keras-tqdm | Keras integration with TQDM progress bars | Machine Learning library

by bstriner Python Version: 2.0.1 License: MIT

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

kandi X-RAY | keras-tqdm Summary

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

Keras integration with TQDM progress bars

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Quality

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Support

keras-tqdm has a low active ecosystem.

It has 348 star(s) with 38 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 18 open issues and 11 have been closed. On average issues are closed in 11 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-tqdm is 2.0.1

Quality

keras-tqdm has 0 bugs and 0 code smells.

Security

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

keras-tqdm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-tqdm 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-tqdm releases are available to install and integrate.

Deployable package is available in PyPI.

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

Top functions reviewed by kandi - BETA

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

Generate an MNIST model
Build the model
Generate a generator of n - points
Return a list of MNIST data
Process the numpy array
Initialize tqdm progress bar
Run tqdm progress bar
Build a tqdm progress bar
Add logs to Tqdm
Append logs to the running logs
Format metrics
Update Tqdm progress bar
Builds an MNIST model
Initialize tqdm

Get all kandi verified functions for this library.

keras-tqdm Key Features

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

keras-tqdm Examples and Code Snippets

No Code Snippets are available at this moment for keras-tqdm.

Community Discussions

Trending Discussions on keras-tqdm

AsyncResult hangs in unexpected cases in fit_generator of tensorflow's keras

Keras with tensorflow-gpu totally freezes PC

QUESTION

AsyncResult hangs in unexpected cases in fit_generator of tensorflow's keras

Asked 2020-Jan-29 at 09:57

This is a copy-paste of an issue I posted on the tensorflow Github.

System information

Have I written custom code: yes
OS Platform and Distribution: Linux Ubuntu 16.04
TensorFlow installed from: pip
TensorFlow version: 2.0.0b1
Python version: 3.6.8
CUDA/cuDNN version: V10.0.130
GPU model and memory: Quadro P5000 (16GB)

Describe the current behavior

I have a very complicated model solving an image-to-image problem. I also use a custom callback which at some point generates some noise using numpy. When I use fit_generator on this model, it manages to do the first epoch, then on the second, third or fourth it hangs at the beginning of the epoch. I managed to see where the problem was happening, and it happens here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/utils/data_utils.py#L875 Basically, if I put a timeout on the second get it times out after a few successful epochs (sometimes just one). There is no error thrown out so I don't know why it hangs. Furthermore, if I debug at that point in code, I can just execute the function synchronously and everything will work just fine.

Code to reproduce the issue

I didn't manage to get a minimal example using fit_generator (basically it relies too much on me using my model which is complex). However, I have a minimal example which reproduces the bug when I mimic the model_iteration function. You need to install the following to make it work: pip install tensorflow-gpu==2.0.0b1 numpy tqdm

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ANSWER

Answered 2020-Jan-29 at 09:57

This issue has been resolved in version 2.1.

Another fix would be to use the new random number generation API of numpy as advised here. That changes the line noise = np.random.normal(scale=1.0, size=image_shape) to noise = np.random.default_rng().normal(scale=1.0, size=image_shape). This fix works even in version 2.0.

This is a copy-paste of the answer I gave on Github.

Source https://stackoverflow.com/questions/58957519

QUESTION

Keras with tensorflow-gpu totally freezes PC

Asked 2019-Jan-21 at 09:20

I have pretty simple architecture lstm NN. After few epoch 1-2 my PC totally freezes I can't even move my mouse :

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ANSWER

Answered 2018-Jul-14 at 18:03

Please remove cpu version of tensorflow==1.0.1 first. Try installing the tensorflow-gpu==1.8.0 by building TensorFlow from sources as mentioned here

Replace LSTM with CuDNNLSTM while training model on GPU. Later load the trained model weights into same model architecture with LSTM layer to use the model on CPU. (Make sure to use recurrent_activation='sigmoid' in LSTM layer when re-loading CuDNNLSTM model weights!)

Source https://stackoverflow.com/questions/51320027

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

Vulnerabilities

No vulnerabilities reported

Install keras-tqdm

You can install using 'pip install keras-tqdm' or download it from GitHub, PyPI.
You can use keras-tqdm 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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