semvec/pypi-packages huggingface dataset:
wandb-callbacks 0.2.1
Additional custom callbacks for Weights & Biases
Keywords: wandb,keras,tensorflow,callbacks

[![PyPI version](https://badge.fury.io/py/wandb-callbacks.svg)](https://badge.fury.io/py/wandb-callbacks)

# Weights &amp; Biases Callbacks
`wandb-callbacks` provides some additional Callbacks for Weights &amp; Biases.

Callbacks currently implemented:
* `ActivationCallback`
  * visualizes the activations of a layer
  * activations are computed for a sample of each class
* `DeadReluCallback`
  * logs the number of dead relus in each layer
  * prints warning if the percentage is higher than a threshold
* `GradCAMCallback`
  *  [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://arxiv.org/pdf/1610.02391.pdf)
  *  produces a coarse localization map highlighting the important regions in the image for predicting the class of the image

## Sample Implementation
Can be found in `notebooks/sample_implementation.ipynb`.
acestream 0.2.0
Interact with the AceStream Engine and the HTTP API

# Python AceStream
Python interface to interact with the AceStream [Engine](https://wiki.acestream.media/Streaming), [HTTP API](https://wiki.acestream.media/Engine_HTTP_API) and [Search API](https://wiki.acestream.media/Search_API).

## Installation
```
pip install acestream
```

## Usage
```python
import time
import subprocess

from acestream.server import Server
from acestream.engine import Engine
from acestream.stream import Stream

# Create an engine instance
engine = Engine('acestreamengine', client_console=True)

# Connect to a remote server
server = Server(host='streams.com', port=6880)

# If the remote server is not available, connect to a local server
if not server.available:
  server = Server(host='127.0.0.1', port=6878)

  # Start engine if the local server is not available
  if not server.available:
    engine.start()

    # Wait for engine to start
    while not engine.running:
      time.sleep(1)

# Start a stream using an acestream channel ID
stream = Stream(server, id='ff36fce40a7d2042e327eaf9f215a1e9cb622b56')
stream.start()

# Open a media player to play the stream
player = subprocess.Popen(['mpv', stream.playback_url])

# Wait for player to close and stop the stream
player.communicate()
stream.stop()

# Stop acestream engine
engine.stop()
```

## Search
```python
import time
import random
import subprocess

from acestream.server import Server
from acestream.engine import Engine
from acestream.search import Search

# Create an engine instance
engine = Engine('acestreamengine', client_console=True)

# Connect to a local server
server = Server(host='127.0.0.1', port=6878)

# Start engine if the local server is not available
if not server.available:
  engine.start(stdout=subprocess.PIPE, stderr=subprocess.PIPE)

  # Wait for engine to start
  while not engine.running:
    time.sleep(1)

# Start a search for the sport category
search = Search(server, category='sport')
search.get(page=1)

# Iterate and print search results
for result in search.results:
  print("%40s %10s %40s" % (result.name, result.bitrate, result.infohash))

# Start a random stream from the search results
stream = random.choice(search.results).stream
stream.start(hls=True, transcode_audio=True)

# Open a media player to play the stream
player = subprocess.Popen(['mpv', stream.playback_url])

# Wait for player to close and stop the stream
player.communicate()
stream.stop()

# Stop acestream engine
engine.stop()
```

## Contributing
Bug reports and pull requests are welcome on GitHub at https://github.com/jonian/python-acestream.

## License
Python AceStream is available as open source under the terms of the [GPLv3](http://www.gnu.org/licenses/gpl-3.0.en.html)
gclassifier 0.6
Gender classifier module for python

# GENDER-CLASSIFIER
[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

Gender classifier is a python libary written over a classification model which can classify gender of the human inside the image
  - Classify gender from a image
  - Model train on tensorflow (CNN)


#### Building for source
First install gclassifier from pip:
```sh
>>> pip install gclassifier
```


### Run the drawing code:

Step 1 :  Import module for classifier
```
>>> from gclassifier import image_classifier
>>> import cv2
```
Step 2 :  Runn the classifier
```
>>> img = cv2.imread('7.jpg')
>>> a = image_classifier(frame=img)
['woman']
```

License
----
MIT

PYPI MODULE
----------------
https://pypi.org/project/gclassifier/

### NOTE :
```
This modlue have been inspired from - https://www.youtube.com/watch?v=WOuAI5DhHyU

Author: Dip Ghosh
email: dipghoshraj@gmail.com
IOT and robotics developer with a handson experience in Software development and DevOps.
```


<!-- **Free Software, Yeah it's fucking truth!** -->