model_optimization.py

import cv2
import tensorflow as tf
import tensorflow_model_optimization as tfmo

# Load and optimize a deep learning model using model pruning
model = tf.saved_model.load("model")
pruned_model = tfmo.sparsity.keras.prune_low_magnitude(model, pruning_schedule=tfmo.sparsity.keras.PolynomialDecay(initial_sparsity=0.50, final_sparsity=0.90, begin_step=1000, end_step=2000))

# Classify an image using the pruned model
def classify_image(image, patch_size=None):
    # Prepare the input tensor
    input_data = tf.constant(image, dtype=tf.float32)

    # Split the input image into patches, if patch_size is specified
    if patch_size is not None:
        patches = cv2.split(image)
    else:
        patches = [image]

    # Classify each patch individually
    results = []
    for patch in patches:
        patch_data = tf.constant(patch, dtype=tf.float32)
        patch_result = pruned_model(patch_data, training=False).numpy()
        results.append(patch_result.tolist()[0])

    # Merge the classification results for each patch into a single result
    result = sum(results) / len(results)

    # Return the final classification result
    return result