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import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM import numpy as np class AIHelper: def __init__(self): self._initialize_memory_model() self._initialize_decision_model() def _initialize_memory_model(self): self.memory_model = Sequential([ LSTM(50, input_shape=(10, 1), return_sequences=False), Dense(20, activation='relu') ]) self.memory_model.compile(optimizer='adam', loss='mse') def _initialize_decision_model(self): self.decision_model = Sequential([ Dense(64, input_dim=20, activation='relu'), Dense(32, activation='relu'), Dense(2, activation='softmax') ]) self.decision_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) def train_memory_model(self, input_data, target_data, epochs=10): input_data_reshaped = input_data.reshape((input_data.shape[0], input_data.shape[1], 1)) self.memory_model.fit(input_data_reshaped, target_data, epochs=epochs) def train_decision_model(self, input_data, target_data, epochs=10): self.decision_model.fit(input_data, target_data, epochs=epochs) def remember(self, input_data): input_data_reshaped = input_data.reshape((input_data.shape[0], input_data.shape[1], 1)) return self.memory_model.predict(input_data_reshaped) def decide(self, memory_output): return self.decision_model.predict(memory_output) def assist(self, new_input): memory_output = self.remember(new_input) decision_output = self.decide(memory_output) return decision_output # Example usage if __name__ == "__main__": np.random.seed(42) tf.random.set_seed(42) ai_helper = AIHelper() # Simulated sensory input data input_data = np.random.random((1000, 10)) target_memory_data = np.random.random((1000, 20)) # Train memory model ai_helper.train_memory_model(input_data, target_memory_data, epochs=10) # Simulated decision labels decision_labels = np.random.randint(2, size=(1000, 1)) decision_labels = tf.keras.utils.to_categorical(decision_labels, 2) # Generate pseudo-memory outputs memory_outputs = ai_helper.remember(input_data) # Train decision-making model ai_helper.train_decision_model(memory_outputs, decision_labels, epochs=10) # Simulated new sensory input for testing new_input = np.random.random((1, 10)) # AI helper assisting with new input decision_output = ai_helper.assist(new_input) print("AI Helper decision output:", decision_output)
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