read_tfrecord.py

def read_tfrecord(serialized_example):
    
    # Create the feature description dictionary
    feature_description = {
        'image': tf.io.FixedLenFeature((), tf.string, ""),
        'label': tf.io.FixedLenFeature((), tf.int64, -1),
    }
    # Parse the serialized_example and decode the image
    example = tf.io.parse_single_example(serialized_example, feature_description)
    image = tf.io.decode_jpeg(example['image'], channels=3)
    
    image = tf.cast(image, tf.float32)
    
    # Normalize the pixels in the image
    image = image/255.
    
    # Resize the image to (224, 224) using tf.image.resize
    image = tf.image.resize(image, [224, 224])
    
    return image, example['label']