Hello everybody.
Good Morning!
Please, does anyone know what this error means:
In fact, in my research here on the forum and also on the internet, I could see that it is a version mismatch error.
Does anyone know how I can fix this problem?
Hi, Luiz.
This error is on the Face Recognition assignment, but we already talked about a different error in that assignment and solved it just a couple of hours ago. So I assume you already figured out how to get around this âbad marshal dataâ problem. If not, please âcopy/pasteâ the complete exception trace. From the image you show, we canât see your user code: the trace only shows the stuff from deep in the bowels of TensorFlow.
Hi my dear @paulinpaloalto. My sincere apologies for the delay in returning. I got into a whirlwind of work flow and activities that took a lot out of me.
Thank you for your attention and for coming back. This error I couldnât solve, itâs an error presented in my personal notebook, and not in the coursera notebook. I believe it is a Tensor Flow version incompatibility error.
I managed to validate and get the grade needed to pass the activity, because as I mentioned, this error only occurs on my Pycharm. At the end of the course I return to these activities (âproblem childâ) and eliminate the errors one by one.
Ah, I did not realize that this error happened when running the notebook locally. Yes, version compatibility can be an issue for TF as well as other packages that are used by the assignments. Also note that you need to download all the associated files with the notebook. The mentors are working on putting together some instructions for how to do that, so that should be available by the time you circle back to this issue.
Thank you for the informations above!
Could you solve the issue? I have the same problem running the assignment on a local machine after downloading all required files.
Iâve already upgraded all packages but still get the same message.
Python: 3.9.5
TF: 2.7.0
Well, maybe itâs not a great idea to update to the latest versions of everything. All of the notebooks I checked on the website are running TF version 2.3.0. There are still no instructions for how to run locally, so you are on your own here. Sorry!
I donât know if it would solve this specific issue, but Iâm currently using Google Colab to perform the TensorFlow Developer Specialization (Certification).
I donât remember if I tried to use it in the deep learning specialization.
I hope I could have helped.
Great @paulinpaloalto,
Greetings my noble and esteemed mentor.
How have you been? Iâm back for another specialization. I believe Iâm on the right path.
A fraternal hug my dear fellow.
I wanted to share how I fixed the marshall data issue (and anything else to do with compatibility problems for this course) - at least on Mac.
Note that I already had two versions of Python on my Mac - which is common as Macâs need a 2.7.x version to work. To test your setup, run âpython -Vâ and âpython3 -Vâ in the Terminal. The first one on my machine returns 2.7.18; second returns 3.9.10 - if you donât have this dual Python setup the below may not work quite the same for you.
What versions of Python or TensorFlow do we need. First we need to know what versions of Python and TensorFlow we actually need. To check, run the following in the Jupyter code so you know what to aim for.
import tensorflow as tf
print("Tensorflow version")
print(tf.version.VERSION)
import sys
print("Python version")
print (sys.version)
For the issue in this post, we need Python 3.7.6 and TensorFlow 2.3.0.
Step 1: getting the right Python version. Install pyenv - a virtual Python environment that allows you to install multiple versions of Python and switch between them. I did this with Homebrew. To install Homebrew, run the following in Macâs Terminal (cmd-space, then type Terminal):
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
To install pyenv, run:
brew install pyenv
To install the relevant version of python, run:
pyenv install 3.7.6
Issue 1: this didnât work! First irritant: this failed for me - I got an error during installation (final bit of error was âret = sendfile(in, out, offset, &sbytes, &sf, flags);â. Apparently this is something to do with Xcode. This GitHub page contained a magical bit of code which fixed this for me - to install successfully I adapted the following bit of code - which originally had --patch 3.8.0 but worked with me just changing to --patch 3.7.6:
CFLAGS="-I$(brew --prefix openssl)/include -I$(brew --prefix bzip2)/include -I$(brew --prefix readline)/include -I$(xcrun --show-sdk-path)/usr/include" LDFLAGS="-L$(brew --prefix openssl)/lib -L$(brew --prefix readline)/lib -L$(brew --prefix zlib)/lib -L$(brew --prefix bzip2)/lib" \
pyenv install --patch 3.7.6 < <(curl -sSL https://github.com/python/cpython/commit/8ea6353.patch\?full_index\=1)
This successfully installed the right Python version.
Step 2: setting this as the main Python version. To set as the global version run the following:
pyenv global 3.7.6
Issue 2: setting global pyenv didnât work. To check whether the above has worked, run âpython3 -Vâ and you should hopefully get the number 3.7.6 as opposed to whatever your versions was beforehand (it also changes the Python call itself - i.e. âpython -Vâ now points to 3.7.6 but I try and stay away from the systemâs Python calls so will continue to call python3).
To fix this, I found a second GitHub page with magical code on it (the pyenv page): run the following to temporarily set your python3 to point at this new pyenv-installed version of Python rather than your usual python3 version:
export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH" # if `pyenv` is not already on PATH
eval "$(pyenv init --path)"
eval "$(pyenv init -)"
Check this works with âpython3 -Vâ and you should now get 3.7.6.
Step 3: getting the right TensorFlow. When you switch to a pyenv-installed new version of Python you need to re-install all your packages. Python package installer comes with distributions of Python, so you can simply call âpip3 install package-nameâ to get packages you want. Youâll need to install any other ones you need for the assignment (like pandas in this case).
Make sure pip3 is upgraded otherwise you likely wonât find the right TensorFlow version - running:
pip3 install --upgrade pip
To install the correct TensorFlow version:
pip3 install tensorflow==2.3.0
Note the order here matters: TensorFlow 2.3.0 isnât compatible with Python 3.8 and on - so you have to downgrade to Python 3.7.6 first, get your system to point at the right install and then install the downgraded TensorFlow.
Step 4: testing your code. Finally test your code by running the following from within your assignment 4 directory. Note you will need all the files from that folder to work locally. This post by @paulinpaloalto details how to do that. If your terminal doesnât have a command prompt see here as well.
python3 file_name.py
Warning 1: in my case at least, the Python version defaults back to itâs system normals (i.e. 2.7.18 for python calls and 3.9.10 for python3 calls) when I close the terminal. You have to set the pyenv global and run the four lines of code above to get back to working with 3.7.6 (or whatever version you need).
Warning 2: I could likely fix this but havenât - I generally work in Sublime Text - but that looks at python 3.9.10 regardless of what Iâm doing. So for the purposes of the assignments where the version of Python or Tensorflow matters - I just work out of the Terminal.
Hi,
The â2.3.0â version of tensorflow is not available anymore with the pip install.
Another solution would be to build again the json model with your tensorflow version with the python script available of the facenet model.
But at the end I have a layer mistmatch when loading the wheights.
.
I have the same problem and cannot install tensorflowâs version 2.3.0!
Could anybody solve the issue?
Notice that what is happening there is you are loading a model and coefficients for FaceNet from files that were provided with the course, so they match the TF version of the course. So the problem is probably that the Keras APIs for loading pretrained models have changed in a way that is not backwards compatible. If you are not able to configure your environment (E.g. using pip or conda as described on this thread), then the other alternative is to find a pretrained FaceNet model from some other source. It looks like Keras does not provide FaceNet, but you can google âfacenet pretrained modelâ and youâll find lot of hits.
I could not find something that I can use instead of the model. If anybody have found something, I would be more than grateful if he/she shares it with us.
I ran into this problem as well, trying to run the model_from_json on the keras-facenet-h5/model.json file on python 3.8 and tensorflow 2.3.0. So seems like this is not a tensorflow issue. Seems like internally tensorflow/keras uses the internal marshal method to serialize parts of the model, like lambda functions (got the hint from this discussion), so even though itâs JSON, when you have a Lambda layer, its serialization contains also a special field function that contains base64 encoded binary serialization in an internal python version specific format - which apparently isnât compatible even between python 3.7 and 3.8
I managed to solve this simply by installing the additional python3.7 interpreter (This was a helpful guide for my Fedora 40 env)