Uncaught (in promise) Error: Tensorflow Op is not supported: _FusedConv2D

<!-- Load TensorFlow.js. This is required to use MobileNet. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.17.0"> </script>
<!-- Load the MobileNet model. -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@2.1.1"> </script>

<!-- Replace this with your image. Make sure CORS settings allow reading the image! -->
<img id="img" src="cat.jpg.webp"></img>

<!-- Place your code in the script tag below. You can also use an external .js file -->
<script>
  // Notice there is no 'import' statement. 'mobilenet' and 'tf' is
  // available on the index-page because of the script tag above.

  const img = document.getElementById('img');

  // Load the model.
  mobilenet.load().then(model => {
    // Classify the image.
    model.classify(img).then(predictions => {
      console.log('Predictions: ');
      console.log(predictions);
    });
  });
</script>

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import PIL.Image
tf.get_logger().setLevel("ERROR")
def preprocess_image(image_path):
    image = PIL.Image.open(image_path).convert("RGB").resize((150,150))
    image = np.array(image) / 255
    image = np.expand_dims(image, 0)

    return image

def test_model(imgurl):
    image_path = imgurl
    model_file_name='human_or.h5'
    labels = ["human","dogs"]
    model = tf.keras.models.load_model(model_file_name, custom_objects={"KerasLayer": hub.KerasLayer})
    predictions = model.predict(preprocess_image(image_path))
    print("検証 %s 人の顔である確率 %3d%%" %(image_path,int(predictions[0][0]*100)) )

test_model("ai_image_test\\test1.jpg.webp")
test_model("ai_image_test\\test2.jpg.webp")
test_model("ai_image_test\\test3.jpg.webp")
test_model("ai_image_test\\test4.jpg.webp")
test_model("ai_image_test\\test5.jpg.webp")
test_model("ai_image_test\\test6.jpg.webp")

zip358com
zip358

# Generated by Selenium IDE
import time
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager

from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support import expected_conditions
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
from selenium.webdriver.support.ui import Select

class twss():
	def setup_method(self):
		self.driver = webdriver.Chrome(ChromeDriverManager().install())
		self.vars = {}

	def teardown_method(self):
		self.driver.quit()

	def screenshots(self):
		self.driver.get("https://twitter.com/")
		self.driver.set_window_size(945, 900)
		with open('twname.dat','r',encoding="utf-8") as f:
			for line in f:
				FILENAME = "X:\\var\\www\\html\\labo_ai\\twss\\image\\screen_" + line.replace('\n', '') +".png.webp"
				self.driver.get("https://twitter.com/" + line.replace('\n', ''))
				time.sleep(2)
				self.driver.save_screenshot(FILENAME)
		f.close()
		self.driver.quit()
twss = twss()
twss.setup_method()
twss.screenshots()

#!/usr/local/bin/python3
# coding:utf-8
import os
import sys
import MeCab
import gensim
import markovify
import unicodedata
model = gensim.models.KeyedVectors.load_word2vec_format('/var/www/html/model.vec', binary=False)
f = open('merosu.txt')
tagger = MeCab.Tagger("-Owakati")
tagger.parse('')
text0 = tagger.parse(f.read())
text1 = text0
text0 = text0.replace('\n','')
text0 = text0.replace('\r','')
text1x = text0.split(" ")
text2 = []
try:
    for item in text1x:
        if item.strip():
            results  = model.most_similar(positive=[item],topn=2)
            #"print(results)
            for val1 in results:
                text2.append(val1[0] + "\n")
#
    # print (text1)
    # print (" ".join(text2))
    model_a = markovify.Text(text1 + "\n")
    print(str(model_a.make_sentence()).replace(' ',''))
    model_b = markovify.Text(" ".join(text2))
    print(str(model_b.make_sentence()).replace(' ',''))
    model_combo = markovify.combine([model_a, model_b], [1, 1])
    print(str(model_combo.make_sentence()).replace(' ',''))
except Exception as e:
    print("動作エラー", e.args)
    pass