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Neural networks can be implemented on a Raspberry Pi using frameworks like TensorFlow Lite or PyTorch, allowing for efficient AI processing on edge devices. The Raspberry Pi is capable of running deep learning models for tasks such as image recognition, speech processing, and predictive analytics.

Below is a Python script for implementing a simple neural network on a Raspberry Pi using TensorFlow Lite. This script performs image classification using a pretrained MobileNet model and a connected Pi Camera for real-time inference.

Installation Requirements:

Before running the script, install the necessary libraries:

pip install tensorflow lite runtime opencv-python numpy picamera

Python Code for Raspberry Pi Neural Network Implementation:

import cv2
import numpy as np
import tensorflow.lite as tflite
import picamera
import picamera.array
import time

# Load TensorFlow Lite model
model_path = "mobilenet_v1_1.0_224.tflite"
interpreter = tflite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

# Get model input details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']

# Initialize PiCamera
camera = picamera.PiCamera()
camera.resolution = (224, 224)
camera.framerate = 30
time.sleep(2)  # Allow camera to warm up

# Function to preprocess image
def preprocess_image(frame):
    frame = cv2.resize(frame, (input_shape[1], input_shape[2]))  # Resize to model input
    frame = np.expand_dims(frame, axis=0)  # Add batch dimension
    frame = frame.astype(np.float32) / 255.0  # Normalize
    return frame

# Function for inference
def predict(frame):
    processed = preprocess_image(frame)
    interpreter.set_tensor(input_details[0]['index'], processed)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    predicted_class = np.argmax(output_data)
    return predicted_class

# Real-time inference loop
try:
    with picamera.array.PiRGBArray(camera, size=(224, 224)) as stream:
        while True:
            camera.capture(stream, format="bgr")
            frame = stream.array
            stream.truncate(0)  # Reset buffer
            
            prediction = predict(frame)
            print(f"Predicted Class: {prediction}")

            # Display output
            cv2.putText(frame, f"Prediction: {prediction}", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
            cv2.imshow("Neural Network Output", frame)

            if cv2.waitKey(1) & 0xFF == ord('q'):  # Exit on 'q'
                break
finally:
    camera.close()
    cv2.destroyAllWindows()

How It Works:

• Loads TensorFlow Lite model for efficient inference.

• Captures real-time images from the Pi Camera.

• Processes images and normalizes pixel values.

• Runs inference and displays predicted class.

• Press 'q' to exit the program.