semvec/pypi-packages huggingface dataset:
keras-drop-connect 0.2.0
Drop-connect wrapper
License: MIT

# Keras Drop-Connect

[![Travis](https://travis-ci.org/CyberZHG/keras-drop-connect.svg)](https://travis-ci.org/CyberZHG/keras-drop-connect)
[![Coverage](https://coveralls.io/repos/github/CyberZHG/keras-drop-connect/badge.svg?branch=master)](https://coveralls.io/github/CyberZHG/keras-drop-connect)
[![Version](https://img.shields.io/pypi/v/keras-drop-connect.svg)](https://pypi.org/project/keras-drop-connect/)
![Downloads](https://img.shields.io/pypi/dm/keras-drop-connect.svg)
![License](https://img.shields.io/pypi/l/keras-drop-connect.svg)

![](https://img.shields.io/badge/keras-tensorflow-blue.svg)
![](https://img.shields.io/badge/keras-tf.keras-blue.svg)
![](https://img.shields.io/badge/keras-tf.keras/eager-blue.svg)
![](https://img.shields.io/badge/keras-tf.keras/2.0.0_beta-blue.svg)

\[[中文](https://github.com/CyberZHG/keras-bert/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-bert/blob/master/README.md)\]

## Install

```bash
pip install keras-drop-connect
```

## Usage

Set drop connect rate for all weights:

```python
import keras
from keras_drop_connect import DropConnect

DropConnect(
    layer=keras.layers.Dense(units=10),
    rate=0.2,
)
```

Set drop connect rate with a dict:

```python
import keras
from keras_drop_connect import DropConnect

DropConnect(
    layer=keras.layers.Dense(units=10),
    rate={'kernel': 0.2},
)
```
tensorboardY 0.1.9
Interact with ML models in the browser so that we can better understand their strengths and weaknesses on real world data
License: MIT
Requires Python: >=2.5.0


# tensorboardY
The easier it is to interact with ML models, the faster we can determine their current limitations. This library seeks to automate the creation of cool ML demo websites like

* [style transfer](https://reiinakano.github.io/arbitrary-image-stylization-tfjs/)
* [Megadepth](http://megadepthdemo.pythonanywhere.com/)
* [Talk to transformer](https://talktotransformer.com/?ref=producthunt)
* [GLTR](http://gltr.io/dist/index.html)
* [Wayve demo](http://perception.wayve.ai/)
* [FUNIT](https://nvlabs.github.io/FUNIT/petswap.html)

Documentation is [here](https://jonahthelion.github.io/tensorboardY/) (generated using [pdoc3](https://pdoc3.github.io/pdoc/)).
The github repo is [here](https://github.com/jonahthelion/tensorboardY).

## Install
`pip install tensorboardY`

## Examples
* `python examples/simple_example.py`

```
import tensorboardY as ty
import matplotlib.pyplot as plt

def forward(x, title):
    plt.imshow(x)
    plt.title(title)
    return plt.gcf()

inputs = [ty.Image(var='x', exs=['imgs/curve.jpg']),
          ty.Text(var='title', exs=["EXAMPLE"])]

ty.show(forward, inputs)
```
<img src="https://github.com/jonahthelion/tensorboardY/raw/master/imgs/simple_example_site.png" width="800" />

* `python examples/full_example.py`

```
import tensorboardY as ty
import os

def forward(z):
    return z

inputs = [ty.Widget("z", name="Choose your input",
                    camera=True,
                    image_upload=True,
                    image_list=['imgs/curve.jpg'], image_names=['Curve example'],
                    text_input=True,
                    text_list=['This is an example text!'], text_names=['Random'],
                    option_list=["This is an example option!"], option_names=['Resnet50'],
                    boolean=True,
                    slider=(5, 20, 0.5), slider_default=10.3)]

ty.show(forward, inputs)
```
<img src="https://github.com/jonahthelion/tensorboardY/raw/master/imgs/full_example_site.png" width="800" />
madmac 1.0.1
madmac: MAC address generator
License: MIT
Requires Python: >=3.5.2


# MadMAC [![Build Status](https://travis-ci.com/laminko/madmac.svg?branch=master)](https://travis-ci.com/laminko/madmac)

MAC address generator library for testers.

## Content

- [Installation](#installation)
- [Usage](#usage)
  - [Command](#as-a-command)
  - [Module](#as-a-module)

### Installation

Using `pip`:

> `pip install madmac`

Using source:

```bash
$ git clone https://github.com/laminko/madmac.git
$ cd madmac
$ python setup.py install
```

### Usage

#### As a command

Can be used `madmac` as command. The following will generate random MAC address.

```bash
madmac
```

To see help, enter `madmac --help`.

```bash
madmac --help
usage: madmac [-h] [-o OUI] [-r START] [-s STOP] [-t TOTAL] [-d DELIMITER]
              [-c CASE]

MAC address generator library for testers.

optional arguments:
  -h, --help            show this help message and exit
  -o OUI, --oui OUI     6-digit organizationally unique identifier
  -r START, --start START
                        NIC specific start address
  -s STOP, --stop STOP  NIC specific end address
  -t TOTAL, --total TOTAL
                        Number of MACs to generate
  -d DELIMITER, --delimiter DELIMITER
                        Delimiter for MAC address
  -c CASE, --case CASE  Use lower or upper
```

> **NOTE**: `madmac` is not a binary file. You need to install python 3.5 to execute the command.

#### As a module

Import `MacGenerator` class from `madmac` module. And create an object using `MacGenerator` and call its member `generate()` function. `generate()` function will return python generator object.

The following code will generate single MAC address using default values.

```python
from madmac import MacGenerator

macg = MacGenerator()
macs = macg.generate()  # generator object which contains one item
from pprint import pprint
pprint(list(macs))
```

Default values are as follows:

```python
# 'oui':  None,
# 'start': None,
# 'stop': None,
# 'total': 1,
# 'delimiter': ':',
# 'case': 'lower'
```

One can provide `oui`:

```python
from madmac import MacGenerator

macg = MacGenerator(oui='F0-9F-C2')
list(macg.generate())
```

Also specify `start` address and `end` address if they are known:

```python
from madmac import MacGenerator

start = '00-B0-A0'
stop = '00-B0-DF'
macg = MacGenerator(start=start, stop=stop)
```

> **NOTE**: Above snippet describes to use random `oui`, but to use certain range from `start` and `stop` values. It will ignore `total` parameter. Delimiter and Case will be default values.

Sometimes, we might want to generate certain amount of MAC addresses:

```python
from madmac import MacGenerator

macg = MacGenerator(total=100)
```