keras-to-tensorflow | A tutorial on running Keras models in Tensorflow | Machine Learning library

by bitbionic Python Version: Current License: MIT

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kandi X-RAY | keras-to-tensorflow Summary

keras-to-tensorflow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-to-tensorflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However keras-to-tensorflow build file is not available. You can download it from GitHub.

So you’ve built an awesome machine learning model in Keras and now you want to run it natively thru Tensorflow. This tutorial will show you how.

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keras-to-tensorflow has a low active ecosystem.

It has 113 star(s) with 67 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 3 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-to-tensorflow is current.

Quality

keras-to-tensorflow has no bugs reported.

Security

keras-to-tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

keras-to-tensorflow is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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keras-to-tensorflow releases are not available. You will need to build from source code and install.

keras-to-tensorflow has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed keras-to-tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into keras-to-tensorflow implemented functionality, and help decide if they suit your requirements.

Train a model .
Build the classifier .
Convert a model to a TF graph .
Reads a Tensor from a file .
Invert key - value pairs .
Load a tensorflow graph from a file .
Loads a label file .

Get all kandi verified functions for this library.

keras-to-tensorflow Key Features

No Key Features are available at this moment for keras-to-tensorflow.

keras-to-tensorflow Examples and Code Snippets

No Code Snippets are available at this moment for keras-to-tensorflow.

Community Discussions

Trending Discussions on keras-to-tensorflow

tensorflow c++ batch inference

Fine tuned keras .h5 model to .pb model used in c++ gives running model failed error

TensorFlow C++ on Windows: Executor failed to create kernel

Linker issue in g++

QUESTION

tensorflow c++ batch inference

Asked 2019-Jun-29 at 15:44

I have a problem with making inference on a batchsize greater than 1 using the c++ tensorflow api. The network input planes are 8x8x13 and the output is a single float. When I try to infer on multiple samples as follows, the result is correct only for the first sample. I used keras2tensorflow tool for converting the graph to .pb format.

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ANSWER

Answered 2019-Jun-29 at 15:44

The problem turned out to be due to a bug the keras_to_tensorflow I used for conversion. I reported the issue here. The bug is still there in keras_to_tensorflow

On line 68:

Source https://stackoverflow.com/questions/50240083

QUESTION

Fine tuned keras .h5 model to .pb model used in c++ gives running model failed error

Asked 2018-Jul-24 at 20:41

I've fine tuned VGG16 model and saved the model with best accuracy. My keras code is -

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ANSWER

Answered 2018-Jul-24 at 20:41

The problem was probably due to resizing the image size to 125*125 before feeding it to VGG16 net. It requires 224*224 image size else the image size may reduce to zero while training which is what was probably happening. Keeping image size as 224*224 removed all the errors and allowed the model to be trained on a bigger batch size.

Source https://stackoverflow.com/questions/51490254

QUESTION

TensorFlow C++ on Windows: Executor failed to create kernel

Asked 2018-Jun-20 at 10:13

I've built TensorFlow C++ for Windows using CMake and I'm able to run a few examples. But, when trying to perform a prediction on my own model, I get the following error message:

Executor failed to create kernel. Invalid argument: NodeDef mentions attr 'dilations' not in Op

and

Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.

I've trained my model using Keras and tf 1.5. The model uses adam as optimizer and is a simple ResNet50-based model. I converted the Keras model to TensorFlow using this script and it runs OK on TensorFlow for Python.

Any ideas?

Here's the full log:

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ANSWER

Answered 2018-Mar-14 at 13:43

The solution was updating TensorFlow to a newer version. The version I was used did not supported some operations implemented in Keras.

Source https://stackoverflow.com/questions/48947488

QUESTION

Linker issue in g++

Asked 2018-Mar-27 at 10:29

I have the following .sh file (from here).

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ANSWER

Answered 2018-Mar-27 at 10:29

The linker is saying that the linkage requires shared library libtensorflow_framework.so (presumably because -ltensorflow_cc depends on it and requests it) but is not given in your commandline. This should be solved by adding -ltensorflow_framework at the end, with an additional -L option if necessary.

Source https://stackoverflow.com/questions/49498825

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install keras-to-tensorflow

You can download it from GitHub.
You can use keras-to-tensorflow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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