tensorflow_input_image_by_tfrecord | image data to a pre-trained network | Machine Learning library
kandi X-RAY | tensorflow_input_image_by_tfrecord Summary
kandi X-RAY | tensorflow_input_image_by_tfrecord Summary
tensorflow_input_image_by_tfrecord is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Docker applications. tensorflow_input_image_by_tfrecord has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tensorflow_input_image_by_tfrecord build file is not available. You can download it from GitHub.
Feed your own image data to a pre-trained network by tensorflow
Feed your own image data to a pre-trained network by tensorflow
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Support
tensorflow_input_image_by_tfrecord has a low active ecosystem.
It has 167 star(s) with 120 fork(s). There are 10 watchers for this library.
It had no major release in the last 6 months.
There are 1 open issues and 10 have been closed. On average issues are closed in 48 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of tensorflow_input_image_by_tfrecord is current.
Quality
tensorflow_input_image_by_tfrecord has 0 bugs and 31 code smells.
Security
tensorflow_input_image_by_tfrecord has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
tensorflow_input_image_by_tfrecord code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
tensorflow_input_image_by_tfrecord is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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tensorflow_input_image_by_tfrecord releases are not available. You will need to build from source code and install.
tensorflow_input_image_by_tfrecord 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.
tensorflow_input_image_by_tfrecord saves you 418 person hours of effort in developing the same functionality from scratch.
It has 991 lines of code, 68 functions and 11 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed tensorflow_input_image_by_tfrecord and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow_input_image_by_tfrecord implemented functionality, and help decide if they suit your requirements.
- Process image files
- Convert to tf Example
- Check if filename is a PNG file
- Creates a tf train Feature
- Process an image
- Train flower model
- Full flower inference
- Read flower input
- Reads and decodes a TFRecord file
- Get list of tfrecord files
- Lists all tfrecord files
- Processes a single dataset
- Return a list of image files and labels
- Decode an image file
- Convert images to tfrecord
- Displays a single image
- Example example
- Evaluate the prediction
Get all kandi verified functions for this library.
tensorflow_input_image_by_tfrecord Key Features
No Key Features are available at this moment for tensorflow_input_image_by_tfrecord.
tensorflow_input_image_by_tfrecord Examples and Code Snippets
No Code Snippets are available at this moment for tensorflow_input_image_by_tfrecord.
Community Discussions
Trending Discussions on tensorflow_input_image_by_tfrecord
QUESTION
Second feed_dict in sess.run(), Evaluation while training
Asked 2018-Mar-26 at 01:21
The infamous:
...ANSWER
Answered 2018-Mar-26 at 01:21Answered my own question: For anyone else having this issue, it turns out that the tf.summary part of the graph required the training labels placeholder! Removing that from the validation loop fixed the issue immediately, while adding adjusting the summary graph solved the problem. Sorry for cluttering SO.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install tensorflow_input_image_by_tfrecord
You can download it from GitHub.
You can use tensorflow_input_image_by_tfrecord 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.
You can use tensorflow_input_image_by_tfrecord 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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