python_for_microscopists | https://wwwyoutubecom/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

by bnsreenu Jupyter Notebook Version: Current License: No License

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

kandi X-RAY | python_for_microscopists Summary

python_for_microscopists is a Jupyter Notebook library. python_for_microscopists has no bugs, it has no vulnerabilities and it has medium support. You can download it from GitHub.

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

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Support

python_for_microscopists has a medium active ecosystem.

It has 2760 star(s) with 1972 fork(s). There are 84 watchers for this library.

It had no major release in the last 6 months.

There are 47 open issues and 10 have been closed. On average issues are closed in 159 days. There are 9 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of python_for_microscopists is current.

Quality

python_for_microscopists has no bugs reported.

Security

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

License

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

Top functions reviewed by kandi - BETA

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

Normalization of an image
Extracts a feature extractor
Extract feature from input image
Train the discriminator
Save images to images
Generate fake samples
Generate points in the latent space
Grain region segmentation
Load custom annotations
Layer Attention resunet
Attention block
Gating signal
Repeat elements of a tensor
Simple unet model
Attention unetet
Unset convolutional layer
Load data from a JSON file
Predict image using smoothing
Runs the model on the given image or video
Define discriminator
Define an image generator
Calculate memory usage for given model size
Plots a heatmap
Convert labeled image to RGB
Visualize a 3D matrix
Function to plot data to dataframe

Get all kandi verified functions for this library.

python_for_microscopists Key Features

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python_for_microscopists Examples and Code Snippets

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Community Discussions

Trending Discussions on python_for_microscopists

InvalidArgumentError: required broadcastable shapes at loc(unknown)

Training data dimensions for semantic segmentation using CNN

Why is the predicted output (value) from segmentation model library (0 to 1) instead of (0 or 1)?

QUESTION

InvalidArgumentError: required broadcastable shapes at loc(unknown)

Asked 2021-May-29 at 09:07

Background

I am totally new to Python and to machine learning. I just tried to set up a UNet from code I found on the internet and wanted to adapt it to the case I'm working on bit for bit. When trying to .fit the UNet to the training data, I received the following error:

...

ANSWER

Answered 2021-May-29 at 08:40

Try to check whether ks.layers.concatenate layers' inputs are of equal dimension. For example ks.layers.concatenate([u7, c3]), here check u7 and c3 tensors are of same shape to be concatenated except the axis input to the function ks.layers.concatenate. Axis = -1 default, that's the last dimension. To illustrate if you are giving ks.layers.concatenate([u7,c3],axis=0), then except the first axis of both u7 and c3 all other axes' dimension should match exactly, example, u7.shape = [3,4,5], c3.shape = [6,4,5].

Source https://stackoverflow.com/questions/67557515

QUESTION

Training data dimensions for semantic segmentation using CNN

Asked 2021-May-24 at 17:23

I encountered many hardships when trying to fit a CNN (U-Net) to my tif training images in Python.

I have the following structure to my data:

X
- 0
- - [Images] (tif, 3-band, 128x128, values ∈ [0, 255])
X_val
- 0
- - [Images] (tif, 3-band, 128x128, values ∈ [0, 255])
y
- 0
- - [Images] (tif, 1-band, 128x128, values ∈ [0, 255])
y_val
- 0
- - [Images] (tif, 1-band, 128x128, values ∈ [0, 255])

Starting with this data, I defined ImageDataGenerators:

...

ANSWER

Answered 2021-May-24 at 17:23

I found the answer to this particular problem. Amongst other issues, "class_mode" has to be set to None for this kind of model. With that set, the second array in both X and y is not written by the ImageDataGenerator. As a result, X and y are interpreted as the data and the mask (which is what we want) in the combined ImageDataGenerator. Otherwise, X_val_gen already produces the tuple shown in the screenshot, where the second entry is interpreted as the class, which would make sense in a classification problem with images spread out in various folders each labeled with a class ID.

Source https://stackoverflow.com/questions/67644593

QUESTION

Why is the predicted output (value) from segmentation model library (0 to 1) instead of (0 or 1)?

Asked 2021-May-01 at 18:47

While performing semantic-segmentation task by following this tutorial ,
I noticed that the final predicted output from the model is not 0 and 1,
it consists of decimal values from 0.0000xxxx to 1.0.

Since the model took in the label of 0 and 1 only,
what is the meaning of the the decimal values range in the output?
(The possibility of the pixels belonging to a certain class?)

...

ANSWER

Answered 2021-May-01 at 11:08

I have found about this from the same youtube tutorial in a different video (19.20 ~ 20.04).

The values in the prediction indeed reflecting the probability of a pixel to it's corresponding class.

In this case, it is referring the probability of the current pixel to be the membrane.

Source https://stackoverflow.com/questions/67336715

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

Vulnerabilities

No vulnerabilities reported

Install python_for_microscopists

You can download it from GitHub.

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