AIF.py

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.")