Machine Learning with TensorFlow 1.x

Exporting the model for production

 def export_model(checkpoint_dir, export_dir, export_name,   export_version): 
    graph = tf.Graph() 
    with graph.as_default(): 
        image = tf.placeholder(tf.float32, shape=[None, None, 3]) 
        processed_image = datasets.preprocessing(image,          is_training=False) 
        with tf.variable_scope("models"): 
         logits = nets.inference(images=processed_image,            is_training=False) 
 
        model_checkpoint_path =          get_model_path_from_ckpt(checkpoint_dir) 
        saver = tf.train.Saver() 
 
        config = tf.ConfigProto() 
        config.gpu_options.allow_growth = True 
        config.gpu_options.per_process_gpu_memory_fraction = 0.7 
 
        with tf.Session(graph=graph) as sess: 
            saver.restore(sess, model_checkpoint_path) 
            export_path = os.path.join(export_dir, export_name,   str(export_version)) ...