URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
(
    [query] => Array
        (
            [match] => Array
                (
                    [text] => Array
                        (
                            [query] => Linux 桌面玩家指南：15. 深度学习可以这样玩 - 京山游侠
                        )

                )

        )

    [highlight] => Array
        (
            [fields] => Array
                (
                    [text] => stdClass Object
                        (
                        )

                )

            [pre_tags] => #em#
            [post_tags] => #/em#
        )

    [size] => 8
    [from] => 0
)
</pre>RESPONSE:<pre>string(9226) "{"took":42,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":85.80619,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"44323","_score":85.80619,"_source":{"id":"44323","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a15. \u6df1\u5ea6\u5b66\u4e60\u53ef\u4ee5\u8fd9\u6837\u73a9 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1544891661"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：#em#15#/em#. #em#深#/em##em#度#/em##em#学#/em##em#习#/em##em#可#/em##em#以#/em##em#这#/em##em#样#/em##em#玩#/em# - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"30885","_score":56.761444,"_source":{"id":"30885","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a01. \u73a9\u8f6c Linux \u7cfb\u7edf\u7684\u65b9\u6cd5\u8bba - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1637691796"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：01. #em#玩#/em#转 #em#Linux#/em# 系统的方法论 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"33220","_score":53.059574,"_source":{"id":"33220","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a04. Linux \u684c\u9762\u7cfb\u7edf\u5b57\u4f53\u914d\u7f6e\u8981\u7565 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1637426174"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：04. #em#Linux#/em# #em#桌#/em##em#面#/em#系统字体配置要略 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"38622","_score":52.99518,"_source":{"id":"38622","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a10. \u6ca1\u6709 GUI \u7684\u65f6\u5019\u5e94\u8be5\u600e\u4e48\u73a9 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1637096405"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：10. 没有 GUI 的时候应该怎么#em#玩#/em# - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"32221","_score":51.877293,"_source":{"id":"32221","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a03. \u9488\u5bf9 Gnome 3 \u7684 Linux \u684c\u9762\u8fdb\u884c\u7f8e\u5316 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1639109858"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：03. 针对 Gnome 3 的 #em#Linux#/em# #em#桌#/em##em#面#/em#进行美化 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"35277","_score":48.616196,"_source":{"id":"35277","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a07. Linux \u4e2d\u7684 Qemu\u3001KVM\u3001VirtualBox\u3001Xen \u865a\u62df\u673a\u4f53\u9a8c - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1640661387"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：07. #em#Linux#/em# 中的 Qemu、KVM、VirtualBox、Xen 虚拟机体验 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"48127","_score":47.406013,"_source":{"id":"48127","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a16. \u4f7f\u7528 CUDA \u53d1\u6325\u663e\u5361\u7684\u8ba1\u7b97\u6027\u80fd - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1639553818"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：16. 使用 CUDA 发挥显卡的计算性能 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}},{"_index":"likecs_art_db","_type":"_doc","_id":"61534","_score":46.447037,"_source":{"id":"61534","text":"Linux \u684c\u9762\u73a9\u5bb6\u6307\u5357\uff1a20. \u628a Linux \u7cfb\u7edf\u88c5\u5165\u79fb\u52a8\u786c\u76d8\u6253\u5305\u5e26\u8d70 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1641074722"},"highlight":{"text":["#em#Linux#/em# #em#桌#/em##em#面#/em##em#玩#/em##em#家#/em##em#指#/em##em#南#/em#：20. 把 #em#Linux#/em# 系统装入移动硬盘打包带走 - #em#京#/em##em#山#/em##em#游#/em##em#侠#/em#"]}}]}}"
</pre>URL: http://127.0.0.1:9200/likecs_art_db/_search<br>REQUEST:<pre>Array
(
    [query] => Array
        (
            [match] => Array
                (
                    [text] => Array
                        (
                            [query] => Linux 桌面玩家指南：15. 深度学习可以这样玩 - 京山游侠
                        )

                )

        )

    [highlight] => Array
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            [fields] => Array
                (
                    [text] => stdClass Object
                        (
                        )

                )

            [pre_tags] => #em#
            [post_tags] => #/em#
        )

    [size] => 8
    [from] => 8
)
</pre>RESPONSE:<pre>string(8889) "{"took":50,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":10000,"relation":"gte"},"max_score":85.80619,"hits":[{"_index":"likecs_art_db","_type":"_doc","_id":"229933","_score":44.000744,"_source":{"id":"229933","text":"Linux \u7eaf\u5b57\u7b26\u754c\u9762\u7684\u73a9\u6cd5 - \u4eac\u5c71\u6e38\u4fa0","intro":"\u76ee\u5f55\n\nECharts\n\u5f02\u6b65\u52a0\u8f7d\n\n\n\nECharts\r\n\u6570\u636e\u53ef\u89c6\u5316\u5728\u8fc7\u53bb\u51e0\u5e74\u4e2d\u53d6\u5f97\u4e86\u5de8\u5927\u8fdb\u5c55\u3002\u5f00\u53d1\u4eba\u5458\u5bf9\u53ef\u89c6\u5316\u4ea7\u54c1\u7684\u671f\u671b\u4e0d\u518d\u662f\u7b80\u5355\u7684\u56fe\u8868\u521b\u5efa\u5de5\u5177\uff0c\u800c\u662f\u5728\u4ea4\u4e92\u3001\u6027\u80fd\u3001\u6570\u636e\u5904\u7406\u7b49\u65b9\u9762\u6709\u66f4\u9ad8\u7684\u8981\u6c42\u3002\r\nchart.setOption({\r\n    color: [\r\n        ","username":"youxia","tagsname":null,"tagsid":"","catesname":null,"catesid":"","createtime":"1629531520"},"highlight":{"text":["#em#Linux#/em# 纯字符界#em#面#/em#的#em#玩#/em#法 - 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            <footer class="tc"> youxia 
                <time class="post-date" datetime="2018-12-16">2018-12-16</time>
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<div id="likecs_post_body" class="blogpost-body cnblogs-markdown">
<blockquote>
<p><strong>特别说明：</strong>要在我的随笔后写评论的小伙伴们请注意了，我的博客开启了 MathJax 数学公式支持，MathJax 使用<code>$</code>标记数学公式的开始和结束。如果某条评论中出现了两个<code>$</code>，MathJax 会将两个<code>$</code>之间的内容按照数学公式进行排版，从而导致评论区格式混乱。如果大家的评论中用到了<code>$</code>，但是又不是为了使用数学公式，就请使用<code>\$</code>转义一下，谢谢。</p>
</blockquote>
<p>想从头阅读该系列吗？下面是传送门：</p>
<ul>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDAxLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：01. 玩转 Linux 系统的方法论</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDAyLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：02. 以最简洁的方式打造实用的 Vim 环境</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDAzLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：03. 针对 Gnome 3 的 Linux 桌面进行美化</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA0Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：04. Linux 桌面系统字体配置要略</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA1Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：05. 发博客必备的图片处理和视频录制神器</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA2Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：06. 优雅地使用命令行及 Bash 脚本编程语言中的美学与哲学</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA3Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：07. Linux 中的 Qemu、KVM、VirtualBox、Xen 虚拟机体验</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA4Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：08. 使用 GCC 和 GNU Binutils 编写能在 x86 实模式运行的 16 位代码</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDA5Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：09. X Window 的奥秘</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDEwLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：10. 没有 GUI 的时候应该怎么玩</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDExLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：11. 在同一个硬盘上安装多个 Linux 发行版以及为 Linux 安装 Nvidia 显卡驱动</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDEyLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：12. 优秀的文本化编辑思想大碰撞（Markdown、LaTeX、MathJax）</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDEzLmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：13. 使用 Git 及其 和 Eclipse 的集成</a></li>
<li><a href="/default/index/url?u=aHR0cHM6Ly93d3cuY25ibG9ncy5jb20veW91eGlhL3AvTGludXhEZXNrdG9wMDE0Lmh0bWw%3D" target="_blank" rel="noopener">Linux 桌面玩家指南：14. 数值计算和符号计算</a></li>
</ul>
<h3 id="前言">前言</h3>
<p>这一篇相当于是深度学习方面的一个 Hello World 吧。深度学习目前大火，吸引了无数人。但是很多时候，想了解深度学习的人总觉得云山雾罩，怎么也看不明白。如果要是有一个能运行的简单的深度学习的例子该多好啊。我这里就来玩一玩深度学习，来一个让大家摸得着、看得见的例子，揭开深度学习的神秘面纱。语言方面，当然是选择对科学计算极度友好的 Python 啦。我当然不会从头撸代码，肯定会使用现成的库的啦。目前最流行的深度学习框架是 Keras，在 Keras 中，它又使用了大名鼎鼎的 TensorFlow 作为后端。在 Python 中，安装这几个库真的是太方便了。最后说一句，我用的是 Python3。</p>
<p>上一篇浮光掠影地讲了一下科学计算，并探讨了一下适合数值计算的语言需要什么样的特色。非常幸运，Python 正好具有这样的特色，准确地说，应该是 Python 中的 numpy 库正好具有这样的特色。上一篇的内容略有过时，比如 IPython Notebook，目前就已经改成 Jupyter Notebook 了。但是在这一篇中，我就不用什么 Notebook 了，我用 PyCharm。</p>
<p>JetBrains 大家都知道啦，JetBrains 全家桶是广大程序员的福利啦。做科学计算，使用 PyCharm 的 Community 版就足够了，没必要下载 Professional 版啦，我更加是不鼓励大家用破解版的啦。</p>
<h3 id="环境安装">环境安装</h3>
<p>到 JetBrains 的官网下载最新的 PyCharm 2018.3，正如前面所说，Community 版就够了。解压，运行之，用它创建一个 Python 项目，选择使用虚拟环境，如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjAyMjI1MDUxNTA5LTExNDI0MDc0MjkucG5n" alt="" loading="lazy"></p>
<p>使用虚拟环境的好处，是可以为这个项目单独安装依赖的库，不用担心为了学一个什么东西而把系统中的 Python 搞得乱七八糟。直接打开 PyCharm 中的 Terminal，就可以进入这个项目的虚拟环境，在这个 Terminal 中运行的命令，默认就在这个项目的虚拟环境中执行。我们可以在这个 Terminal 中运行<code>pip3 install</code>命令安装所有需要的库。在 PyCharm 中使用<code>pip3 install</code>命令时，有一个令人头痛的问题，那就是从国外的源下载的速度太慢，我们可以替换成国内的源。我太懒，都是使用临时替换，只需要添加<code>-i 源地址</code>参数就可以了。例如，要安装 numpy，并且选择从阿里云下载，就使用<code>pip3 install numpy -i https://mirrors.aliyun.com/pypi/simple/</code>命令。如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjAyMjMwMjUzODUwLTE2NjcyMjcwNDcucG5n" alt="" loading="lazy"></p>
<p>常用的国内源有：</p>
<ul>
<li>清华：<a href="https://pypi.tuna.tsinghua.edu.cn/simple" target="_blank" rel="nofollow">https://pypi.tuna.tsinghua.edu.cn/simple</a>
</li>
<li>阿里云：<a href="https://mirrors.aliyun.com/pypi/simple/" target="_blank" rel="nofollow">https://mirrors.aliyun.com/pypi/simple/</a>
</li>
<li>中国科技大学 <a href="https://pypi.mirrors.ustc.edu.cn/simple/" target="_blank" rel="nofollow">https://pypi.mirrors.ustc.edu.cn/simple/</a>
</li>
<li>华中理工大学：<a href="http://pypi.hustunique.com/" target="_blank" rel="nofollow">http://pypi.hustunique.com/</a>
</li>
<li>山东理工大学：<a href="http://pypi.sdutlinux.org/" target="_blank" rel="nofollow">http://pypi.sdutlinux.org/</a>
</li>
<li>豆瓣：<a href="http://pypi.douban.com/simple/" target="_blank" rel="nofollow">http://pypi.douban.com/simple/</a>
</li>
</ul>
<p>新版 Ubuntu 要求使用 https 源，要注意。</p>
<p>在这里，我们需要安装 numpy、keras、tensorflow、matplotlib 库，numpy 用来操作向量和矩阵，matplotlib 用来画图。为了能够读取和预处理图片，我还需要使用 PIL，可惜 PIL 不支持 Python 3，不过没关系，使用 Pillow 就好了。安装这几个库只需要如下几个命令：</p>
<pre><code class="language-bash">pip3 install numpy -i https://mirrors.aliyun.com/pypi/simple/
pip3 install tensorflow -i https://mirrors.aliyun.com/pypi/simple/
pip3 install keras -i https://mirrors.aliyun.com/pypi/simple/
pip3 install matplotlib -i https://mirrors.aliyun.com/pypi/simple/
pip3 install Pillow -i https://mirrors.aliyun.com/pypi/simple/
</code></pre>
<p>这些库都直接安装到了我们的项目内，不会和系统中的 Python 起冲突。如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjAyMjMxMTUxNjI3LTE2ODk0Mzc5Ny5wbmc%3D" alt="" loading="lazy"></p>
<p>PyCharm 最大的优势当然是它的自动代码提示了。不管是写一个<code>.py</code>文件，还是直接使用 PyCharm 中的 Python Console，就是有非常好的代码提示的。Python Console 还有一个非常棒的功能，就是可以观察每一个变量的值，真的是太方便了。如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjAyMjM0OTQxMTEwLTE2MDYzNjkyNzMucG5n" alt="" loading="lazy"></p>
<p>而在系统的终端中直接运行 Python3，我们是得不到这么好的辅助功能的。如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjAyMjM1MDQ0NDI4LTIwMDEyNDMwNzIucG5n" alt="" loading="lazy"></p>
<h3 id="最简单的深度学习示例">最简单的深度学习示例</h3>
<p>首先，我们看一个最简单的深度学习示例，就是训练一个能够识别手写数字的密集连接神经网络。在这里，我们需要一个用于训练和测试的数据集，而这个数据集就是 MNIST 数据集，该数据集包含 60000 张用于训练的 28×28 像素的手写数字图片，以及 10000 张用于训练的 28×28 像素的手写数字图片。Keras 能够自动下载该数据集。训练这个神经网络的代码如下：</p>
<pre><code class="language-python">from keras.datasets import mnist
from keras import models
from keras import layers
from keras.utils import to_categorical

(train_images, train_labels),(test_images, test_labels) = mnist.load_data()

network = models.Sequential()
network.add(layers.Dense(512, activation=\'relu\', input_shape=(28*28,)))
network.add(layers.Dense(10, activation=\'softmax\'))

network.compile(optimizer=\'rmsprop\', loss=\'categorical_crossentropy\', metrics=[\'accuracy\'])

train_images = train_images.reshape((60000, 28*28))
train_images = train_images.astype(\'float32\') / 255

test_images = test_images.reshape(10000, 28*28)
test_images = test_images.astype(\'float32\') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network.fit(train_images, train_labels, epochs=5, batch_size=128)
</code></pre>
<p>把以上这几行代码逐行输入到 PyCharm 的 Python 控制台，就可以看到效果了。当输入到<code>(train_images, train_labels),(test_images, test_labels) = mnist.load_data()</code>这一行时，Keras 就会自动下载 MNIST 数据集，可惜的是，由于下载地址被墙的原因，经常会出现下载失败的情况。不过出现下载失败也不用着急，用搜索引擎找一下，很容易找到这个数据集的文件：<code>mnist.npz</code>文件，大小才 11.5M，下载很快的。把下载的这个文件放到<code>~/.keras/datasets</code>目录中即可。</p>
<p>以上所有的代码都输入完成后，就开始了训练，在这个例子中，训练速度非常快，每个周期 4 秒多就完成了，总共 5 个周期。如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjEzMjMwOTQyMjM4LTE1MjQwNzAwMjgucG5n" alt="" loading="lazy"></p>
<p>可以看到，这个神经网络达到了 98.9% 的精度。下面，我自己手写一个数字测试一下。先打开 Inkscape，自己随便写一个数字，如下图：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjEzMjMxMjI3MDc4LTE2ODMxNzY2NzQucG5n" alt="" loading="lazy"></p>
<p>保存为<code>~/test.png</code>。然后，用下面的代码把该图片读入内存，并更改大小为 28×28 像素，最后转化为灰度图像。这些操作都使用 PIL 完成。PIL 操作图像那是相当的方便。</p>
<pre><code class="language-python">from PIL import Image
image = Image.open(\'/home/youxia/test.png\').resize((28,28)).convert(\'L\')
</code></pre>
<p>然后，将该图像转化为 numpy 的数组，并将其中的数据处理为 0 到 1 之间的浮点数。</p>
<pre><code class="language-python">import numpy as np
im = np.array(image)
im = 255 - im
im = im.astype(\'float32\') / 255.0
</code></pre>
<p>可以使用 matplotlib 查看一下该图像，代码如下：</p>
<pre><code class="language-python">import matplotlib.pyplot as plt
plt.imshow(im, cmap=plt.cm.binary)
</code></pre>
<p>这是我们处理好之后的图像是下面这个效果，和 MNIST 自带的图像基本基本一致:<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjEzMjMxODI0MjM0LTcyMjI2NzYwMC5wbmc%3D" alt="" loading="lazy"></p>
<p>下面使用我们前面训练的神经网络来识别该图片，使用 predict 函数即可。识别时，需要将图像数据的 shape 更改为 (1,784):</p>
<pre><code class="language-python">network.predict(im.reshape(1,784))
</code></pre>
<p>返回的值是一个包含是个数字的列表，代表该图片可能为数字 0-9 的概率，可以看到，其中第 6 项，也就是为数字 5 的概率最大，接近于 1:</p>
<pre><code class="language-nohighlight">array([[2.19245143e-11, 7.56166017e-13, 2.06190620e-10, 3.12406366e-04,
        1.12510295e-13, 9.99686599e-01, 8.85503199e-11, 2.70172443e-11,
        2.85677032e-07, 7.55779013e-07]], dtype=float32)
</code></pre>
<p>如果不想自己用眼睛去判断哪个概率值最大，可以使用 numpy 的 argmax 函数，如下：</p>
<pre><code class="language-python">np.argmax(network.predict(im.reshape(1,784)))
</code></pre>
<p>返回结果为几，就说明识别出的数字为几，如下：</p>
<pre><code class="language-nohighlight">5
</code></pre>
<h3 id="从密集连接神经网络到卷积神经网络">从密集连接神经网络到卷积神经网络</h3>
<p>前面的例子使用的是密集连接神经网络，每一幅图像都转化为一个以为向量进行处理。在对图像进行深度学习时，最常用的方法是二维卷积神经网络。卷积神经网络的优点一是学到的模式具有平移不变性，在图像某一个区域学习到的模式，如果模式出现在新位置，它仍然能够识别；二是卷积神经网络可以学到模式的空间层次结构。</p>
<p>在 Keras 中，对卷积神经网络也提供了非常强大的支持。上一节的代码，只需要进行简单的修改，就可以构建一个二维卷积神经网络，代码如下：</p>
<pre><code class="language-python">from keras import layers
from keras import models
from keras.datasets import mnist
from keras.utils import to_categorical


model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation=\'relu\', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D(2, 2))
model.add(layers.Conv2D(64, (3, 3), activation=\'relu\'))
model.add(layers.MaxPooling2D(2, 2))
model.add(layers.Conv2D(64, (3, 3), activation=\'relu\'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation=\'relu\'))
model.add(layers.Dense(10, activation=\'softmax\'))

model.compile(optimizer=\'rmsprop\', loss=\'categorical_crossentropy\', metrics=[\'accuracy\'])

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype(\'float32\') / 255
test_images = test_images.reshape(10000, 28, 28, 1)
test_images = test_images.astype(\'float32\') / 255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.fit(train_images, train_labels, epochs=5, batch_size=64)
</code></pre>
<p>主要的改变体现在以下几个方面：一是使用 Conv2D 层、MaxPooling 层和 Flatten 层修改了深度学习框架的结构，二是修改了输入数组的形状。除此之外，该深度学习框架使用的激活函数、损失函数都和前面是一样的。训练该网络，发现它比前面的网络训练起来要慢一些，但是达到了 99.3%的准确度。</p>
<p>使用如下代码进行验证：</p>
<pre><code class="language-python">import numpy as np
from PIL import Image

im = np.array(Image.open(\'/home/youxia/test.png\').resize((28,28)).convert(\'L\'))
im = 255 - im
im = im.astype(\'float32\') / 255.0

print(np.argmax(model.predict(im.reshape(1, 28, 28, 1))))
</code></pre>
<h3 id="关于深度学习的理论和实战">关于深度学习的理论和实战</h3>
<p>深度学习的理论相对来说比较难，所以我这篇随笔里面就没有怎么介绍。如果想全面了解深度学习的理论，可以阅读这本“圣经”：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjE1MTAwNDM2MzQ3LTkzMzc2NzU3MS5qcGc%3D" alt="" loading="lazy"></p>
<p>但是，光有理论而无实践，这么枯燥的知识是学不下去的。在实战方面，我觉得这本书不错：<br><img src="/default/index/img?u=aHR0cHM6Ly9pbWcyMDE4LmNuYmxvZ3MuY29tL2Jsb2cvMTY1NzYvMjAxODEyLzE2NTc2LTIwMTgxMjE1MTAwNzIxNzYyLTE1MDA0ODk5NjUuanBn" alt="" loading="lazy"></p>
<p>我这里的内容，就是参考了这本书。最后，再次对 Python 点赞，用 Python 写科学计算的代码，真的是太舒服了。</p>
<h3 id="版权申明">版权申明</h3>
<p>该随笔由京山游侠在2018年12月16日发布于博客园，引用请注明出处，转载或出版请联系博主。QQ邮箱：1841079@qq.com</p>

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