{ "cells": [ { "cell_type": "markdown", "id": "d97a84c9", "metadata": {}, "source": [ "# BaseAttentive: Quick Start Guide\n", "\n", "This notebook demonstrates how to create and configure a BaseAttentive model.\n", "\n", "## What is BaseAttentive?\n", "\n", "BaseAttentive is a foundational blueprint for building powerful, data-driven, sequence-to-sequence time series forecasting models with advanced attention mechanisms.\n", "\n", "### Key Features\n", "- **Flexible Architecture**: Hybrid (LSTM+Attention) or Pure Transformer\n", "- **Multiple Attention Types**: Cross, Hierarchical, Memory-Augmented\n", "- **Feature Selection**: Variable Selection Networks (VSN) for learnable feature importance\n", "- **Uncertainty Quantification**: Quantile-based forecasting for confidence intervals\n", "- **Dynamic Time Warping**: Automatic temporal alignment" ] }, { "cell_type": "markdown", "id": "1e2bef14", "metadata": {}, "source": [ "## Step 1: Installation\n", "\n", "```bash\n", "pip install base-attentive==2.2.0\n", "```\n", "\n", "Or for development:\n", "```bash\n", "git clone https://github.com/earthai-tech/base-attentive.git\n", "cd base-attentive\n", "pip install -e \".[dev]\"\n", "```\n", "\n", "> **v2.2.0 note:** `BASE_ATTENTIVE_BACKEND` must be set before importing\n", "> `base_attentive`. Run the **Backend Setup** cell above first." ] }, { "cell_type": "code", "execution_count": 1, "id": "fdf276e9", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:46.416095Z", "iopub.status.busy": "2026-04-21T01:43:46.416095Z", "iopub.status.idle": "2026-04-21T01:43:50.162692Z", "shell.execute_reply": "2026-04-21T01:43:50.161362Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Backend: tensorflow\n" ] } ], "source": [ "# ── v2.2.0 Backend Setup ─────────────────────────────────────────────────────\n", "# BASE_ATTENTIVE_BACKEND must be set *before* importing base_attentive.\n", "# Choose your installed backend: \"tensorflow\" | \"torch\" | \"jax\" | \"auto\"\n", "import os\n", "os.environ.setdefault(\"BASE_ATTENTIVE_BACKEND\", \"tensorflow\")\n", "os.environ.setdefault(\"KERAS_BACKEND\", os.environ[\"BASE_ATTENTIVE_BACKEND\"])\n", "import keras # initialise Keras 3 backend before base_attentive\n", "BACKEND = os.environ[\"BASE_ATTENTIVE_BACKEND\"]\n", "print(f\"Backend: {BACKEND}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "2a422b90", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.166139Z", "iopub.status.busy": "2026-04-21T01:43:50.165133Z", "iopub.status.idle": "2026-04-21T01:43:50.208371Z", "shell.execute_reply": "2026-04-21T01:43:50.207470Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BaseAttentive version: 2.2.0\n" ] } ], "source": [ "# Import required libraries\n", "import numpy as np\n", "\n", "from base_attentive import BaseAttentive, __version__\n", "\n", "print(f\"BaseAttentive version: {__version__}\")" ] }, { "cell_type": "markdown", "id": "0086718d", "metadata": {}, "source": [ "## Step 2: Create a Basic Model\n", "\n", "Define input dimensions and create a model instance." ] }, { "cell_type": "code", "execution_count": 3, "id": "d1f83cd7", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.211768Z", "iopub.status.busy": "2026-04-21T01:43:50.210523Z", "iopub.status.idle": "2026-04-21T01:43:50.522008Z", "shell.execute_reply": "2026-04-21T01:43:50.520517Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'static_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'static_dim' instead.\n", " resolved = resolve_deprecated_kwargs(\n", "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'dynamic_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'dynamic_dim' instead.\n", " resolved = resolve_deprecated_kwargs(\n", "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'future_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'future_dim' instead.\n", " resolved = resolve_deprecated_kwargs(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Model created successfully!\n", "\n" ] } ], "source": [ "# Define input dimensions\n", "STATIC_DIM = (\n", " 4 # e.g., location, altitude, climate zone, soil type\n", ")\n", "DYNAMIC_DIM = 8 # e.g., temperature, humidity, pressure, wind speed, etc\n", "FUTURE_DIM = 6 # e.g., forecasted weather features\n", "OUTPUT_DIM = 2 # e.g., target 1 and target 2\n", "FORECAST_HORIZON = 24 # Predict 24 steps ahead\n", "\n", "# Create model\n", "model = BaseAttentive(\n", " static_input_dim=STATIC_DIM,\n", " dynamic_input_dim=DYNAMIC_DIM,\n", " future_input_dim=FUTURE_DIM,\n", " output_dim=OUTPUT_DIM,\n", " forecast_horizon=FORECAST_HORIZON,\n", " embed_dim=32,\n", " attention_units=64,\n", " num_heads=8,\n", " dropout_rate=0.15,\n", " name=\"BasicAttentiveModel\",\n", ")\n", "\n", "print(\"✅ Model created successfully!\")\n", "print(model)" ] }, { "cell_type": "markdown", "id": "2ade9c75", "metadata": {}, "source": [ "## Step 3: Inspect Model Configuration\n", "\n", "View the complete model configuration." ] }, { "cell_type": "code", "execution_count": 4, "id": "2128cee3", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.524024Z", "iopub.status.busy": "2026-04-21T01:43:50.524024Z", "iopub.status.idle": "2026-04-21T01:43:50.537760Z", "shell.execute_reply": "2026-04-21T01:43:50.536666Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "📋 Model Configuration:\n", "==================================================\n", " static_dim.................... 4\n", " dynamic_dim................... 8\n", " future_dim.................... 6\n", " output_dim.................... 2\n", " forecast_horizon.............. 24\n", " num_encoder_layers............ 2\n", " embed_dim..................... 32\n", " hidden_units.................. 64\n", " lstm_units.................... 64\n", " attention_units............... 64\n", " num_heads..................... 8\n", " dropout_rate.................. 0.15\n", " lookback_window............... 10\n", " memory_size................... 100\n", " multi_scale_agg............... last\n", " final_agg..................... last\n", " activation.................... relu\n", " use_residuals................. True\n", " use_vsn....................... True\n", " use_batch_norm................ False\n", " apply_dtw..................... True\n", " objective..................... hybrid\n", " architecture_config........... {}\n", " component_overrides........... {}\n", " verbose....................... 0\n", " name.......................... BasicAttentiveModel\n" ] } ], "source": [ "# Get full configuration\n", "config = model.get_config()\n", "\n", "print(\"\\n📋 Model Configuration:\")\n", "print(\"=\" * 50)\n", "for key, value in config.items():\n", " if value is not None:\n", " print(f\" {key:.<30} {value}\")" ] }, { "cell_type": "markdown", "id": "51044e7f", "metadata": {}, "source": [ "## Step 4: Create Example Data\n", "\n", "Generate synthetic data to demonstrate the model structure." ] }, { "cell_type": "code", "execution_count": 5, "id": "106a3cbd", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.540181Z", "iopub.status.busy": "2026-04-21T01:43:50.540181Z", "iopub.status.idle": "2026-04-21T01:43:50.553399Z", "shell.execute_reply": "2026-04-21T01:43:50.552063Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "📊 Sample Data Shapes:\n", " Static: (32, 4)\n", " Dynamic: (32, 10, 8)\n", " Future: (32, 24, 6)\n" ] } ], "source": [ "# Generate sample data\n", "BATCH_SIZE = 32\n", "TIME_STEPS = 10 # Historical lookback window\n", "\n", "# Static features: (batch_size, static_dim)\n", "static_features = np.random.randn(\n", " BATCH_SIZE, STATIC_DIM\n", ").astype(\"float32\")\n", "\n", "# Dynamic features: (batch_size, time_steps, dynamic_dim)\n", "dynamic_features = np.random.randn(\n", " BATCH_SIZE, TIME_STEPS, DYNAMIC_DIM\n", ").astype(\"float32\")\n", "\n", "# Future features: (batch_size, forecast_horizon, future_dim)\n", "future_features = np.random.randn(\n", " BATCH_SIZE, FORECAST_HORIZON, FUTURE_DIM\n", ").astype(\"float32\")\n", "\n", "print(\"\\n📊 Sample Data Shapes:\")\n", "print(f\" Static: {static_features.shape}\")\n", "print(f\" Dynamic: {dynamic_features.shape}\")\n", "print(f\" Future: {future_features.shape}\")" ] }, { "cell_type": "markdown", "id": "bae69eaa", "metadata": {}, "source": [ "## Step 5: Model Summary\n", "\n", "Display model hyperparameters and settings." ] }, { "cell_type": "code", "execution_count": 6, "id": "70bbaa7a", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.556600Z", "iopub.status.busy": "2026-04-21T01:43:50.555495Z", "iopub.status.idle": "2026-04-21T01:43:50.569125Z", "shell.execute_reply": "2026-04-21T01:43:50.568060Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "📈 Model Summary:\n", "==================================================\n", " Architecture............. hybrid\n", " Mode..................... None\n", " Encoder Layers........... 2\n", " Attention Heads.......... 8\n", " Dropout Rate............. 0.15\n", " Use Residuals............ True\n", " Use VSN.................. True\n", " Apply DTW................ True\n" ] } ], "source": [ "# Display model summary\n", "print(\"\\n📈 Model Summary:\")\n", "print(\"=\" * 50)\n", "\n", "summary_info = {\n", " \"Architecture\": model.objective,\n", " \"Mode\": model.mode,\n", " \"Encoder Layers\": model.num_encoder_layers,\n", " \"Attention Heads\": model.num_heads,\n", " \"Dropout Rate\": model.dropout_rate,\n", " \"Use Residuals\": model.use_residuals,\n", " \"Use VSN\": model.use_vsn,\n", " \"Apply DTW\": model.apply_dtw,\n", "}\n", "\n", "for key, value in summary_info.items():\n", " print(f\" {key:.<25} {value}\")" ] }, { "cell_type": "markdown", "id": "afdba401", "metadata": {}, "source": [ "## Step 6: Save/Load Model Configuration\n", "\n", "Demonstrate configuration serialization." ] }, { "cell_type": "code", "execution_count": 7, "id": "4209ab2f", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.571321Z", "iopub.status.busy": "2026-04-21T01:43:50.571321Z", "iopub.status.idle": "2026-04-21T01:43:50.757093Z", "shell.execute_reply": "2026-04-21T01:43:50.756091Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "💾 Saved Configuration (JSON snippet):\n", "{\n", " \"static_dim\": 4,\n", " \"dynamic_dim\": 8,\n", " \"future_dim\": 6,\n", " \"output_dim\": 2,\n", " \"forecast_horizon\": 24,\n", " \"mode\": null,\n", " \"num_encoder_layers\": 2,\n", " \"quantiles\": null,\n", " \"embed_dim\": 32,\n", " \"hidden_units\": 64,\n", " \"lstm_units\": 64,\n", " \"attention_units\": 64,\n", " \"num_heads\": 8,\n", " \"dropout_rate\": 0.15,\n", " \"lookback_window\": 10,\n", " \"memory_size\": 100,\n", " \"scales\": null,\n", " \"multi_scale_agg\": \"last\",\n", " \"final_agg\": \"last\",\n", " \"activation\": \"relu\",\n", " \"use_residuals\": true,\n", " \"use_vsn\": true,\n", " \"vsn_units\": nul...\n", "\n", "✅ Model recreated from configuration\n" ] } ], "source": [ "# Save configuration\n", "import json\n", "\n", "config = model.get_config()\n", "\n", "# Convert to JSON-serializable format\n", "json_config = json.dumps(\n", " {\n", " k: str(v)\n", " if not isinstance(v, (int, float, bool, type(None)))\n", " else v\n", " for k, v in config.items()\n", " },\n", " indent=2,\n", ")\n", "\n", "print(\"\\n💾 Saved Configuration (JSON snippet):\")\n", "print(json_config[:500] + \"...\")\n", "\n", "# Later, recreate model from config\n", "model2 = BaseAttentive.from_config(config)\n", "print(\"\\n✅ Model recreated from configuration\")" ] }, { "cell_type": "markdown", "id": "0b1eda14", "metadata": {}, "source": [ "## Step 5: Train the Model\n", "\n", "Compile and fit the model on our synthetic data for a quick demonstration." ] }, { "cell_type": "code", "execution_count": 8, "id": "98b856f6", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:43:50.760092Z", "iopub.status.busy": "2026-04-21T01:43:50.759092Z", "iopub.status.idle": "2026-04-21T01:44:25.652686Z", "shell.execute_reply": "2026-04-21T01:44:25.651601Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 2s/step - loss: 0.8540 - mae: 0.7169 - val_loss: 0.1792 - val_mae: 0.3470\n", "Epoch 2/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.2138 - mae: 0.3689 - val_loss: 0.1308 - val_mae: 0.2854\n", "Epoch 3/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1521 - mae: 0.3033 - val_loss: 0.1196 - val_mae: 0.2704\n", "Epoch 4/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1419 - mae: 0.2939 - val_loss: 0.1062 - val_mae: 0.2599\n", "Epoch 5/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1326 - mae: 0.2875 - val_loss: 0.1019 - val_mae: 0.2569\n", "Epoch 6/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1301 - mae: 0.2881 - val_loss: 0.0971 - val_mae: 0.2482\n", "Epoch 7/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1264 - mae: 0.2810 - val_loss: 0.0958 - val_mae: 0.2448\n", "Epoch 8/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1229 - mae: 0.2737 - val_loss: 0.0897 - val_mae: 0.2386\n", "Epoch 9/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1183 - mae: 0.2692 - val_loss: 0.0868 - val_mae: 0.2389\n", "Epoch 10/10\n", "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1143 - mae: 0.2657 - val_loss: 0.0834 - val_mae: 0.2334\n", "\n", "Final train MSE: 0.1143\n", "Final val MSE: 0.0834\n" ] } ], "source": [ "import keras\n", "\n", "# Use a small, quick-to-train setup\n", "BATCH_SIZE = 32\n", "TIME_STEPS = 10\n", "\n", "# Regenerate data with consistent seed\n", "np.random.seed(42)\n", "static_data = np.random.randn(BATCH_SIZE, STATIC_DIM).astype('float32')\n", "dynamic_data = np.random.randn(BATCH_SIZE, TIME_STEPS, DYNAMIC_DIM).astype('float32')\n", "future_data = np.random.randn(BATCH_SIZE, FORECAST_HORIZON, FUTURE_DIM).astype('float32')\n", "\n", "# Synthetic target: sine wave per sample + noise\n", "t = np.linspace(0, 2 * np.pi, FORECAST_HORIZON)\n", "target = (np.sin(t)[None, :, None] * np.random.rand(BATCH_SIZE, 1, OUTPUT_DIM)\n", " + 0.1 * np.random.randn(BATCH_SIZE, FORECAST_HORIZON, OUTPUT_DIM)).astype('float32')\n", "\n", "_ = model([static_data, dynamic_data, future_data]) # build weights\n", "model.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse', metrics=['mae'])\n", "\n", "history = model.fit(\n", " [static_data, dynamic_data, future_data],\n", " target,\n", " epochs=10,\n", " batch_size=16,\n", " validation_split=0.2,\n", " verbose=1,\n", ")\n", "\n", "print()\n", "print(f'Final train MSE: {history.history[\"loss\"][-1]:.4f}')\n", "print(f'Final val MSE: {history.history[\"val_loss\"][-1]:.4f}')" ] }, { "cell_type": "markdown", "id": "9c082004", "metadata": {}, "source": [ "## Step 6: Visualize Training History\n", "\n", "Plot the training and validation loss curves." ] }, { "cell_type": "code", "execution_count": 9, "id": "7102bc5f", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:44:25.654984Z", "iopub.status.busy": "2026-04-21T01:44:25.654984Z", "iopub.status.idle": "2026-04-21T01:44:25.840655Z", "shell.execute_reply": "2026-04-21T01:44:25.838490Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots(figsize=(8, 4))\n", "ax.plot(history.history['loss'], label='Train MSE', linewidth=2)\n", "ax.plot(history.history['val_loss'], label='Val MSE', linewidth=2, linestyle='--')\n", "ax.set_title('Training History — BaseAttentive Quick Start', fontsize=13)\n", "ax.set_xlabel('Epoch')\n", "ax.set_ylabel('MSE Loss')\n", "ax.legend()\n", "ax.grid(True, alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "fb861b1e", "metadata": {}, "source": [ "## Step 7: Forecast vs Actual\n", "\n", "Compare model predictions against the synthetic targets for a single sample." ] }, { "cell_type": "code", "execution_count": 10, "id": "8e92bf4c", "metadata": { "execution": { "iopub.execute_input": "2026-04-21T01:44:25.843076Z", "iopub.status.busy": "2026-04-21T01:44:25.843076Z", "iopub.status.idle": "2026-04-21T01:44:27.447563Z", "shell.execute_reply": "2026-04-21T01:44:27.446440Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Sample MAE: 0.1528\n" ] } ], "source": [ "# Predict on the full batch\n", "preds = model.predict([static_data, dynamic_data, future_data], verbose=0)\n", "# preds shape: (batch, forecast_horizon, output_dim)\n", "\n", "sample_idx = 0\n", "fig, axes = plt.subplots(1, OUTPUT_DIM, figsize=(6 * OUTPUT_DIM, 4), squeeze=False)\n", "\n", "hours = np.arange(1, FORECAST_HORIZON + 1)\n", "for d in range(OUTPUT_DIM):\n", " ax = axes[0][d]\n", " ax.plot(hours, target[sample_idx, :, d], label='Actual', linewidth=2, color='steelblue')\n", " ax.plot(hours, preds[sample_idx, :, d], label='Forecast', linewidth=2, color='tomato', linestyle='--')\n", " ax.fill_between(hours,\n", " preds[sample_idx, :, d] - 0.1,\n", " preds[sample_idx, :, d] + 0.1,\n", " alpha=0.2, color='tomato', label='±0.1 band')\n", " ax.set_title(f'Output dim {d + 1}: Forecast vs Actual', fontsize=12)\n", " ax.set_xlabel('Forecast step (h)')\n", " ax.set_ylabel('Value')\n", " ax.legend()\n", " ax.grid(True, alpha=0.3)\n", "\n", "plt.suptitle('BaseAttentive — Quick Start Forecast', fontsize=13, y=1.02)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "mae = float(np.mean(np.abs(preds[sample_idx] - target[sample_idx])))\n", "print(f'Sample MAE: {mae:.4f}')" ] }, { "cell_type": "markdown", "id": "e70f4e8f", "metadata": {}, "source": [ "## What's Next?\n", "\n", "Check out the other example notebooks:\n", "\n", "1. **02_hybrid_vs_transformer.ipynb** - Compare hybrid and transformer encoder choices\n", "2. **03_attention_stack_configuration.ipynb** - Configure the decoder attention stack\n", "3. **04_standalone_applications.ipynb** - End-to-end forecasting examples across domains\n", "4. **05_kernel_robust_networks.ipynb** - Use BaseAttentive as a reusable forecasting kernel\n", "5. **06_crps_probabilistic_forecasting.ipynb** - Quantile, Gaussian, and mixture CRPS workflows\n", "6. **07_v2_spec_registry.ipynb** - Declarative V2 specs, registries, and resolver assembly\n", "\n", "## 📚 Resources\n", "\n", "- [Full Documentation](https://github.com/earthai-tech/base-attentive)\n", "- [GitHub Issues](https://github.com/earthai-tech/base-attentive/issues)\n" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.20" } }, "nbformat": 4, "nbformat_minor": 5 }