tensorFlowTest | Currently just a test repo

by Createdd Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tensorFlowTest Summary

tensorFlowTest is a Python library. tensorFlowTest has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Currently just a test repo. See article on install:

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tensorFlowTest has a low active ecosystem.

It has 7 star(s) with 8 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. On average issues are closed in 66 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorFlowTest is current.

Quality

tensorFlowTest has 0 bugs and 0 code smells.

Security

tensorFlowTest has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorFlowTest code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorFlowTest does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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tensorFlowTest releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

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

Deepnn layer
Max pooling op
Bias tensor
Creates a weight variable
2D convolutional layer
Generate summaries for a variable
Create a bias variable
2d convolutional layer
Decode a list of integers

Get all kandi verified functions for this library.

tensorFlowTest Key Features

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tensorFlowTest Examples and Code Snippets

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Community Discussions

Trending Discussions on tensorFlowTest

I have built the classification model using tensorflow estimator after saving the model , when converting it into tensorflow lite it shows an error

Using a TensorFlow 2.1.0 model built in Python in Java TensorFlow 1.15 | No Operation named [input] in the Graph

QUESTION

I have built the classification model using tensorflow estimator after saving the model , when converting it into tensorflow lite it shows an error

Asked 2021-Apr-19 at 02:27

import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model("/content/drive/MyDrive/tensorflowtest/1618754788") #path to the SavedModel directenter code hereory
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
 tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]

...

ANSWER

Answered 2021-Apr-19 at 02:27

The TF select option in the TFLite product does not allow tf.AsString op yet. For such cases, you can report the feature request at here.

The above op isn't included the TF select's allowed list, which can be fixed by adding the relevant code like this commit. It would be great if you can create a such PR.

The fix is submitted and the AsString op will be available through the TF select option since the tomorrow's TensorFlow nightly version.

Source https://stackoverflow.com/questions/67149749

QUESTION

Using a TensorFlow 2.1.0 model built in Python in Java TensorFlow 1.15 | No Operation named [input] in the Graph

Asked 2020-May-09 at 07:23

I have a model written in Python 3.7 using TensorFlow 2.1.0. and I am trying to use it in an Java application (using TensorFlow 1.4), however, the model is not accepting input. I would guess that this is a compatibility issue, but the model successfully loads in Java. I've tried to use keras.Sequential and keras.Model, but it doesn't seem to make a difference. I've seen tf.placeholder being used in TF v1, but understand the v2 replacement is tf.keras.Input.

Python:

...

ANSWER

Answered 2020-May-07 at 20:03

The best option is to retrieve those names dynamically from the model signatures and feed them to your model for inference.

To see in Java what is the list of inputs/outputs of your saved model, you can retrieve the MetaGraphDef from the SavedModelBundle, as explained here: Tensorflow 2.0 & Java API. (you can also double-check using the [saved_model_cli][1] command line utility).

But be aware that there is a bug with TF2.x when it comes to functional models, where TF proceed to some undocumented name mangling when it encodes the inputs/outputs signatures, as described here.

In addition, you might want to take a look at the next version of TF Java, which supports natively TF2.x versions but are only available as snapshots at the moment.

Source https://stackoverflow.com/questions/61650866

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

Vulnerabilities

No vulnerabilities reported

Install tensorFlowTest

You can download it from GitHub.
You can use tensorFlowTest 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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