keras-vis | Neural network visualization toolkit for keras | Machine Learning library

by raghakot Python Version: 0.4.1 License: MIT

X-Ray Key Features Code Snippets(6)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | keras-vis Summary

keras-vis is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-vis has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install keras-vis' or download it from GitHub, PyPI.

keras-vis is a high-level toolkit for visualizing and debugging your trained keras neural net models. Currently supported visualizations include:. All visualizations by default support N-dimensional image inputs. i.e., it generalizes to N-dim image inputs to your model. The toolkit generalizes all of the above as energy minimization problems with a clean, easy to use, and extendable interface. Compatible with both theano and tensorflow backends with 'channels_first', 'channels_last' data format.

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Quality

Security

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Support

keras-vis has a medium active ecosystem.

It has 2948 star(s) with 670 fork(s). There are 72 watchers for this library.

It had no major release in the last 12 months.

There are 113 open issues and 101 have been closed. On average issues are closed in 117 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-vis is 0.4.1

Quality

keras-vis has 0 bugs and 0 code smells.

Security

keras-vis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-vis code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-vis 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

keras-vis releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

keras-vis saves you 462 person hours of effort in developing the same functionality from scratch.

It has 1090 lines of code, 94 functions and 25 files.

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

Top functions reviewed by kandi - BETA

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

Visualize an activation layer
Minimize the function
Post processing
Generate an image with the given losses
Generate Markdown documentation
Generate documentation for a class
Convert a function to markdown
Convert a module to markdown
Compute the absolute value of gradients
Return the absolute absolute value of gradients
Invert gradients
Generate a pre - trained convolutional model
Generate a camera with the given losses
Find the penultimate layer in the model
Draw a text image
Find a font file
Lookup image labels
Ensure value is a list
Performs pre - image pre - processing
Process jitter values
Compute the loss of the image
Normalize an array
Build model

Get all kandi verified functions for this library.

keras-vis Key Features

No Key Features are available at this moment for keras-vis.

keras-vis Examples and Code Snippets

Keras Visualization Toolkit throwing `input:0 is both fed and fetched.` for simple `Sequential` model

Python

Lines of Code : 3

License : Strong Copyleft (CC BY-SA 4.0)

Copy

pip uninstall keras-vis
pip install --user git+https://github.com/raghakot/keras-vis.git

InvalidArgumentError: input_1_1:0 is both fed and fetched

Python

Lines of Code : 3

License : Strong Copyleft (CC BY-SA 4.0)

Copy

pip uninstall vis
pip install git+https://github.com/raghakot/keras-vis.git -U

Changing activation function of a keras layer w/o replacing whole layer

Python

Lines of Code : 8

License : Strong Copyleft (CC BY-SA 4.0)

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from vis.utils.utils import apply_modifications

def update_layer_activation(model, activation, index=-1):
    model.layers[index].activation = activation
    return apply_modifications(model)

model = update_layer_

Error during building of exe using cx_freez

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

from PyQt5.QtGui import QIcon

Saliency Map for Keras CNN LSTM (TF Backend)

Python

Lines of Code : 6

License : Strong Copyleft (CC BY-SA 4.0)

Copy

saliency = keras.backend.gradients(keras.backend.sum(max_outp), inp)

saliency = keras.backend.gradients(keras.backend.sum(max_outp), inp)[0]

saliency = K.gradients(keras.backend.sum(max_out

Tensorflow - Access weights while doing backprop

Python

Lines of Code : 14

License : Strong Copyleft (CC BY-SA 4.0)

Copy

x = tf.get_variable(...)
y = 5.0 * x
tf.gradients(y, x)

@tf.RegisterGradient(name)
def my_grad(op, grad):
    weights = op.inputs[1]
    ...

@tf.RegisterGradient(name)
def my_grad(op, grad)

Community Discussions

Trending Discussions on keras-vis

Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas

How to compute saliency map using keras backend

QUESTION

Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas

Asked 2021-Oct-11 at 14:51

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

...

ANSWER

Answered 2021-Oct-11 at 14:21

geopandas 0.10.1

have noted that your data is on kaggle, so start by sourcing it
there really is only one issue shapely.geometry.MultiPoint() constructor does not work with a filtered series. Pass it a numpy array instead and it works.
full code below, have randomly selected a point to serve as gpdPoint

Source https://stackoverflow.com/questions/69521034

QUESTION

How to compute saliency map using keras backend

Asked 2020-Aug-11 at 14:37

I am trying to construct a basic "vanilla gradient" saliency heatmap (gradient-based feature attribution) for MNIST using keras. I know there are libraries such as this one to compute saliency heatmaps, but I would like to construct this from scratch since the vanilla gradient approach seems conceptually straightforward to implement. I have trained the following digit classifier in Keras using functional model definition:

...

ANSWER

Answered 2020-Aug-11 at 14:37

If you add the activation as a single layer after the last dense layer with:

keras.layers.Activation('softmax')

you can do:

linear_model = keras.Model(input=model, output=model.layers[-2].output)

To then compute the gradients like:

Source https://stackoverflow.com/questions/63107141

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

Vulnerabilities

No vulnerabilities reported

Install keras-vis

In image backprop problems, the goal is to generate an input image that minimizes some loss function. Setting up an image backprop problem is easy. Various useful loss functions are defined in losses. A custom loss function can be defined by implementing Loss.build_loss. In order to generate natural looking images, image search space is constrained using regularization penalties. Some common regularizers are defined in regularizers. Like loss functions, custom regularizer can be defined by implementing Loss.build_loss. Concrete examples of various supported visualizations can be found in examples folder.
Install keras with theano or tensorflow backend. Note that this library requires Keras > 2.0.
Install keras with theano or tensorflow backend. Note that this library requires Keras > 2.0
Install keras-vis

Support

Read the documentation at https://raghakot.github.io/keras-vis. The Japanese edition is https://keisen.github.io/keras-vis-docs-ja.Join the slack channel for questions/discussions.We are tracking new features/tasks in waffle.io. Would love it if you lend us a hand and submit PRs.

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