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1_685_688.mp3
真 是 可 喜 可 賀 可 喜 可 賀 這 樣 子
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25.070822
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23.909524
1_690_691.mp3
我 們 今 天 就 是 學 到 說
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13.604676
1_691_694.mp3
這 個 輝 夜 大 小 姐 這 個 動 畫 啊
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5.137287
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7.574755
1_694_696.mp3
它 真 是 一 個 了 不 起 的 動 畫
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25.212231
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24.091331
1_703_706.mp3
這 三 個 故 事 是 可 以 串 在 一 起 的
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43.210282
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43.270215
1_706_707.mp3
就 是 這 麼 回 事
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33.164486
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33.675638
1_707_709.mp3
所 以 我 們 對 機 器 說 一 句 話
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38.463401
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38.321607
1_709_710.mp3
問 它 說 輝 夜 是 誰
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39.796712
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39.796712
1_710_713.mp3
其 實 每 個 人 也 可 能 都 有 不 同 的 答 案
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12.865858
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15.950584
1_714_715.mp3
這 個 時 候 我 們 就 需 要
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21.949692
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22.684899
1_719_720.mp3
好 當 然 generative 的 model
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-0.01232
[]
null
1_720_722.mp3
其 中 一 個 非 常 知 名 的 呢
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9.46601
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9.38133
1_722_725.mp3
就 是 generative adversarial network
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18.150795
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27.088739
1_728_731.mp3
那 我 們 這 一 堂 課 呢 主 要 就 是 介 紹
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41.622397
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41.282968
1_731_733.mp3
generative adversarial network
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43.247133
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37.177599
1_734_736.mp3
好 那 介 紹 generative
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13.215299
[ 10.063263893127441 ]
10.063264
1_736_737.mp3
adversarial network 之 前 呢
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35.340529
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35.090441
1_737_738.mp3
我 們 就 遇 到 一 個 問 題 啊
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-3.65335
[]
null
1_738_741.mp3
就 是 gan 要 怎 麼 唸 呢
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40.761863
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40.396555
1_745_748.mp3
“gan gan gan”
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15.203419
1_748_751.mp3
好 就 ㄍㄢˋ 四 聲 啦 嘿 就 是 這 麼 回 事
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27.941392
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27.321996
1_751_755.mp3
好 那 這 個 ㄍㄢˋ 四 聲 呢 這 個 gan 呢
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42.535311
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42.139355
1_755_759.mp3
它 其 實 有 很 多 各 式 各 樣 的 變 形
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31.145361
1_759_761.mp3
你 可 以 在 網 路 上 呢 找 到
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7.551714
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7.862172
1_761_764.mp3
一 個 gan 的 動 物 園
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40.327849
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34.096301
1_764_766.mp3
找 到 一 個 gan 的 zoo
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23.892334
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23.111979
1_768_771.mp3
蒐 集 了 超 過 五 百 種 以 上 的 gan
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49.608291
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49.453133
1_772_774.mp3
那 你 知 道 每 次 有 人 發 明 了
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42.974593
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42.854229
1_774_775.mp3
一 個 新 的 gan 的 時 候
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20.527043
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19.77417
1_779_781.mp3
那 但 是 你 知 道 英 文 的 字 母 是 有 限 的 啊
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9.292252
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9.086581
1_793_796.mp3
甚 至 還 發 生 了 這 樣 子 的 狀 況
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48.79979
1_796_799.mp3
有 一 篇 paper 他 提 出 來 的 叫 做
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40.704942
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40.0778
1_799_801.mp3
“variational auto encoding gan”
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47.769896
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47.227596
1_805_808.mp3
但 是 作 者 呢 加 了 一 個 註 解 說
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39.75265
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39.161339
1_808_810.mp3
哎 呀 aegan 被 別 人 用 了 啦
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13.85752
1_810_810.mp3
所 有 的 英 文 字 母
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26.705206
1_810_812.mp3
看 起 來 都 被 別 人 用 了 啦
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20.825669
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17.540912
1_812_814.mp3
我 只 好 叫 做 α gan 這 樣 子
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29.072011
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27.255483
1_815_816.mp3
沒 有 錯 gan 呢 實 在 太 多 了
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15.427062
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14.357073
1_816_818.mp3
所 以 就 會 發 生 這 種 狀 況
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14.718162
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13.992645
1_819_822.mp3
誰 可 以 記 得 最 多 gan 的 名 字
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11.460546
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11.471678
1_822_823.mp3
然 後 從 a 唸 到 z 這 樣
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33.305554
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30.692454
1_825_827.mp3
好 那 我 們 現 在 等 一 下 要 舉 的 例 子 啊
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9.081978
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8.718853
1_827_830.mp3
就 是 要 讓 機 器 生 成 動 畫 人 物 的
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1_830_832.mp3
二 次 元 人 物 的 臉
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19.896892
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19.896804
1_837_839.mp3
什 麼 叫 unconditional generation 呢
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48.62437
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48.602505
1_844_845.mp3
generation 的 時 候
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23.220734
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23.689414
1_855_857.mp3
那 輸 入 的 這 個 z 是 什 麼 呢
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31.879827
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31.63131
1_857_859.mp3
我 們 等 一 下 都 假 設 z 呢
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17.733641
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17.867909
1_866_868.mp3
那 這 個 向 量 呢 通 常 會 是 一 個
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42.72744
1_870_872.mp3
它 的 維 度 其 實 是 你 自 訂 的 啦
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43.029813
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43.040506
1_873_876.mp3
那 通 常 你 就 訂 個 50 啦 100 啊
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35.296094
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39.564743
1_876_877.mp3
這 樣 子 的 大 小
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6.902376
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6.902376
1_880_881.mp3
好 你 從 這 邊 z 呢
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18.172466
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21.841721
1_881_883.mp3
你 從 這 個 normal distribution
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21.347541
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21.347541
1_883_885.mp3
裡 面 呢 sample 一 個 向 量 以 後
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12.27873
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11.762542
1_887_889.mp3
generator 就 給 你 一 個 對 應 的 輸 出
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34.752989
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34.825448
1_894_896.mp3
那 到 底 generator 要 輸 出
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12.93163
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12.939243
1_896_897.mp3
怎 麼 樣 的 東 西
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32.944049
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33.080861
1_897_900.mp3
才 會 變 成 一 個 二 次 元 人 物 的 人 臉 呢
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11.62518
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11.376547
1_907_909.mp3
所 以 generator 實 際 上 做 的 事 情
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[ 0.9620535373687744, 0.9614747166633606, 0.9493018388748169, 0.9252837300300598, 0.9147960543632507, 0.9177975654602051, 0.9125627279281616, 0.9003827571868896, 0.9145154356956482, 0.9041088819503784, 0.8878257870674133, 0.8997685313224792, 0.9071609377861023, 0.9033637642860413, 0.9119778871536255, 0.9215614199638367, 0.9224140644073486, 0.9208990335464478, 0.9199516177177429, 0.9253736734390259, 0.9302217960357666, 0.9268123507499695, 0.9268485903739929, 0.9162737727165222, 0.9152141809463501, 0.913014829158783, 0.9144676327705383, 0.9111124873161316, 0.9105257391929626, 0.911357581615448, 0.9148500561714172, 0.9154950976371765, 0.912826418876648, 0.9179134964942932, 0.9190062880516052, 0.9261049628257751, 0.9276123642921448, 0.9247637391090393, 0.9241799116134644, 0.9292317628860474, 0.9301109313964844, 0.9264459609985352, 0.924659252166748, 0.9233913421630859, 0.9273934364318848, 0.9235957860946655, 0.9234912991523743, 0.9259664416313171, 0.9225231409072876, 0.9146358966827393, 0.9180911779403687, 0.9151895046234131, 0.9153317213058472, 0.9141736626625061, 0.9171578884124756, 0.9165287613868713, 0.9131739735603333, 0.9114643931388855, 0.9111815094947815, 0.9115676283836365, 0.9067284464836121, 0.9108532071113586, 0.9067027568817139, 0.90869140625, 0.9023399949073792, 0.9014179706573486, 0.9023280739784241, 0.8981600403785706, 0.8905860781669617, 0.8743979334831238, 0.8855023384094238, 0.8795822262763977, 0.886526882648468, 0.9007686376571655, 0.9014105796813965, 0.8962746262550354, 0.8861611485481262, 0.8871432542800903, 0.8787500858306885, 0.8688064813613892, 0.8617151975631714, 0.8244479298591614, 0.8184146881103516, 0.8177968263626099, 0.8123984932899475, 0.8252368569374084, 0.8415053486824036, 0.8529748916625977, 0.8526819944381714, 0.8365057110786438, 0.8527706861495972, 0.8472161293029785, 0.8386863470077515, 0.8414329290390015, 0.837575376033783, 0.826603889465332, 0.8019818067550659, 0.7754600048065186, 0.7560063004493713, 0.7080116868019104, 0.661082923412323, 0.6809559464454651, 0.7260284423828125, 0.7245694994926453, 0.6877800822257996, 0.6536364555358887, 0.6400636434555054, 0.6160145998001099, 0.5584238171577454, 0.5328800678253174, 0.5744842290878296, 0.5990971326828003, 0.6054671406745911, 0.563478410243988, 0.5117238163948059, 0.453968346118927, 0.44566377997398376, 0.4512237012386322, 0.4022344648838043, 0.3977036774158478, 0.31063491106033325, 0.2516299784183502, 0.19963113963603973, 0.17229285836219788, 0.138912633061409, 0.08238384872674942, 0.047750718891620636 ]
23.600461
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22.377016
1_909_912.mp3
就 是 產 生 一 個 非 常 高 維 的 向 量
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26.984055
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27.96636
1_917_921.mp3
那 你 的 generator 輸 出 就 是 64x64x3
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18.333077
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21.690581
1_921_924.mp3
那 麼 長 的 向 量 把 那 個 向 量 整 理 一 下
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26.181965
1_924_927.mp3
就 變 成 一 張 二 次 元 人 物
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21.706675
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22.103902
1_927_929.mp3
這 個 就 是 generator 要 做 的 事 情
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29.284571
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26.332401
1_930_932.mp3
當 你 輸 入 的 向 量 不 同 的 時 候
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32.876597
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31.42656
1_935_936.mp3
所 以 你 從 這 個
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10.664917
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8.430583
1_936_937.mp3
normal distribution 裡 面
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2.184852
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2.097753
1_937_940.mp3
sample z 出 來 sample 到 不 同 的 z
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15.045911
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12.685147
1_940_942.mp3
那 你 輸 出 來 的 y 都 不 一 樣
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6.135334
[ 10.561477661132812, 3.1309893131256104, 0.5744400024414062, 0.33315277099609375, 0.909828245639801, 0.8147201538085938, 0.9966049790382385, 0.7578964829444885, 1.5541685819625854, 5.760368824005127, 2.67156982421875 ]
2.551383
1_942_944.mp3
那 我 們 希 望 說 不 管 你 這 邊 sample 到 什 麼 z
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38.640617
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38.820991
1_948_950.mp3
那 講 到 這 邊 可 能 有 同 學 會 問 說
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11.395941
[]
null
1_950_952.mp3
這 邊 為 什 麼 是
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10.009197
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10.009197
1_952_953.mp3
normal distribution 呢
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35.051086
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35.192525
1_961_963.mp3
distribution 之 間 的 差 異
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11.080044
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10.805106
1_963_965.mp3
可 能 並 沒 有 真 的 非 常 大
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33.848679
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32.953502
1_969_970.mp3
有 沒 有 差 異
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37.066225
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35.40628
1_971_973.mp3
夠 簡 單 的 distribution 就 行
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22.27655
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21.934124
1_973_976.mp3
因 為 你 的 generator 會 想 辦 法
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48.588895
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48.660627
1_976_977.mp3
把 這 個 簡 單 的 distribution
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37.634893
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36.596645
1_981_982.mp3
所 以 你 可 以 把 選 擇
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5.335924
[ 6.057891368865967, 7.034813404083252, 7.691238880157471, 8.185218811035156 ]
7.242291
1_982_984.mp3
distribution 這 件 事 情
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21.431078
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20.443086
1_984_986.mp3
交 給 你 的 generator 來 處 理
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23.429799
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29.150388
1_986_988.mp3
那 這 邊 我 們 在 等 一 下 的 討 論 裡 面
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46.987035
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47.096623
1_995_997.mp3
除 了 generator 以 外
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6.982511
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3.846677
1_997_999.mp3
我 們 要 多 訓 練 一 個 東 西
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34.302789
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33.898924
1_1003_1004.mp3
好 那 這 個 discriminator
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1.990276
[ 7.123650074005127, 6.82879638671875, 4.4281005859375 ]
6.126849
1_1004_1005.mp3
是 做 什 麼 的 呢
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20.591629
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20.975148
1_1005_1007.mp3
這 個 discriminator 它 的 作 用 是
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46.637996
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46.753762
1_1007_1010.mp3
它 會 拿 一 張 圖 片 作 為 輸 入
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35.185655
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34.993891
1_1010_1011.mp3
那 它 的 輸 出 是 什 麼 呢
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34.021668
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34.611319
1_1029_1031.mp3
這 個 scalar 越 大 就 代 表 說
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39.869244
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39.869244
1_1031_1033.mp3
現 在 輸 入 的 這 張 圖 片
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23.360017
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24.251773
1_1041_1043.mp3
那 就 輸 出 1 假 設 1 是 最 大 的 值 啦
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39.280419
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39.055415
1_1048_1050.mp3
這 個 不 知 道 在 畫 什 麼 就 輸 出 0.1
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48.879475
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47.940222
1_1052_1053.mp3
那 至 於 discriminator 的
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35.474395
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30.701266
1_1053_1055.mp3
neural network 的 架 構 啊
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25.447187
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23.273321
1_1055_1057.mp3
這 也 完 全 是 你 自 己 設 計 的
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47.240403
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46.662441
1_1057_1058.mp3
所 以 generator
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37.673448
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36.321628