create_tfrecords | simpler way | Analytics library

by kwotsin Python Version: Current License: MIT

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kandi X-RAY | create_tfrecords Summary

create_tfrecords is a Python library typically used in Analytics, Tensorflow, Numpy applications. create_tfrecords has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However create_tfrecords build file is not available. You can download it from GitHub.

A simpler way of preparing large-scale image dataset by generalizing functions from TensorFlow-slim.

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create_tfrecords has a low active ecosystem.

It has 129 star(s) with 51 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 10 have been closed. On average issues are closed in 191 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of create_tfrecords is current.

Quality

create_tfrecords has 0 bugs and 6 code smells.

Security

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

create_tfrecords code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

create_tfrecords is licensed under the MIT License. This license is Permissive.

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

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

create_tfrecords saves you 53 person hours of effort in developing the same functionality from scratch.

It has 140 lines of code, 14 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Convert a dataset
Creates a tf train Feature
Create a tf train Feature
Convert image data into a tf example
Decode a JPEG image
Returns the filename for a dataset
Read image dimensions
Check if the dataset exists

Get all kandi verified functions for this library.

create_tfrecords Key Features

No Key Features are available at this moment for create_tfrecords.

create_tfrecords Examples and Code Snippets

No Code Snippets are available at this moment for create_tfrecords.

Community Discussions

Trending Discussions on create_tfrecords

Cannot Train Keras Pre-trained Model in Tensorflow Estimator

QUESTION

Cannot Train Keras Pre-trained Model in Tensorflow Estimator

Asked 2018-Aug-14 at 07:18

While implementing a Tensorflow keras VGG16 pre-trained model with custom data using the Estimator class, it is throwing the error "ValueError: Cannot find input with name "image" in Keras Model. It needs to match one of the following: input_30".

In this code I haven't reshaped the input tensor into (-1, 224,224,3), instead, the shape is (224,224,3). I tried both shapes in the parser function - the function to be mapped to in the dataset API section.

Can anybody point out where is the mistake in the code. Feel free to change the code if there are any unnecessary mistakes.

This is done in Colab, so I am giving a link to it to check the error, in case you want to check it.

...

ANSWER

Answered 2018-Aug-14 at 07:18

You are passing the following dictionary as input to your model:

Source https://stackoverflow.com/questions/51827712

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

Vulnerabilities

No vulnerabilities reported

Install create_tfrecords

You can download it from GitHub.
You can use create_tfrecords 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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