Instructions to use sharktide/FV-FloodNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sharktide/FV-FloodNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://sharktide/FV-FloodNet") - Notebooks
- Google Colab
- Kaggle
| import tensorflow as tf | |
| from tensorflow.keras import layers, models, callbacks | |
| from tensorflow.keras.saving import register_keras_serializable | |
| import numpy as np | |
| def rainfall_proximity_penalty(inputs): | |
| rainfall = inputs[:, 0] | |
| distance = inputs[:, 4] | |
| proximity_score = tf.sigmoid((150 - distance) * 0.04) | |
| rainfall_score = tf.sigmoid((rainfall - 90) * 0.3) | |
| return (rainfall_score * proximity_score)[:, None] | |
| def flood_risk_booster(inputs): | |
| slope = inputs[:, 3] | |
| rainfall = inputs[:, 0] | |
| slope_boost = tf.sigmoid((slope - 2.0) * 1.5) | |
| rain_boost = tf.sigmoid((rainfall - 60) * 0.25) | |
| return (1.0 + 0.25 * slope_boost * rain_boost)[:, None] | |
| def flood_suppression_mask(inputs): | |
| elevation = inputs[:, 2] | |
| rainfall = inputs[:, 0] | |
| flatness = tf.sigmoid((elevation - 9.0) * 0.6) | |
| dryness = tf.sigmoid((20.0 - rainfall) * 0.2) | |
| return (1.0 - 0.3 * flatness * dryness)[:, None] | |
| CUSTOM_OBJECTS = { | |
| "rainfall_proximity_penalty": rainfall_proximity_penalty, | |
| "flood_risk_booster": flood_risk_booster, | |
| "flood_suppression_mask": flood_suppression_mask | |
| } | |