training-data

问题 I'm trying to call a java function from a Matlab script, I tried all the solutions put in the website but I didn't get an issue. My class is simple: package testMatlabInterface; public class TestFunction { private double value; public TestFunction() { value=0; } public double Add(double v) { value += v; return value; } public static void main(String args[]) { } } So I put .java file (also .class ) in my workingspace C:\scriptsMatlab and I added this path to javaclasspath of Matlab, but when I

问题 I need some help to create a CaffeDB for siamese CNN out of a plain directory with images and label-text-file. Best would be a python-way to do it. The problem is not to walk through the directory and making pairs of images. My problem is more of making a CaffeDB out of those pairs. So far I only used convert_imageset to create a CaffeDB out of an image directory. Thanks for help! 回答1: Why don't you simply make two datasets using good old convert_imagest ? layer { name: "data_a" top: "data_a"

问题 I trained data from 500 devices to predict their performance. Then I applied my trained model to a test data set for another 500 devices and show pretty good prediction results. Now my executives want me to prove this model will work well on one million devices not only on 500. Obviously we don't have data for one million devices. And if the model is not reliable, they want me to discover the required amount of train data in order to make a reliable prediction on one million devices. How

问题 I need to train tesseract to recognize just ten words. Words are pharma name, thing like: Atrasil, Spectful Since fonts used are pretty common, I tried to unpack eng.traineddata, substitute freq-dawg and word-dawg with just those words. Then I've repack them into a new traineddata, unfortunatly it doesn't seem to work very well. Matching results are still unacceptable and I can't use them, even when I use images obtained from a simple word file. Is there a way to achieve a good matching? do I

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 5 months ago . I am using the tess-two library for OCR recognition on Android . I want to create the training data on Android . I have followed this link and successfully created training data on linux system . How to do the same on Android using tess-two or any other library ? 回答1: The tess-two library for Android uses the

问题 I'm trying to do training with batches in tensorflow. This works a little since I can do the first epoch in batches. I currently have 2 problems with my code. 1. After the first epoch has finished the second epoch immediatly goes to the except tf.errors.OutOfRangeError and the next epoch doesn't restart the batch from the top. How can I do another epoch where it gives batches again? 2. I print the batchnr and I notice that the last batch of the epoch prints print(batchnr) but doesn't print

问题 I am using caffe net with python. I have train.txt file like this: train/1175-c/b0a1.bmp b0a1 train/1175-c/b0a2.bmp b0a2 train/1175-c/b0a3.bmp b0a3 train/1175-c/b0a4.bmp b0a4 train/1175-c/b0a5.bmp b0a5 train/1175-c/b0a6.bmp b0a6 train/1175-c/b0a7.bmp b0a7 train/1175-c/b0a8.bmp b0a8 train/1175-c/b0a9.bmp b0a9 train/1175-c/b0aa.bmp b0aa my questions: Can I use hex instead of int at the end of each line? About the label, does it need to start from 0 Or Should I change the above to: train/1175-c

问题 I am using Tensorflow Object Detection API to train my object detection model. I accumulated the dataset, and am going through the this tutorial. Everything went fine until I tried to train my dataset. When I run the following line on terminal, python train.py --logtostderr \ --train_dir=training/ \ --pipeline_config_path=training/ssd_mobilenet_v1_coco.config I get the following error Traceback (most recent call last): File "legacy/train.py", line 49, in <module> from object_detection

问题 I was running a random forest classification model and initially divided the data into train (80%) and test (20%). However, the prediction had too many False Positive which I think was because there was too much noise in training data, so I decided to split the data in a different method and here's how I did it. Since I thought the high False Positive was due to the noise in the train data, I made the train data to have the equal number of target variables. For example, if I have data of 10

问题 I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. Currently, the train set has about 2 years data. Is there a way to train on another 2 years and (kind of) append it to the existing saved model. rf = RandomForestRegressor(n_estimators=100) print ("Trying to fit the Random Forest model --> ") if os.path.exists('rf.pkl'): print ("Trained model already pickled -- >") with open('rf.pkl',