import tensorflow as tf



def main(argv=None):
    keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
    image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
    annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation")
    z = tf.placeholder(tf.float32, shape=[None, 4, 4, 128], name="z")

    # pred_annotation, logits = inference(image, keep_probability,z)
 #   tf.summary.image("input_image", image, max_outputs=2)
 #   tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
 #   tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
