Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: tabular-classification
+library_name: keras
+---
+
+This model requires the following custom objects:
+
+```python
+import tensorflow as tf
+from tensorflow.keras.saving import register_keras_serializable
+from tensorflow.keras import layers, models, backend as K
+import numpy as np
+
+@register_keras_serializable()
+def cold_temp_penalty(inputs):
+    temp = inputs[:, 0]
+    penalty = tf.where(
+        temp > 295.0,
+        1.0,
+        tf.where(
+            temp < 290.0,
+            0.0,
+            (temp - 290.0) / 5.0
+        )
+    )
+    return penalty[:, None]
+
+@register_keras_serializable()
+def fire_risk_booster(inputs):
+    temp = inputs[:, 0]
+    humidity = inputs[:, 1]
+    wind = inputs[:, 2]
+    veg = inputs[:, 3]
+
+    # Boost ranges
+    temp_boost = tf.sigmoid((temp - 305.0) * 1.2)
+    humidity_boost = tf.sigmoid((20.0 - humidity) * 0.5)
+    wind_boost = tf.sigmoid((wind - 15.0) * 0.8)
+    veg_boost = tf.sigmoid((veg - 70.0) * 0.5)
+
+    # Combine and scale
+    combined = temp_boost * humidity_boost * wind_boost * veg_boost
+    boost = 1.0 + 0.3 * combined  # Up to 30% increase in fire score
+    return boost[:, None]
+
+@register_keras_serializable()
+def fire_suppression_mask(inputs):
+    temp = inputs[:, 0]
+    humidity = inputs[:, 1]
+    wind = inputs[:, 2]
+
+    # Suppress if warm but humid and still
+    temp_flag = tf.sigmoid((temp - 293.0) * 1.2)
+    humid_flag = tf.sigmoid((humidity - 50.0) * 0.4)
+    wind_flag = 1 - tf.sigmoid((wind - 5.0) * 0.8)
+
+    suppression = temp_flag * humid_flag * wind_flag
+    penalty = 1.0 - 0.3 * suppression  # Max 30% suppression
+    return penalty[:, None]
+
+CUSTOM_OBJECTS = {
+    "cold_temp_penalty": cold_temp_penalty,
+    "fire_risk_booster": fire_risk_booster,
+    "fire_suppression_mask": fire_suppression_mask
+}
+```