I tried to run a project (ipynb extension) from GitHub using Google colab.
I have managed to run the program, but when compared with the author’s output, mine is a little different.
For example, train_df.describe() does not print some of the columns (‘target' column in particular because that is used to plot a graph.
Why is it that I run the same program but get different result?
Related
I use IntelliJ premium, I updated the whole app, and I saw a very annoying new output style of it's Jupyter notebook. Then I reinstalled an older version, I see the jupyter output is still in new format. I guess it is because of Jupyter's package update.
How can I have the old-style table format?
the new style shows only 10 rows and for every run, you should change 10 to a higher number which becomes annoying after a couple of minutes. It has gotten really slow too.
Here everyone, I am fairly new google cloud console. I am trying to customize a google document ai model that will learn to extract different sections of document to various data. As you can on the image that it fails to train the model and I have been running the Labeling Task for several days now I have not seen progress. Can you please assist in telling what is the right way to customize google document ia modelenter image description here
I have tried to manually label the different sections of the document, it took me a while so I did around 20 test and training dataset which I think the model to not train then I decided to do the Labeling Task as an alternative to manually labeling the dataset.
Here is the information about Labeling Tasks for Document AI
https://cloud.google.com/document-ai/docs/workbench/label-documents#labeling-tasks
Labeling Tasks use Human-in-the-Loop to have human labelers label documents for training data or production review. You can either set up your own labeling team or apply for access to the Google-Managed Workforce.
However, it doesn't seem like this is the correct course of action for what you are trying to do, since the labeling has already been completed.
Could you provide more clarification on what you are trying to accomplish with the Custom Document Extractor?
Note: Some of the error messages that are output from Document AI Workbench are not very descriptive (e.g. Internal Error Occurred) but the product development team is working to surface more helpful errors when possible.
I am curios if there are examples on how to run TF-Slim models/slim using deployment/model_deploy.py across multiple GPU’s on multiple machines. The documentation is pretty good but I am missing a couple of pieces. Specifically what needs to be put in for worker_device and ps_device and what additionally needs to be run on each machine?
An example like the one at the bottom of the distributed page would be awesome.
https://www.tensorflow.org/how_tos/distributed/
I have few questions about TensorFlow. I'm following the "TensorFlow for Poets" tutorial (https://petewarden.com/2016/02/28/tensorflow-for-poets/), and i got the expected result.
However i would like to know two thing:
1. How to classify more than one image at a time?
2. How to extract the result in .txt format?
Thank you
I had the same issue, so I built the TensorPy GitHub repo to easily handle image classifications of either individual or multiple images directly from web pages. How it works: For multiple images, first it scrapes all the image links directly from a web page given. Then it downloads those images to a temporary folder and converts those images to JPEG format. Finally, it uses TensorFlow to classify all those images and print out the result, which you can then easily output to a txt file by adding " > output.txt" to the end of your command line run statement. See the video tutorial link in the repo, and swap out the individual image file from the example for a web page. Since you probably want your own customization, feel free to look at how the code works so that you can create your own version as you need it.
After creating my solution, I saw that there are also other good solutions available online. Check out Siraj's image classification tutorial, which has a link to the associated GitHub repo in the video description.
UPDATE:
If you're just looking to run TensorFlow's classify_image.py on multiple image files in a folder, you can easily create a bash script for that:
for i in temp_image_folder/*.jpg; do
python classify_image.py --image=$i
done
I am currently using the "find" command.
find ./ -type f -iname "*.jpg" -exec python3 classify_image.py --image={} \;
But I'm also looking for a solution that does not have to load the complete script for every image.
I have downloaded several HDF files from the MODIS database.
According to the documentation, the layers have to be multiplied by 0.1 to obtain the real values.
I get an error when I put the name of the HDF-layer in the Raster Calculator, however it does work when I export it as a new raster before. But after multiplication with 0.1, I still do not get a continuous scale image but only black and white areas. I excluded the seven highest values as indicated in the documentation, but still no change.
Another way of getting the MODIS files is to use the respective toolbox. Data imported with this tool does show up correctly, but I cannot import most of it even though it is available under the link indicated above:
Failed to execute (CreateCustomGeoTransformation)
Failed to execute (ImportEvapotranspiration)
Has anyone experienced something similar?