class Monitor(keras.callbacks.Callback):
    def __init__(self, target):
        self.lock = threading.Lock()
        self.target = target
        self.stable = clone_my_model(target)
    def on_train_batch_end(self, batch, logs=None):
        self.lock.acquire()
        self.stable.set_weights(self.target.get_weights())
        self.lock.release()
    def snapshot(self):
        self.lock.acquire()
        result = self.stable
        self.lock.release()
        return result

def run(monitor):

def run(m):

        outputs = monitor.snapshot()(inputs)

        outputs = m(inputs)

def train(monitor, filename):

def train(m, filename):

    monitor.target.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000, callbacks=monitor)

    m.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)

    outputs = tf.reshape(monitor.target(inputs), (1024,2048, 1))

    outputs = tf.reshape(m(inputs), (1024,2048, 1))

    monitor = model.Monitor(m)
    bg_thread = threading.Thread(target=train, args=(monitor, sys.argv[1]), kwargs={})

    bg_thread = threading.Thread(target=train, args=(m, sys.argv[1]), kwargs={})

    drawmap.run(monitor)

    drawmap.run(m)