: 0.7694 - val_acc: 0.7794\n",
"Epoch 178/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3998 - acc: 0.8745 - val_loss: 0.7392 - val_acc: 0.7745\n",
"Epoch 179/300\n",
"5/5 [==============================] - 1s 178ms/step - loss: 0.4062 - acc: 0.8753 - val_loss: 0.6859 - val_acc: 0.8039\n",
"Epoch 180/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3371 - acc: 0.8952 - val_loss: 0.6828 - val_acc: 0.8088\n",
"Epoch 181/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3429 - acc: 0.8935 - val_loss: 0.6903 - val_acc: 0.7941\n",
"Epoch 182/300\n",
"5/5 [==============================] - 1s 181ms/step - loss: 0.3259 - acc: 0.8944 - val_loss: 0.7370 - val_acc: 0.7745\n"
]
}
],
"source": [
"x_train = train_data.paper_id.to_numpy()\n",
"history = run_experiment(gnn_model, x_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ByHboNqLuw5r"
},
"source": [
"Let's plot the learning curves"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "ZI-J0tnVuw5r",
"outputId": "ad5918c7-9d74-4010-b3e2-577f4327f74c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"display_learning_curves(history)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UO6Ss5eBuw5r"
},
"source": [
"Now we evaluate the GNN model on the test data split.\n",
"The results may vary depending on the training sample, however the GNN model always outperforms\n",
"the baseline model in terms of the test accuracy."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "r8OoqyKjuw5r",
"outputId": "efbf256e-88ef-45af-ac2a-f1265fcf4db0",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test accuracy: 79.69%\n"
]
}
],
"source": [
"x_test = test_data.paper_id.to_numpy()\n",
"_, test_accuracy = gnn_model.evaluate(x=x_test, y=y_test, verbose=0)\n",
"print(f\"Test accuracy: {round(test_accuracy * 100, 2)}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ik1cqrxauw5r"
},
"source": [
"### Examine the GNN model predictions\n",
"\n",
"Let's add the new instances as nodes to the `node_features`, and generate links\n",
"(citations) to existing nodes."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "N5ubeNmJuw5r"
},
"outputs": [],
"source": [
"# First we add the N new_instances as nodes to the graph\n",
"# by appending the new_instance to node_features.\n",
"num_nodes = node_features.shape[0]\n",
"new_node_features = np.concatenate([node_features, new_instances])\n",
"# Second we add the M edges (citations) from each new node to a set\n",
"# of existing nodes in a particular subject\n",
"new_node_indices = [i + num_nodes for i in range(num_classes)]\n",
"new_citations = []\n",
"for subject_idx, group in papers.groupby(\"subject\"):\n",
" subject_papers = list(group.paper_id)\n",
" # Select random x papers specific subject.\n",
" selected_paper_indices1 = np.random.choice(subject_papers, 5)\n",
" # Select random y papers from any subject (where y < x).\n",
" selected_paper_indices2 = np.random.choice(list(papers.paper_id), 2)\n",
" # Merge the selected paper indices.\n",
" selected_paper_indices = np.concatenate(\n",
" [selected_paper_indices1, selected_paper_indices2], axis=0\n",
" )\n",
" # Create edges between a citing paper idx and the selected cited papers.\n",
" citing_paper_indx = new_node_indices[subject_idx]\n",
" for cited_paper_idx in selected_paper_indices:\n",
" new_citations.append([citing_paper_indx, cited_paper_idx])\n",
"\n",
"new_citations = np.array(new_citations).T\n",
"new_edges = np.concatenate([edges, new_citations], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iUk1GzcNuw5r"
},
"source": [
"Now let's update the `node_features` and the `edges` in the GNN model."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "cUC80ZRFuw5r",
"outputId": "220e8f6e-e95a-488f-aeae-7973be3272bb",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Original node_features shape: (2708, 1433)\n",
"Original edges shape: (2, 5429)\n",
"New node_features shape: (2715, 1433)\n",
"New edges shape: (2, 5478)\n",
"Instance 1:\n",
"- Case_Based: 30.51%\n",
"- Genetic_Algorithms: 18.0%\n",
"- Neural_Networks: 3.5%\n",
"- Probabilistic_Methods: 2.29%\n",
"- Reinforcement_Learning: 39.68%\n",
"- Rule_Learning: 1.64%\n",
"- Theory: 4.38%\n",
"Instance 2:\n",
"- Case_Based: 0.18%\n",
"- Genetic_Algorithms: 95.39%\n",
"- Neural_Networks: 4.01%\n",
"- Probabilistic_Methods: 0.05%\n",
"- Reinforcement_Learning: 0.22%\n",
"- Rule_Learning: 0.04%\n",
"- Theory: 0.1%\n",
"Instance 3:\n",
"- Case_Based: 0.27%\n",
"- Genetic_Algorithms: 0.19%\n",
"- Neural_Networks: 91.87%\n",
"- Probabilistic_Methods: 6.27%\n",
"- Reinforcement_Learning: 0.52%\n",
"- Rule_Learning: 0.1%\n",
"- Theory: 0.78%\n",
"Instance 4:\n",
"- Case_Based: 65.41%\n",
"- Genetic_Algorithms: 1.8%\n",
"- Neural_Networks: 4.07%\n",
"- Probabilistic_Methods: 9.52%\n",
"- Reinforcement_Learning: 1.07%\n",
"- Rule_Learning: 13.52%\n",
"- Theory: 4.6%\n",
"Instance 5:\n",
"- Case_Based: 0.98%\n",
"- Genetic_Algorithms: 77.79%\n",
"- Neural_Networks: 0.96%\n",
"- Probabilistic_Methods: 0.07%\n",
"- Reinforcement_Learning: 19.92%\n",
"- Rule_Learning: 0.04%\n",
"- Theory: 0.24%\n",
"Instance 6:\n",
"- Case_Based: 0.87%\n",
"- Genetic_Algorithms: 0.17%\n",
"- Neural_Networks: 56.18%\n",
"- Probabilistic_Methods: 1.64%\n",
"- Reinforcement_Learning: 0.6%\n",
"- Rule_Learning: 1.8%\n",
"- Theory: 38.73%\n",
"Instance 7:\n",
"- Case_Based: 0.35%\n",
"- Genetic_Algorithms: 0.09%\n",
"- Neural_Networks: 30.04%\n",
"- Probabilistic_Methods: 67.39%\n",
"- Reinforcement_Learning: 0.8%\n",
"- Rule_Learning: 0.2%\n",
"- Theory: 1.13%\n"
]
}
],
"source": [
"print(\"Original node_features shape:\", gnn_model.node_features.shape)\n",
"print(\"Original edges shape:\", gnn_model.edges.shape)\n",
"gnn_model.node_features = new_node_features\n",
"gnn_model.edges = new_edges\n",
"gnn_model.edge_weights = tf.ones(shape=new_edges.shape[1])\n",
"print(\"New node_features shape:\", gnn_model.node_features.shape)\n",
"print(\"New edges shape:\", gnn_model.edges.shape)\n",
"\n",
"logits = gnn_model.predict(tf.convert_to_tensor(new_node_indices))\n",
"probabilities = keras.activations.softmax(tf.convert_to_tensor(logits)).numpy()\n",
"display_class_probabilities(probabilities)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wy6gCYzTuw5r"
},
"source": [
"Notice that the probabilities of the expected subjects\n",
"(to which several citations are added) are higher compared to the baseline model."
]
},
{
"cell_type": "code",
"source": [
"!pip install huggingface-hub\n",
"!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n",
"!sudo apt-get install git-lfs\n",
"!git-lfs install"
],
"metadata": {
"id": "3PDc8EE1vyjx",
"outputId": "bb76bbbe-cd13-4eb3-e2a0-3aeb22774a28",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting huggingface-hub\n",
" Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
"\u001b[?25l\r\u001b[K |█████▎ | 10 kB 17.4 MB/s eta 0:00:01\r\u001b[K |██████████▋ | 20 kB 9.8 MB/s eta 0:00:01\r\u001b[K |███████████████▉ | 30 kB 7.9 MB/s eta 0:00:01\r\u001b[K |█████████████████████▏ | 40 kB 7.2 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▌ | 51 kB 4.1 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 61 kB 4.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61 kB 388 kB/s \n",
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.4.0)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.8.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.62.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.10.0.2)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (21.3)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (2.23.0)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.13)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub) (3.0.6)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub) (3.6.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2021.10.8)\n",
"Installing collected packages: huggingface-hub\n",
"Successfully installed huggingface-hub-0.2.1\n",
"Detected operating system as Ubuntu/bionic.\n",
"Checking for curl...\n",
"Detected curl...\n",
"Checking for gpg...\n",
"Detected gpg...\n",
"Running apt-get update... done.\n",
"Installing apt-transport-https... done.\n",
"Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n",
"Importing packagecloud gpg key... done.\n",
"Running apt-get update... done.\n",
"\n",
"The repository is setup! You can now install packages.\n",
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"The following NEW packages will be installed:\n",
" git-lfs\n",
"0 upgraded, 1 newly installed, 0 to remove and 67 not upgraded.\n",
"Need to get 6,526 kB of archives.\n",
"After this operation, 14.7 MB of additional disk space will be used.\n",
"Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 3.0.2 [6,526 kB]\n",
"Fetched 6,526 kB in 1s (6,956 kB/s)\n",
"debconf: unable to initialize frontend: Dialog\n",
"debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
"debconf: falling back to frontend: Readline\n",
"debconf: unable to initialize frontend: Readline\n",
"debconf: (This frontend requires a controlling tty.)\n",
"debconf: falling back to frontend: Teletype\n",
"dpkg-preconfigure: unable to re-open stdin: \n",
"Selecting previously unselected package git-lfs.\n",
"(Reading database ... 155226 files and directories currently installed.)\n",
"Preparing to unpack .../git-lfs_3.0.2_amd64.deb ...\n",
"Unpacking git-lfs (3.0.2) ...\n",
"Setting up git-lfs (3.0.2) ...\n",
"Git LFS initialized.\n",
"Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
"Git LFS initialized.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!huggingface-cli login"
],
"metadata": {
"id": "ggB9kTy5v00W",
"outputId": "54caf99c-a3a1-4fe0-bb1c-c0f14e61464e",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
"\n",
" To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
" (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
" \n",
"Token: \n",
"Login successful\n",
"Your token has been saved to /root/.huggingface/token\n",
"\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
"You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
"\n",
"git config --global credential.helper store\u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub.keras_mixin import push_to_hub_keras\n",
"push_to_hub_keras(model = gnn_model, repo_url = \"https://huggingface.co/keras-io/graph-attention-nets\", organization = \"keras-io\")\n"
],
"metadata": {
"id": "ihaggFkGv16r",
"outputId": "532efb2a-3081-414f-d458-1e5b7d729000",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187,
"referenced_widgets": [
"02a900330032498cab8abbeaeda25f60",
"fac067e536ff4f858d85821c1dd27c53",
"f32a408fe89d46dd9c763f79f508849c",
"d233cdf5234d4335917f7076da6890ec",
"ab25e23f9a254d8a93c0a3b26a23d65d",
"fea4185b344f4a7e99c4ba6e88c7466e",
"55d90faad141451ba879867f1067349c",
"371bbe061ae047f0928b29ee6b98b205",
"0e243ed4268047318dafd43e5f9da334",
"1a0f8a3250b04f008ee59a177dc30f51",
"bffee562cd2845e284d0e725d311e41c",
"aaaa0eef43c644a2a051e6039848bb6a",
"2b46334cb01a49fa856e730126493ec1",
"1eb6f9a0509147eab1370ee7ad74faf0",
"3798b6c1873841a9854e79ca363336e1",
"1af6815d69c34979af48feec45c1b80d",
"2a9de25067024b039d33be465575a56b",
"d348cc3a5d3d49609df8c083236a7ce8",
"963ad473b34d43db82dc6dc69f5f70f6",
"ad0b4d1d291b4b138a226959f35330dd",
"3fe0c52755b248278d2c64bc3d0cbf18",
"fbe7a8ced9d14176a72e5302c5e7bc3f"
]
}
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Cloning https://huggingface.co/keras-io/graph-attention-nets into local empty directory.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO:tensorflow:Assets written to: graph-attention-nets/assets\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "02a900330032498cab8abbeaeda25f60",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Upload file saved_model.pb: 0%| | 32.0k/211M [00:00 main\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'https://huggingface.co/keras-io/graph-attention-nets/commit/88c5cf87d3e5138347f4ff448cc3e2f2c78d1301'"
]
},
"metadata": {},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "JTwh_3jRxVlk"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "gnn_citations",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"02a900330032498cab8abbeaeda25f60": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HBoxView",
"_dom_classes": [],
"_model_name": "HBoxModel",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.5.0",
"box_style": "",
"layout": "IPY_MODEL_fac067e536ff4f858d85821c1dd27c53",
"_model_module": "@jupyter-widgets/controls",
"children": [
"IPY_MODEL_f32a408fe89d46dd9c763f79f508849c",
"IPY_MODEL_d233cdf5234d4335917f7076da6890ec",
"IPY_MODEL_ab25e23f9a254d8a93c0a3b26a23d65d"
]
}
},
"fac067e536ff4f858d85821c1dd27c53": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"f32a408fe89d46dd9c763f79f508849c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HTMLView",
"style": "IPY_MODEL_fea4185b344f4a7e99c4ba6e88c7466e",
"_dom_classes": [],
"description": "",
"_model_name": "HTMLModel",
"placeholder": "",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": "Upload file saved_model.pb: 100%",
"_view_count": null,
"_view_module_version": "1.5.0",
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_55d90faad141451ba879867f1067349c"
}
},
"d233cdf5234d4335917f7076da6890ec": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "ProgressView",
"style": "IPY_MODEL_371bbe061ae047f0928b29ee6b98b205",
"_dom_classes": [],
"description": "",
"_model_name": "FloatProgressModel",
"bar_style": "success",
"max": 220956677,
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": 220956677,
"_view_count": null,
"_view_module_version": "1.5.0",
"orientation": "horizontal",
"min": 0,
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_0e243ed4268047318dafd43e5f9da334"
}
},
"ab25e23f9a254d8a93c0a3b26a23d65d": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HTMLView",
"style": "IPY_MODEL_1a0f8a3250b04f008ee59a177dc30f51",
"_dom_classes": [],
"description": "",
"_model_name": "HTMLModel",
"placeholder": "",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": " 211M/211M [00:41<00:00, 2.29MB/s]",
"_view_count": null,
"_view_module_version": "1.5.0",
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_bffee562cd2845e284d0e725d311e41c"
}
},
"fea4185b344f4a7e99c4ba6e88c7466e": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "DescriptionStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"_model_module": "@jupyter-widgets/controls"
}
},
"55d90faad141451ba879867f1067349c": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"371bbe061ae047f0928b29ee6b98b205": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "ProgressStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"bar_color": null,
"_model_module": "@jupyter-widgets/controls"
}
},
"0e243ed4268047318dafd43e5f9da334": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"1a0f8a3250b04f008ee59a177dc30f51": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "DescriptionStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"_model_module": "@jupyter-widgets/controls"
}
},
"bffee562cd2845e284d0e725d311e41c": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"aaaa0eef43c644a2a051e6039848bb6a": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HBoxView",
"_dom_classes": [],
"_model_name": "HBoxModel",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.5.0",
"box_style": "",
"layout": "IPY_MODEL_2b46334cb01a49fa856e730126493ec1",
"_model_module": "@jupyter-widgets/controls",
"children": [
"IPY_MODEL_1eb6f9a0509147eab1370ee7ad74faf0",
"IPY_MODEL_3798b6c1873841a9854e79ca363336e1",
"IPY_MODEL_1af6815d69c34979af48feec45c1b80d"
]
}
},
"2b46334cb01a49fa856e730126493ec1": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"1eb6f9a0509147eab1370ee7ad74faf0": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HTMLView",
"style": "IPY_MODEL_2a9de25067024b039d33be465575a56b",
"_dom_classes": [],
"description": "",
"_model_name": "HTMLModel",
"placeholder": "",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": "Upload file keras_metadata.pb: 100%",
"_view_count": null,
"_view_module_version": "1.5.0",
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_d348cc3a5d3d49609df8c083236a7ce8"
}
},
"3798b6c1873841a9854e79ca363336e1": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "ProgressView",
"style": "IPY_MODEL_963ad473b34d43db82dc6dc69f5f70f6",
"_dom_classes": [],
"description": "",
"_model_name": "FloatProgressModel",
"bar_style": "success",
"max": 73082,
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": 73082,
"_view_count": null,
"_view_module_version": "1.5.0",
"orientation": "horizontal",
"min": 0,
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_ad0b4d1d291b4b138a226959f35330dd"
}
},
"1af6815d69c34979af48feec45c1b80d": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "HTMLView",
"style": "IPY_MODEL_3fe0c52755b248278d2c64bc3d0cbf18",
"_dom_classes": [],
"description": "",
"_model_name": "HTMLModel",
"placeholder": "",
"_view_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"value": " 71.4k/71.4k [00:40<00:00, 1.00kB/s]",
"_view_count": null,
"_view_module_version": "1.5.0",
"description_tooltip": null,
"_model_module": "@jupyter-widgets/controls",
"layout": "IPY_MODEL_fbe7a8ced9d14176a72e5302c5e7bc3f"
}
},
"2a9de25067024b039d33be465575a56b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "DescriptionStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"_model_module": "@jupyter-widgets/controls"
}
},
"d348cc3a5d3d49609df8c083236a7ce8": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"963ad473b34d43db82dc6dc69f5f70f6": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "ProgressStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"bar_color": null,
"_model_module": "@jupyter-widgets/controls"
}
},
"ad0b4d1d291b4b138a226959f35330dd": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
},
"3fe0c52755b248278d2c64bc3d0cbf18": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_view_name": "StyleView",
"_model_name": "DescriptionStyleModel",
"description_width": "",
"_view_module": "@jupyter-widgets/base",
"_model_module_version": "1.5.0",
"_view_count": null,
"_view_module_version": "1.2.0",
"_model_module": "@jupyter-widgets/controls"
}
},
"fbe7a8ced9d14176a72e5302c5e7bc3f": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}