Argumen kata kunci tak terduga 'kasar' di Keras

Mencoba menjalankan model keras terlatih dengan kode python berikut:

from keras.preprocessing.image import img_to_array
from keras.models import load_model

from imutils.video import VideoStream
from threading import Thread
import numpy as np
import imutils
import time
import cv2
import os

MODEL_PATH = "/home/pi/Documents/converted_keras/keras_model.h5"

print("[info] loading model..")
model = load_model(MODEL_PATH)


print("[info] starting vid stream..")
vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)

while True:
    frame = vs.Read()
    frame = imutils.resize(frame, width=400)

    image = cv2.resize(frame, (28, 28))
    image = image.astype("float") / 255.0
    image = img_to_array(image)
    image = np.expand_dims(image, axis=0)
    (fuel, redBall, whiteBall, none) = model.predict(image)[0]
    label = "none"
    proba = none

    if fuel > none and fuel > redBall and fuel > whiteBall:
        label = "Fuel"
        proba = fuel
    elif redBall > none and redBall > fuel and redBall > whiteBall:
        label = "Red Ball"
        proba = redBall
    elif whiteBall > none and whiteBall > redBall and whiteBall > fuel:
        label = "white ball"
        proba = whiteBall
    else:
        label = "none"
        proba = none

    label = "{}:{:.2f%}".format(label, proba * 100)
    frame = cv2.putText(frame, label, (10, 25),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    if key == ord("q"):
        break

print("[info] cleaning up..")
cv2.destroyAllWindows()
vs.stop()

Ketika saya menjalankannya dengan python3, saya mendapatkan kesalahan berikut: TypeError: __init__() got an unexpected keyword argument 'ragged'

Apa yang menyebabkan kesalahan, dan bagaimana cara mengatasinya?

Versi: Keras v2.3.1 tensorflow v1.13.1

Edit untuk ditambahkan:

Traceback (most recent call last):
  File "/home/pi/Documents/converted_keras/keras-script.py", line 18, in <module>
    model = load_model(MODEL_PATH)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 492, in load_wrapper
    return load_function(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 584, in load_model
    model = _deserialize_model(h5dict, custom_objects, compile)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 274, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 627, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 301, in from_config
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 301, in from_config
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/network.py", line 1056, in from_config
    process_layer(layer_data)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/network.py", line 1042, in process_layer
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 149, in deserialize_keras_object
    return cls.from_config(config['config'])
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1179, in from_config
    return cls(**config)
  File "/usr/local/lib/python3.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'ragged'

tautan file h5 (google drive)

Jadi saya mencoba tautan di atas yang Anda sebutkan mesin yang bisa diajar.
Ternyata model yang Anda ekspor berasaltensorflow.keras dan tidak langsung dari kerasAPI. Keduanya berbeda. Jadi saat memuat mungkin menggunakan tensor tf.ragged yang mungkin tidak kompatibel dengan API keras.

Soulution untuk masalah Anda:

Jangan mengimpor keras secara langsung karena model Anda disimpan dengan api tingkat tinggi keras Tensorflow. Ubah semua impor Anda ke tensorflow.keras

Ubah:

from keras.preprocessing.image import img_to_array
from keras.models import load_model

untuk ini:

from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model

Itu akan menyelesaikan masalah Anda.

EDIT:
Semua impor Anda, harus dari Kerasatau tensorflow.keras. Meskipun API yang sama beberapa hal berbeda yang menciptakan masalah semacam ini. Juga untuk tensorflowbackend tf.keraslebih disukai, karena Keras 2.3.0 adalah rilis utama terakhir yang akan mendukung backend selain tensorflow.

Rilis ini membawa API sinkron dengan tf.keras API pada TensorFlow 2.0. Namun perlu dicatat bahwa itu tidak mendukung sebagian besar fitur TensorFlow 2.0, khususnya eksekusi yang bersemangat. Jika Anda membutuhkan fitur-fitur ini, gunakan tf.keras . Ini juga merupakan rilis utama terakhir dari multi-backend Keras. Ke depan, kami menyarankan agar pengguna mempertimbangkan untuk mengganti kode Keras mereka ke tf.keras di TensorFlow 2.0.