在windows 10的机器学习项目中导入Python 3.11中的tensorflow时出现错误，尽管我已经通过pip导入了tensorflow。代码是:
from mlforkids import MLforKidsImageProject
# 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))
mlforkids.py的代码为:
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)
我还安装了以下要求:
numpy==1.23.4
Pillow==9.2.0
scipy==1.9.3
tensorflow==2.10.0
tensorflow-hub==0.12.0
根据官方Tensorflow文档中的测试构建配置，Tensorflow 2.10与Python 3.7 - 3.10版本兼容。请通过降级Python版本再试一次。您可以在这里找到构建配置。谢谢你！