tensorflow 机器学习和python中的SSL证书过期错误

cmssoen2  于 2022-12-23  发布在  Python
关注(0)|答案(1)|浏览(301)

我在机器学习项目的python版本3.10.8和Windows 10中遇到错误。

错误为：

Traceback (most recent call last):

  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1348, in do_open
    h.request(req.get_method(), req.selector, req.data, headers,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1282, in request   
    self._send_request(method, url, body, headers, encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1328, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1277, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1037, in _send_output
    self.send(msg)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 975, in send
    self.connect()
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1454, in connect
    self.sock = self._context.wrap_socket(self.sock,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 513, in wrap_socket
    return self.sslsocket_class._create(
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 1071, in _create
    self.do_handshake()
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 1342, in do_handshake
    self._sslobj.do_handshake()
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:997)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "c:\Users\ycvytfbv\OneDrive - Xerox\Desktop\ml training\train.py", line 30, in <module>
    myproject = MLforKidsImageProject(key)
  File "c:\Users\ycvytfbv\OneDrive - Xerox\Desktop\ml training\mlforkids.py", line 37, in __init__
    with urllib.request.urlopen(apiurl) as url:
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 216, in urlopen
    return opener.open(url, data, timeout)
    response = self._open(req, data)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 536, in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 496, in _call_chain
    result = func(*args)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1391, in https_open
    return self.do_open(http.client.HTTPSConnection, req,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1351, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:997)>
PS C:\Users\ycvytfbv\OneDrive - Xerox\Desktop\ml training> python .\train.py
MLFORKIDS: Downloading information about your machine learning project
Traceback (most recent call last):
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1348, in do_open
    h.request(req.get_method(), req.selector, req.data, headers,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1282, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1328, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1277, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1037, in _send_output
    self.send(msg)
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 975, in send
    self.connect()
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\http\client.py", line 1454, in connect
    self.sock = self._context.wrap_socket(self.sock,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 513, in wrap_socket
    return self.sslsocket_class._create(
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 1071, in _create
    self.do_handshake()
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\ssl.py", line 1342, in do_handshake
    self._sslobj.do_handshake()
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:997)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\ycvytfbv\OneDrive - Xerox\Desktop\ml training\train.py", line 30, in <module>
    myproject = MLforKidsImageProject(key)
  File "C:\Users\ycvytfbv\OneDrive - Xerox\Desktop\ml training\mlforkids.py", line 37, in __init__
    with urllib.request.urlopen(apiurl) as url:
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 216, in urlopen
    return opener.open(url, data, timeout)
    result = self._call_chain(self.handle_open, protocol, protocol +
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 496, in _call_chainl_chain
    result = func(*args)                                                                                                                          ps_open
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1391, in https_open                                                                                                                                           open
    return self.do_open(http.client.HTTPSConnection, req,
  File "C:\Program Files\WindowsApps\PythonSoftwareFoundation.Python.3.10_3.10.2288.0_x64__qbz5n2kfra8p0\lib\urllib\request.py", line 1351, in do_open
    raise URLError(err)
urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:997)>

the code in train.py is:

# treat this key like a password and keep it secret!
key = "the key will not be revealed"

# this will train your model and might take a little while
myproject = MLforKidsImageProject(key)
myproject.train_model()

# CHANGE THIS to the image file you want to recognize
demo = myproject.prediction("mytest.JPG")

label = demo["class_name"]
confidence = demo["confidence"]

# CHANGE THIS to do something different with the result
print ("result: '%s' with %d%% confidence" % (label, confidence))

The code for mlforkids.py is:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
tf.get_logger().setLevel('ERROR')

import tensorflow_hub as hub
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dropout, Dense
from tensorflow.keras.layers.experimental.preprocessing import Rescaling

import numpy as np
import urllib.request, urllib.error, json
from time import sleep

#
# Helper class for training an image classifier using training data
#  from the Machine Learning for Kids website.
#
class MLforKidsImageProject:

    IMAGESIZE=(224,224)
    INPUTLAYERSIZE=IMAGESIZE + (3,)

    # scratchkey is the secret API key that allows access to training
    #  data from a single project on the MLforKids website
    def __init__(self, scratchkey: str):
        # register custom HTTP handler
        opener = urllib.request.build_opener(MLforKidsHTTP())
        urllib.request.install_opener(opener)

        print("MLFORKIDS: Downloading information about your machine learning project")
        self.scratchkey = scratchkey
        try:
            apiurl = self.__switchToTemporarySite("https://machinelearningforkids.co.uk/api/scratch/" + scratchkey + "/train")
            with urllib.request.urlopen(apiurl) as url:
                self.__downloaded_training_images_list = json.loads(url.read().decode())
        except urllib.error.HTTPError:
            raise RuntimeError("Unable to retrieve machine learning project - please check that the key is correct")

    # Generates a name for the local cache file where the downloaded training
    #  image is saved. An image file extension is required, otherwise it will
    #  be ignored by ImageDataGenerator.
    def __get_fname(self, trainingitem):
        extension = ".png" if trainingitem["imageurl"].lower().endswith(".png") else ".jpg"
        return trainingitem["id"] + extension

    # Downloads all of the training images for this project, and sets up an
    #  ImageDataGenerator against the folder where they have been downloaded
    def __get_training_images_generator(self):
        print("MLFORKIDS: Getting your training images to use to train your machine learning model")
        cachedir = "~/.keras/"
        cachelocation = os.path.join("datasets", "mlforkids", self.scratchkey)
        projectcachedir = str(os.path.expanduser(os.path.join(cachedir, cachelocation)))
        for trainingitem in self.__downloaded_training_images_list:
            try:
                tf.keras.utils.get_file(origin=self.__switchToTemporarySite(trainingitem["imageurl"]),
                                        cache_dir=cachedir,
                                        cache_subdir=os.path.join(cachelocation, trainingitem["label"]),
                                        fname=self.__get_fname(trainingitem))
                # avoid common rate-limiting errors by pausing
                #  for a quarter-second between each download
                sleep(0.25)
            except Exception as downloaderr:
                print("ERROR: Unable to download training image from", trainingitem["imageurl"])
                print(downloaderr)
                print("ERROR: Skipping training image and continuing without it", trainingitem["imageurl"])
        return ImageDataGenerator().flow_from_directory(str(projectcachedir),
                                                        target_size=MLforKidsImageProject.IMAGESIZE)

    # Creates a lookup table for the classes that this project is being trained
    #  to recognize.
    # TODO : dumb implementation - should rewrite
    def __get_class_lookup(self, training_image_data):
        class_labels = [None]*training_image_data.num_classes
        class_names = training_image_data.class_indices.keys()
        for classname in class_names:
            class_labels[training_image_data.class_indices[classname]] = classname
        return class_labels

    # Defines a simple image classifier based on a mobilenet model from TensorFlow hub
    def __define_model(self):
        print("MLFORKIDS: Defining the layers to include in your neural network")
        model = Sequential([
            # input layer is resizing all images to save having to do that in a manual pre-processing step
            Rescaling(1/127, input_shape=MLforKidsImageProject.INPUTLAYERSIZE),
            # using an existing pre-trained model as an untrainable main layer
            hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/5"),
            #
            Dropout(rate=0.2),
            #
            Dense(self.num_classes)
        ])
        model.build((None,) + MLforKidsImageProject.INPUTLAYERSIZE)

        # model compile parameters copied from tutorial at https://www.tensorflow.org/hub/tutorials/tf2_image_retraining
        model.compile(
            optimizer=tf.keras.optimizers.SGD(learning_rate=0.005, momentum=0.9),
            loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1),
            metrics=['accuracy'])

        return model

    # Runs the model fit function to train the tl model
    def __train_model(self, trainingimagesdata):
        print("MLFORKIDS: Starting the training of your machine learning model")
        if trainingimagesdata.batch_size > trainingimagesdata.samples:
            trainingimagesdata.batch_size = trainingimagesdata.samples
        steps_per_epoch = trainingimagesdata.samples // trainingimagesdata.batch_size
        epochs = 8
        if trainingimagesdata.samples > 55:
            epochs = 15
        self.ml_model.fit(trainingimagesdata, epochs=epochs, steps_per_epoch=steps_per_epoch, verbose=0)
        print("MLFORKIDS: Model training complete")

    # Cloudflare is currently blocking access to the Machine Learning for Kids API
    #  from non-browser user agents
    # While I raise this with them to get this unblocked, switching to this
    #  temporary URL should avoid the problem
    #
    # TODO: remove this function as soon as Cloudflare have
    #  stopped breaking Python apps
    #
    def __switchToTemporarySite(self, url):
        return url.replace("https://machinelearningforkids.co.uk/api/scratch/",
                           "https://mlforkids-api.j8clybxvjr0.us-south.codeengine.appdomain.cloud/api/scratch/")

    #
    # public methods
    #

    # Fetches the training data for this project, and uses it to train a machine learning model
    def train_model(self):
        training_images = self.__get_training_images_generator()
        self.num_classes = training_images.num_classes
        self.ml_class_names = self.__get_class_lookup(training_images)
        self.ml_model = self.__define_model()
        self.__train_model(training_images)

    # Returns a prediction for the image at the specified location
    def prediction(self, image_location: str):
        if hasattr(self, "ml_model") == False:
            raise RuntimeError("Machine learning model has not been trained for this project")
        testimg = image.load_img(image_location, target_size=MLforKidsImageProject.IMAGESIZE)
        testimg = image.img_to_array(testimg)
        testimg = np.expand_dims(testimg, axis=0)
        predictions = self.ml_model.predict(testimg)
        topprediction = predictions[0]
        topanswer = np.argmax(topprediction)
        return {
            "class_name": self.ml_class_names[topanswer],
            "confidence": 100 * np.max(tf.nn.softmax(topprediction))
        }

#
# Helper class for making HTTP requests to fetch training images
#  for machine learning projects
#
# It adds a user-agent header so that when scraping images from
#  third-party websites, the Python code correctly identifies
#  itself, so that appropriate rate-limiting can be applied.
#
class MLforKidsHTTP(urllib.request.HTTPHandler):
    def http_request(self, req):
        req.headers["User-Agent"] = "MachineLearningForKidsPythonBot/1.0"
        return super().http_request(req)

我亦已下载以下要求：

Pillow==9.2.0
scipy==1.9.3
tensorflow==2.10.0
tensorflow-hub==0.12.0

请帮助我在上面的问题。我也有图像文件。

tensorflow

1条答案

按热度按时间
tkclm6bt

tkclm6bt1#

请尝试将下面的脚本放在代码的开头，以避免SSL认证错误。

import ssl

try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    # Legacy Python that doesn't verify HTTPS certificates by default
    pass
else:
    # Handle target environment that doesn't support HTTPS verification
    ssl._create_default_https_context = _create_unverified_https_context
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