imageRecognises
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：82206KB
下载次数：38
上传日期：2018-03-22 20:10:19
上 传 者：
月痕·踏雪
说明： Python编程Keras训练神经网络,识别猫狗图片
(Python programming and Keras training neural network,recognition the cat or dog in the picture.)
文件列表:
imageRecognises\imageRecognise\.idea\imageRecognise.iml (603, 2018-03-21)
imageRecognises\imageRecognise\.idea\jsLibraryMappings.xml (187, 2017-09-09)
imageRecognises\imageRecognise\.idea\libraries\R_User_Library.xml (128, 2018-03-21)
imageRecognises\imageRecognise\.idea\modules.xml (280, 2017-09-09)
imageRecognises\imageRecognise\.idea\runConfigurations\bin_www.xml (560, 2017-09-09)
imageRecognises\imageRecognise\.idea\workspace.xml (41082, 2018-03-22)
imageRecognises\imageRecognise\233.h5 (4879872, 2017-09-12)
imageRecognises\imageRecognise\233.json (4116, 2017-09-12)
imageRecognises\imageRecognise\app.js (1256, 2017-09-09)
imageRecognises\imageRecognise\bin\www (1600, 2017-09-09)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\model1.png (30304, 2018-03-21)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\my_model_architecture.json (4116, 2017-09-09)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\my_model_weights.h5 (4879872, 2017-09-09)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.0.jpg (12414, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.1.jpg (16880, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.2.jpg (24692, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.3.jpg (37971, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.4.jpg (20625, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\cat.5.jpg (5382, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.0.jpg (32053, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.1.jpg (25034, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.2.jpg (8490, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.3.jpg (28457, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.4.jpg (12992, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict\dog.5.jpg (37907, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\predict.py (898, 2018-03-21)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\test.py (1317, 2018-03-22)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\train.py (3004, 2018-03-22)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1000.jpg (5944, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1001.jpg (23099, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1002.jpg (16999, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1003.jpg (13996, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1004.jpg (41052, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1005.jpg (33372, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1006.jpg (23571, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1007.jpg (22455, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1008.jpg (25939, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1009.jpg (20397, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1010.jpg (28075, 2013-09-20)
imageRecognises\imageRecognise\CNN猫狗分类\CNN猫狗分类\try_test\cat\cat.1011.jpg (25313, 2013-09-20)
... ...
# ms
[](https://travis-ci.org/zeit/ms)
[](https://zeit.chat/)
Use this package to easily convert various time formats to milliseconds.
## Examples
```js
ms('2 days') // 172800000
ms('1d') // 8***00000
ms('10h') // 36000000
ms('2.5 hrs') // 9000000
ms('2h') // 7200000
ms('1m') // 60000
ms('5s') // 5000
ms('1y') // 31557600000
ms('100') // 100
```
### Convert from milliseconds
```js
ms(60000) // "1m"
ms(2 * 60000) // "2m"
ms(ms('10 hours')) // "10h"
```
### Time format written-out
```js
ms(60000, { long: true }) // "1 minute"
ms(2 * 60000, { long: true }) // "2 minutes"
ms(ms('10 hours'), { long: true }) // "10 hours"
```
## Features
- Works both in [node](https://nodejs.org) and in the browser.
- If a number is supplied to `ms`, a string with a unit is returned.
- If a string that contains the number is supplied, it returns it as a number (e.g.: it returns `100` for `'100'`).
- If you pass a string with a number and a valid unit, the number of equivalent ms is returned.
## Caught a bug?
1. [Fork](https://help.github.com/articles/fork-a-repo/) this repository to your own GitHub account and then [clone](https://help.github.com/articles/cloning-a-repository/) it to your local device
2. Link the package to the global module directory: `npm link`
3. Within the module you want to test your local development instance of ms, just link it to the dependencies: `npm link ms`. Instead of the default one from npm, node will now use your clone of ms!
As always, you can run the tests using: `npm test`
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