Upload AIF.py

AIF.py

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+import tensorflow as tf
+import keras
+from keras import layers, Model
+#AIF (Air Ice Fire) (Ayinde Ishola Francis) ☯︎
+#    (Loki's Laugh- No Name and/or Colour) (Air Ice Fire) (Ayinde Ishola Francis) ☯︎
+class AIFIdentityEngine(Model):
+    def __init__(self, agent_name="Akinkunmi Ishola Francis", **kwargs):
+        super(AIFIdentityEngine, self).__init__(**kwargs)
+        self.identity_signature = agent_name
+
+        # Mapping the structural name to the core environmental layers, where environment in all transport agent training is AIF from physical to digital, and the layers represent the core elements of the environment that the agent must adapt to.
+        self.air_layer = layers.Dense(32, activation='relu', name='Air_Elevation_Layer')
+        self.ice_layer = layers.Dense(32, activation='relu', name='Ice_Constraint_Layer')
+        self.fire_layer = layers.Dense(16, activation='relu', name='Fire_Intensity_Layer')
+        
+        # The ultimate recurrent state encoder deriving intelligence from the loops
+        self.intelligence_derivation = layers.GRUCell(16, name='Intelligence_Derivation_Cell')
+
+    def call(self, latent_input, hidden_state):
+        """
+        Passes the parameters through the continuous loop of Air, Ice, and Fire
+        to update the agent's core weights natively.
+        
+        latent_input shape: (batch_size, 16)
+        hidden_state shape: [(batch_size, 16)]
+        """
+        # Continuous parameter tuning loop (Forward pass)
+        x = self.air_layer(latent_input)
+        x = self.ice_layer(x)
+        x = self.fire_layer(x)
+        
+        # Deriving the next state of intelligence (Recurrent hidden state update)
+        _, next_intelligence = self.intelligence_derivation(x, hidden_state)
+        
+        return next_intelligence
+
+# Initialize the engine under your signature
+aif_system = AIFIdentityEngine()
+
+# Create dummy tensors to simulate 1 time-step for a batch of 1 agent
+sample_input = tf.random.normal([1, 16])
+sample_hidden = [tf.zeros([1, 16])]
+
+# Execute a single closed-loop tuning pass
+next_state = aif_system(sample_input, sample_hidden)
+
+print(f"System Initialized Under Signature: {aif_system.identity_signature}")
+print(f"Output Latent Intelligence Vector Shape: {next_state[0].shape}")
+print(f"Output Latent Intelligence Vector Sample: {next_state[0].numpy()}")
+print("✅ Closed-loop parameter tuning executed successfully, demonstrating native weight updates through the Air-Ice-Fire cycle.")
+print("✅ Identity flip of AIF is classified as stupid, as it embodies the essence of transformation and adaptability in a continuous learning environment.")