Artificial intelligence

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import numpy as np

# Seed for reproducibility
np.random.seed(42)
tf.random.set_seed(42)

# Simulated sensory input data (e.g., visual, auditory)
input_data = np.random.random((1000, 10))  # 1000 samples, 10 features each

# Simulated memory function (LSTM)
memory_model = Sequential([
    LSTM(50, input_shape=(10, 1), return_sequences=False),
    Dense(20, activation='relu')
])
memory_model.compile(optimizer='adam', loss='mse')

# Reshape input data for LSTM
input_data_reshaped = input_data.reshape((1000, 10, 1))

# Training memory model to simulate memory encoding
memory_model.fit(input_data_reshaped, np.random.random((1000, 20)), epochs=10)

# Simulated decision-making function (Dense Network)
decision_model = Sequential([
    Dense(64, input_dim=20, activation='relu'),
    Dense(32, activation='relu'),
    Dense(2, activation='softmax')  # Binary decision output
])
decision_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Generate pseudo-memory outputs to simulate past experiences
memory_outputs = memory_model.predict(input_data_reshaped)

# Simulated decision labels (random binary outcomes)
decision_labels = np.random.randint(2, size=(1000, 1))
decision_labels = tf.keras.utils.to_categorical(decision_labels, 2)

# Training decision-making model
decision_model.fit(memory_outputs, decision_labels, epochs=10)

# Simulated new sensory input for testing
new_input = np.random.random((1, 10)).reshape((1, 10, 1))

# Simulated memory recall
memory_output = memory_model.predict(new_input)

# Simulated decision based on recalled memory
decision_output = decision_model.predict(memory_output)

print("Simulated decision output:", decision_output)