tfrecords | Functions for creating tfrecords for TensorFlow models | Machine Learning library

by visipedia Python Version: Current License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | tfrecords Summary

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

Convenience functions to create tfrecords that can be used with classification, detection and keypoint localization systems. The create_tfrecords.py file will help create the correct tfrecords to feed into those systems. There are configuration parameters that dictate whether to store the raw images in the tfrecords or not (store_images=True in create_tfrecords.create method or --store_images when calling create_tfrecords.py from the command line). If you choose not to store the raw images in the tfrecords, then you should be mindful that the filename field needs to be a valid path on the system where you will be processing the tfrecords. Also, if those images are too big, you may find that your input pipelines for your model struggle to fill the input queues. Resizing images to 800px seems to work well.

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Support

tfrecords has a low active ecosystem.

It has 107 star(s) with 29 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 3 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tfrecords is current.

Quality

tfrecords has 0 bugs and 0 code smells.

Security

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

tfrecords code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

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.

tfrecords saves you 221 person hours of effort in developing the same functionality from scratch.

It has 540 lines of code, 21 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Process image files
Convert an image
Process an image
Check if filename is a PNG file
Validate text
Decode the given image
Creates a tf train Feature
Construct a tf train Feature
Converts a PNG image to JPEG format
Yield the outputs from a tfrecords file
Decodes a serialized example
Verify that the bboxes are valid
Creates training images
Compute class statistics
Parse command line arguments

Get all kandi verified functions for this library.

tfrecords Key Features

No Key Features are available at this moment for tfrecords.

tfrecords Examples and Code Snippets

Create a parallel interleave dataset .

python

Lines of Code : 58

License : Non-SPDX (Apache License 2.0)

Copy

def parallel_interleave(map_func,
                        cycle_length,
                        block_length=1,
                        sloppy=False,
                        buffer_output_elements=None,
                        prefetch_input_elements

Creates a tf record reader for the given path .

python

Lines of Code : 39

License : Non-SPDX (Apache License 2.0)

Copy

def tf_record_random_reader(path):
  """Creates a reader that allows random-access reads from a TFRecords file.

  The created reader object has the following method:

    - `read(offset)`, which returns a tuple of `(record, ending_offset)`, where

Create a record iterator for the given path .

python

Lines of Code : 15

License : Non-SPDX (Apache License 2.0)

Copy

def tf_record_iterator(path, options=None):
  """An iterator that read the records from a TFRecords file.

  Args:
    path: The path to the TFRecords file.
    options: (optional) A TFRecordOptions object.

  Returns:
    An iterator of serialized T

Community Discussions

Trending Discussions on tfrecords

Convert tfrecords to image

Save tensorflow model with StringLookup layer with encoded vocabulary

Storing multiple values in a tfrecord feature

How to write a tfrecord file and read it? The error is: truncated record at 0' failed with Read less bytes than requested [Op:IteratorGetNext]

How to feed tfrecord file in a model and train?

Writing Pyspark Dataframe to TFrecords file

Reading back a custom dataset TFRecords

does tf.data.Dataset .from_tensor_slices() preserve the order of examples?

Why low mAP on fine-tuned model from Tensorflow 2 Object Detection API?

How to use Tensorflow Hub models on cloud TPU?

QUESTION

Convert tfrecords to image

Asked 2022-Mar-11 at 08:15

I found a training dataset which is a set of tfrecords files,im trying to convert them into images but with no results,is it possible to convert them to images ?

...

ANSWER

Answered 2022-Mar-11 at 08:15

To find out what is inside a tf.record use tf.data.TFRecordDataset and tf.train.Example:

Source https://stackoverflow.com/questions/71428861

QUESTION

Save tensorflow model with StringLookup layer with encoded vocabulary

Asked 2022-Feb-04 at 17:07

I'm having some issues saving a trained TensorFlow model, where I have a StringLookup layer and I'm required to use TFRecods as input for training. A minimal example to reproduce the issue:

First I define the training data

...

ANSWER

Answered 2022-Feb-04 at 17:07

Using your data and original vocabulary:

Source https://stackoverflow.com/questions/70988847

QUESTION

Storing multiple values in a tfrecord feature

Asked 2022-Jan-13 at 20:56

image_id class_1_rle class_2_rle class_3_rle 0002cc93b.jpg 29102 12 29346 24... 0007a71bf.jpg 18661 28 18863 82... 000a4bcdd.jpg 131973 1 132228 4... 229501 11 229741 33...

I am trying to create the tfrecords using the above table. I need to get together the rle (Run Length Encoding) feature in the form of rle per class. Eg. the feature in the final tfrecord looks like

...

ANSWER

Answered 2022-Jan-13 at 20:56

You achieve this in 3 simple steps, although it is hard to say what you actually intend to do without further details:

Create and parse data:

Source https://stackoverflow.com/questions/70687805

QUESTION

How to write a tfrecord file and read it? The error is: truncated record at 0' failed with Read less bytes than requested [Op:IteratorGetNext]

Asked 2022-Jan-13 at 12:20

I want to use tfrecord to deal with heavy MRI images but I don't know how to. Below is my code, the error and data link. (Sorry if you find the code is a bit long).

About the data:

484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
484 labels, each has a shape of (240, 240, 155)

First I rearrange my data,

...

ANSWER

Answered 2021-Nov-29 at 10:37

This error is occurring because you never call close() after writing the example into a file: Here is a working example with random arrays:

Source https://stackoverflow.com/questions/70151981

QUESTION

How to feed tfrecord file in a model and train?

Asked 2022-Jan-13 at 06:49

I wrote a tfrecord file and fed in my Unet model but got a problem with the input shape. Below is my code.

About the data:

484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
484 labels, each has a shape of (240, 240, 155)

I used the first 2 examples:

...

ANSWER

Answered 2021-Nov-29 at 16:49

Your model expects the shape (samples, 240, 240, 155, 4), so try something like this:

Source https://stackoverflow.com/questions/70158072

QUESTION

Writing Pyspark Dataframe to TFrecords file

Asked 2022-Jan-12 at 17:29

I have a dataframe with schema, and want to convert this into tfRecords

...

ANSWER

Answered 2022-Jan-12 at 17:29

The most probable cause (judging from Maven Central information) is that you're using connector compiled for Scala 2.11 on the Databricks runtime that uses Scala 2.12.

Either you need to use DBR 6.4 for that conversion, or compile connector for Scala 2.12 & use.

Source https://stackoverflow.com/questions/70683969

QUESTION

Reading back a custom dataset TFRecords

Asked 2022-Jan-04 at 17:01

I am trying to create a custom dataset in TFRecords for a CycleGAN model. The model requires a new type of dataset which is not available so I need to create one. I have a few JPG images of 256x256. Following this link, I created TFrecords file for my images, below code:

...

ANSWER

Answered 2022-Jan-04 at 10:24

I updated the read_tfrecord function like below, see the commented out line and that solves this particular error.

Source https://stackoverflow.com/questions/70576891

QUESTION

does tf.data.Dataset .from_tensor_slices() preserve the order of examples?

Asked 2021-Dec-02 at 08:59

If I have a set of tfrecords, using .from_tensor_slices() here, will dataset created preserve the order of the data? For example, if I have 3 tfrecords (the first one contains 40 examples, the second one contains 30 examples, the third one contains 70 examples) called 1.tfrecord, 2.tfrecord, 3.tfrecord respectively, then I construct dataset = tf.data.Dataset.from_tensor_slices(['1.tfrecord', '2.tfrecord', '3.tfrecord']). During loading, will the order of these examples preserved?

...

ANSWER

Answered 2021-Nov-12 at 12:39

If I understood your question correctly, yes, the order of examples is preserved when using tf.data.Dataset.from_tensor_slices with tfrecord. Here is a simple example:

Source https://stackoverflow.com/questions/69941348

QUESTION

Why low mAP on fine-tuned model from Tensorflow 2 Object Detection API?

Asked 2021-Nov-07 at 23:41

I follow all the steps and read everything online and I trained successfully SSD-MobileNetV1 from Model Zoo of TF2 OD API.

I fine-tuned this model with new classes "Handgun" and "Knife" and I use a balanced dataset of 3500 images. The training proceeds well, but when I run the evaluation process (for validation) with "pascal_voc_detection_metrics" I achieved 0.005 AP@0.5 (The detection model manages to reach only 0.005 more or less of AP) with the class "Handgun" which is very low, but 0.93 AP@0.5 with the class "Knife".

I didn't understand why. I really read everything but I can't find the solution.

config of SDD-MobileNetV1:

...

ANSWER

Answered 2021-Nov-07 at 23:41

It's a bug of the library as reported at this link. COCO metrics don't have this problem, so use it to evaluate your model. The problem is not fixed yet. If you want to follow updates made to the code(they work fine) please follow the previous link and also this link

Source https://stackoverflow.com/questions/69855511

QUESTION

How to use Tensorflow Hub models on cloud TPU?

Asked 2021-Oct-12 at 16:33

I'm trying to use a model from tensorflow hub on Kaggle. Like so:

...

ANSWER

Answered 2021-Oct-12 at 16:33

The failure happens because the TPU is trying to load the TFHub model from /tmp/ which it doesn't have access to. You should be able to get this to work by:

Source https://stackoverflow.com/questions/69113443

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

Vulnerabilities

No vulnerabilities reported

Install tfrecords

You can download it from GitHub.
You can use 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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