python-3.x

问题 Here is a snippet of code: def display(): threading.Timer(1,display).start() print("Number") display() For this code I want to ask the following things: Every second new thread spawns, is that right? Every second the last thread dies because the function executes completely so the older thread dies, is that right? If not then what is happening? 回答1: Timer derives from Thread , so yes: many threads are started. Threads die when their invoked functions return (or throw) whether or not you call

问题 I would like to save the best model in Keras based on auc and I have this code: def MyMetric(yTrue, yPred): auc = tf.metrics.auc(yTrue, yPred) return auc best_model = [ModelCheckpoint(filepath='best_model.h5', monitor='MyMetric', save_best_only=True)] train_history = model.fit([train_x], [train_y], batch_size=batch_size, epochs=epochs, validation_split=0.05, callbacks=best_model, verbose = 2) SO my model runs nut I get this warning: RuntimeWarning: Can save best model only with MyMetric

问题 I have a project that I am working on, and the requirements are to login to a website using a username and password. I have to do it in python, and then be able to access a part of the site only accessible to people who are logged in. I have tried a few variations of coding to do this, and haven't been able to successfully log in yet. Here is my coding: the function to login to it: def session2(url): #r = requests.get(url) #ckies = [] #print("here are the cookies for twitter:\n") #for cky in

问题 I would like to save the best model in Keras based on auc and I have this code: def MyMetric(yTrue, yPred): auc = tf.metrics.auc(yTrue, yPred) return auc best_model = [ModelCheckpoint(filepath='best_model.h5', monitor='MyMetric', save_best_only=True)] train_history = model.fit([train_x], [train_y], batch_size=batch_size, epochs=epochs, validation_split=0.05, callbacks=best_model, verbose = 2) SO my model runs nut I get this warning: RuntimeWarning: Can save best model only with MyMetric

问题 I am trying to optimize the code below (PySpark UDF). It gives me the desired result (based on my data set) but it's too slow on very large datasets (approx. 180M). The results (accuracy) are better than available Python modules (e.g. geotext, hdx-python-country). So I'm not looking for another module. DataFrame: df = spark.createDataFrame([ ["3030 Whispering Pines Circle, Prosper Texas, US","John"], ["Kalverstraat Amsterdam","Mary"], ["Kalverstraat Amsterdam, Netherlands","Lex"] ]).toDF(

问题 I am training a machine learning model on google's colab. I am using keras with tensorflow backend (python 3.6). I am saving my model using the model.save() function provided by keras. When I call model.save('model_name )` where is the file saved? I cannot find it in my drive anywhere. 回答1: First, mount your drive: !mkdir -p drive !google-drive-ocamlfuse drive Now, append your path: import sys sys.path.append('drive/Project') You should be able to see your files on your local and web-based

问题 I am training a machine learning model on google's colab. I am using keras with tensorflow backend (python 3.6). I am saving my model using the model.save() function provided by keras. When I call model.save('model_name )` where is the file saved? I cannot find it in my drive anywhere. 回答1: First, mount your drive: !mkdir -p drive !google-drive-ocamlfuse drive Now, append your path: import sys sys.path.append('drive/Project') You should be able to see your files on your local and web-based

问题 I am training a machine learning model on google's colab. I am using keras with tensorflow backend (python 3.6). I am saving my model using the model.save() function provided by keras. When I call model.save('model_name )` where is the file saved? I cannot find it in my drive anywhere. 回答1: First, mount your drive: !mkdir -p drive !google-drive-ocamlfuse drive Now, append your path: import sys sys.path.append('drive/Project') You should be able to see your files on your local and web-based

问题 I have a very large folder of images, as well as a CSV file containing the class labels for each of those images. Because it's all in one giant folder, I'd like to split them up into training/test/validation sets; maybe create three new folders and move images into each based on a Python script of some kind. I'd like to do stratified sampling so I can keep the % of classes the same across all three sets. What would be the approach to go about making a script that can do this? 回答1: I ran into

问题 I have a very large folder of images, as well as a CSV file containing the class labels for each of those images. Because it's all in one giant folder, I'd like to split them up into training/test/validation sets; maybe create three new folders and move images into each based on a Python script of some kind. I'd like to do stratified sampling so I can keep the % of classes the same across all three sets. What would be the approach to go about making a script that can do this? 回答1: I ran into