u-net-brain-tumor | U-Net Brain Tumor Segmentation | Machine Learning library

by zsdonghao Python Version: 0.1 License: No License

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kandi X-RAY | u-net-brain-tumor Summary

u-net-brain-tumor is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. u-net-brain-tumor has no bugs, it has no vulnerabilities and it has low support. However u-net-brain-tumor build file is not available. You can download it from GitHub.

Note that according to the license, user have to apply the dataset from BRAST, please do NOT contact me for the dataset. Many thanks. The prepare_data_with_valid.py split the training set into 2 folds for training and validating. By default, it will use only half of the data for the sake of training speed, if you want to use all data, just change DATA_SIZE = 'half' to all.

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u-net-brain-tumor has a low active ecosystem.

It has 458 star(s) with 183 fork(s). There are 33 watchers for this library.

It had no major release in the last 12 months.

There are 41 open issues and 18 have been closed. On average issues are closed in 105 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of u-net-brain-tumor is 0.1

Quality

u-net-brain-tumor has 0 bugs and 45 code smells.

Security

u-net-brain-tumor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

u-net-brain-tumor code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

u-net-brain-tumor 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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u-net-brain-tumor releases are available to install and integrate.

u-net-brain-tumor 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.

u-net-brain-tumor saves you 193 person hours of effort in developing the same functionality from scratch.

It has 476 lines of code, 6 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Load training and validation set .
U_net layer .
Unet layer .
Distortion images
Wrapper for visimages2 .
Saves images to images

Get all kandi verified functions for this library.

u-net-brain-tumor Key Features

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u-net-brain-tumor Examples and Code Snippets

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Community Discussions

Trending Discussions on u-net-brain-tumor

tensorflow, image segmentation convnet InvalidArgumentError: Input to reshape is a tensor with 28800000 values, but the requested shape has 57600

QUESTION

tensorflow, image segmentation convnet InvalidArgumentError: Input to reshape is a tensor with 28800000 values, but the requested shape has 57600

Asked 2018-May-27 at 18:23

I am trying to segment images from the BRATS challenge. I am using U-net in a combination of these two repositories:

https://github.com/zsdonghao/u-net-brain-tumor

https://github.com/jakeret/tf_unet

When I try to output the prediction statistics a mismatch shape error come up:

InvalidArgumentError: Input to reshape is a tensor with 28800000 values, but the requested shape has 57600 [[Node: Reshape_2 = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Cast_0_0, Reshape_2/shape)]]

I am using image slices 240x240, with a batch_verification_size = 500

Then,

this is shape test_x: (500, 240, 240, 1)
this is shape test_y: (500, 240, 240, 1)
this is shape test x: (500, 240, 240, 1)
this is shape test y: (500, 240, 240, 1)
this is shape batch x: (500, 240, 240, 1)
this is shape batch y: (500, 240, 240, 1)
this is shape prediction: (500, 240, 240, 1)
this is cost : Tensor("add_88:0", shape=(), dtype=float32)
this is cost : Tensor("Mean_2:0",shape=(), dtype=float32)
this is shape prediction: (?, ?, ?, 1)
this is shape batch x: (500, 240, 240, 1)
this is shape batch y: (500, 240, 240, 1)

240 x 240 x 500 = 28800000 I don't know why is requesting 57600

It looks like the error is emerging from output_minibatch_stats function:

...

ANSWER

Answered 2018-May-27 at 18:23

You set your batch size as 1 in your tensorflow pipeline during training but feeding in 500 batch size in your testing data. Thats why the network requests only a tensor of shape 57600. You can either set your training batch size 500 or testing batch size as 1.

Source https://stackoverflow.com/questions/50552806

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

Vulnerabilities

No vulnerabilities reported

Install u-net-brain-tumor

You can download it from GitHub.
You can use u-net-brain-tumor 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.

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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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