提交 006d9e01 编写于 作者: W wizardforcel

2020-08-14 18:56:32

上级 c0ef73e6
......@@ -99,7 +99,7 @@ An example of pooling operation
另一个选择是“平均池化”,它可以将一个区域简单地汇总为在该区域中观察到的激活平均值。
TensorFlow implements a large number of pooling layers and a complete list is available online. ([https://www.tensorflow.org/api_guides/python/nn#Pooling](https://www.tensorflow.org/api_guides/python/nn#Pooling)) In short, all the pooling operations are nothing more than a summary operation on a given region.
TensorFlow 实现了大量池化层,[可在线获取完整列表](https://www.tensorflow.org/api_guides/python/nn#Pooling)。简而言之,所有池化操作仅是对给定区域的汇总操作。
# ConvNets 摘要
......@@ -1006,7 +1006,7 @@ Google 于 2014 年训练了神经网络以应对 **ImageNet 大规模视觉识
![](img/cbbf2fe8-00f2-4ee3-b340-050425f20a7c.jpg)
An example of Google Deep Dreams as seen in [https://commons.wikimedia.org/wiki/File:Aurelia-aurita-3-0009.jpg](https://commons.wikimedia.org/wiki/File:Aurelia-aurita-3-0009.jpg)
[如以下所示的 Google Deep Dreams 示例](https://commons.wikimedia.org/wiki/File:Aurelia-aurita-3-0009.jpg)
# 做好准备
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......@@ -136,7 +136,7 @@ Note that the model uses multiple filters (with varying window sizes) to obtain
![](img/15a29ebe-9ed0-4a68-a5f6-a71a16b24261.png)
An example of model architecture as seen in [https://arxiv.org/pdf/1408.5882.pdf](https://arxiv.org/pdf/1408.5882.pdf)
[如图所示的模型架构示例](https://arxiv.org/pdf/1408.5882.pdf)
使用 CNN 进行文本分析是一个活跃的研究领域。 我建议看看以下文章:
......@@ -152,7 +152,7 @@ An example of model architecture as seen in [https://arxiv.org/pdf/1408.5882.pdf
![](img/75e30d9a-ab59-44ab-918e-5c0dd6dd7c5a.jpg)
An example of American Dipper as seen on [https://commons.wikimedia.org/wiki/File:American_Dipper.jpg](https://commons.wikimedia.org/wiki/File:American_Dipper.jpg)
[美国北斗星的一个例子](https://commons.wikimedia.org/wiki/File:American_Dipper.jpg)
可以在网上找到 [ImageNet 映射](https://gist.github.com/yrevar/6135f1bd8dcf2e0cc683)作为 python 泡菜字典,其中 ImageNet 1000 类 ID 映射到了人类可读的标签。
......@@ -297,7 +297,7 @@ GitHub 上的 [keras-vis 存储库](https://github.com/raghakot/keras-vis)提供
![](img/6f3615d0-e3ab-462c-8f9b-d15432b4cca6.png)
An example of saliency maps as seen on [https://github.com/raghakot/keras-vis](https://github.com/raghakot/keras-vis)
[显着性地图的示例](https://github.com/raghakot/keras-vis)
# 使用 VGGNet,ResNet,Inception 和 Xception 对图像进行分类
......@@ -309,7 +309,7 @@ An example of saliency maps as seen on [https://github.com/raghakot/keras-vis](h
![](img/86271cbd-cee5-4a2c-927e-a71caf930080.png)
An example of very deep network configurations as seen in [https://arxiv.org/pdf/1409.1556.pdf](https://arxiv.org/pdf/1409.1556.pdf)
[一个非常深的网络配置示例](https://arxiv.org/pdf/1409.1556.pdf)
在 2015 年,拥有 16 或 19 层就足以考虑网络的深度,而今天(2017 年)我们达到了数百层。 请注意,VGG 网络的训练速度非常慢,并且由于末端的深度和完全连接的层数,它们需要较大的重量空间。
......@@ -423,7 +423,7 @@ classify_image("images/parrot.jpg", "vgg16")
![](img/3c9d5a37-9b53-446b-b811-989ddf26167b.png)
An example of macaw as seen in [https://commons.wikimedia.org/wiki/File:Blue-and-Yellow-Macaw.jpg](https://commons.wikimedia.org/wiki/File:Blue-and-Yellow-Macaw.jpg)
[金刚鹦鹉的一个例子](https://commons.wikimedia.org/wiki/File:Blue-and-Yellow-Macaw.jpg)
```py
classify_image("images/parrot.jpg", "vgg19")
......@@ -473,7 +473,7 @@ classify_image("images/incredible-hulk-180.jpg", "resnet")
![](img/1877e5c6-dc66-428b-809c-786379f3631e.png)
An example of comic classification as seen in [https://comicvine.gamespot.com/the-incredible-hulk-180-and-the-wind-howls-wendigo/4000-14667/](https://comicvine.gamespot.com/the-incredible-hulk-180-and-the-wind-howls-wendigo/4000-14667/)
[如中所示的漫画分类示例](https://comicvine.gamespot.com/the-incredible-hulk-180-and-the-wind-howls-wendigo/4000-14667/)
```py
classify_image("images/cropped_panda.jpg", "resnet")
......@@ -846,7 +846,7 @@ An example of dilated network
![](img/f080e96c-b64c-4ac1-ba0a-18ecdcc2880e.png)
An example of NSynth architecture as seen in [https://magenta.tensorflow.org/nsynth](https://magenta.tensorflow.org/nsynth)
[如下所示的 NSynth 架构示例](https://magenta.tensorflow.org/nsynth)
# 做好准备
......@@ -1262,7 +1262,7 @@ print str(round(y_output[0,label]*100,2)).zfill(5), "% ", labelencoder.inverse_t
![](img/3c13ebd1-a831-48b9-9953-b4af71206181.png)
An example of C3D+RNN as seen in [https://imatge-upc.github.io/activitynet-2016-cvprw/](https://imatge-upc.github.io/activitynet-2016-cvprw/)
[C3D + RNN 的示例](https://imatge-upc.github.io/activitynet-2016-cvprw/)
# 怎么做...
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......@@ -444,7 +444,7 @@ An example of SyntaxNet as seen in https://research.googleblog.com/2016/05/annou
在本食谱中,我们将学习如何生成与威廉·莎士比亚(William Shakespeare)相似的文本。 关键思想很简单:我们将莎士比亚写的真实文本作为输入,并将其作为输入 RNN 的输入,该 RNN 将学习序列。 然后将这种学习用于生成新文本,该文本看起来像最伟大的作家用英语撰写的文本。
For the sake of simplicity, we will use the framework TFLearn ([http://tflearn.org/](http://tflearn.org/)), which runs on top of TensorFlow. This example is part of the standard distribution and it is available at [https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py) . The model developed one is an RNN character-level language model where the sequences considered are sequences of characters and not words.
为了简单起见,我们将使用框架 [TFLearn](http://tflearn.org/),它在 TensorFlow 上运行。 此示例是标准分发版的一部分,[可从以下位置获得](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py)。开发的模型是 RNN 字符级语言模型,其中考虑的序列是字符序列而不是单词序列。
# 怎么做...
......@@ -624,7 +624,9 @@ And the provest and stand,
在本食谱中,我们将学习如何使用 RNN 预测未来的比特币价值。 关键思想是,过去观察到的值的时间顺序可以很好地预测未来的值。 对于此食谱,我们将使用 MIT 许可下的[这个页面](https://github.com/guillaume-chevalier/seq2seq-signal-prediction)上提供的代码。 给定时间间隔的比特币值通过 API 从[这里](https://www.coindesk.com/api/)下载。 这是 API 文档的一部分:
*We offer historical data from our Bitcoin Price Index through the following endpoint:* *https://api.coindesk.com/v1/bpi/historical/close.json* *By default, this will return the previous 31 days' worth of data. This endpoint accepts the following optional parameters:* *?index=[USD/CNY]The index to return data for. Defaults to USD.* *?currency=<VALUE>The currency to return the data in, specified in ISO 4217 format. Defaults to USD.* *?start=<VALUE>&end=<VALUE> Allows data to be returned for a specific date range. Must be listed as a pair of start and end parameters, with dates supplied in the YYYY-MM-DD format, e.g. 2013-09-01 for September 1st, 2013.* *?for=yesterday Specifying this will return a single value for the previous day. Overrides the start/end parameter.* *Sample Request:* [https://api.coindesk.com/v1/bpi/historical/close.json?start=2013-09-01&end=2013-09-05](https://api.coindesk.com/v1/bpi/historical/close.json?start=2013-09-01&end=2013-09-05) *Sample JSON Response:* {"bpi":{"2013-09-01":128.2597,"2013-09-02":127.3648,"2013-09-03":127.5915,"2013-09-04":120.5738,"2013-09-05":120.5333},"disclaimer":"This data was produced from the CoinDesk Bitcoin Price Index. BPI value data returned as USD.","time":{"updated":"Sep 6, 2013 00:03:00 UTC","updatedISO":"2013-09-06T00:03:00+00:00"}}
```py
We offer historical data from our Bitcoin Price Index through the following endpoint: https://api.coindesk.com/v1/bpi/historical/close.json By default, this will return the previous 31 days' worth of data. This endpoint accepts the following optional parameters: ?index=[USD/CNY]The index to return data for. Defaults to USD. ?currency=<VALUE>The currency to return the data in, specified in ISO 4217 format. Defaults to USD. ?start=<VALUE>&end=<VALUE> Allows data to be returned for a specific date range. Must be listed as a pair of start and end parameters, with dates supplied in the YYYY-MM-DD format, e.g. 2013-09-01 for September 1st, 2013. ?for=yesterday Specifying this will return a single value for the previous day. Overrides the start/end parameter. Sample Request: https://api.coindesk.com/v1/bpi/historical/close.json?start=2013-09-01&end=2013-09-05 Sample JSON Response: {"bpi":{"2013-09-01":128.2597,"2013-09-02":127.3648,"2013-09-03":127.5915,"2013-09-04":120.5738,"2013-09-05":120.5333},"disclaimer":"This data was produced from the CoinDesk Bitcoin Price Index. BPI value data returned as USD.","time":{"updated":"Sep 6, 2013 00:03:00 UTC","updatedISO":"2013-09-06T00:03:00+00:00"}}
```
# 怎么做...
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