Untitled

import tensorflow as tf
import numpy as np

def predict_and_calculate_bce_losses(model, images, labels):
    # Check the available GPUs
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if not gpus:
        raise SystemError("No GPUs found for parallel processing")

    # Split images and labels for each GPU
    num_gpus = len(gpus)
    image_splits = np.array_split(images, num_gpus)
    label_splits = np.array_split(labels, num_gpus)

    # Initialize BCE loss function
    bce_loss_function = tf.keras.losses.BinaryCrossentropy()

    # List to store all BCE losses
    all_bce_losses = []

    # Predict and calculate BCE loss for each GPU's data
    for i in range(num_gpus):
        with tf.device(f'/GPU:{i}'):
            # Predict on this split
            predictions = model.predict(image_splits[i])

            # Calculate BCE loss for each prediction
            losses = bce_loss_function(label_splits[i], predictions)

            # Store the losses
            all_bce_losses.extend(losses.numpy())

    return np.array(all_bce_losses)

# Usage of the function
# model: Your pre-trained model
# images: Numpy array of your images
# labels: Numpy array of your labels
# bce_losses = predict_and_calculate_bce_losses(model, images, labels)