April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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394
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第十二章：用 TensorFlow 來自製模型和進行訓練
如果你設定優化法的
clipnorm
或
clipvalue
超參數
，
它會幫你處理這些事
。
如果你想要對
梯度套用任何其他轉換
，
只要在呼叫
apply_gradients()
方法之前做這件事即可
。
如果你對模型加入權重約束
（
例如在建立階層時設定
kernel_constraint
或
bias_
constraint
），
你要在執行
apply_gradients()
之後更新訓練迴圈來套用這些約束
：
for
variable
in
model.variables:
if
variable.constraint
is not
None:
variable.assign(variable.constraint(variable))
最重要的是
，
這個訓練迴圈不處理在訓練和測試期間有不同行為的階層
（
例如
BatchNormalization
或
Dropout
），
若要處理它們
，
你必須用
training=True
來呼叫模型
，
來讓模型將它傳播給需要它的每一個階層
。
如你所見
，
你必須把很多事情做對
，
而且很容易犯錯
。
但是從光明面看
，
你可以完全控制
局面
，
所以決定權在你
。
知道如何自訂模型的任何部分
14
以及訓練演算法之後
，
我們來瞭解如何使用
TensorFlow
的自動圖產生功能
：
它可以大幅提升自訂程式碼的速度
，
而且可以讓程式碼移植到
TensorFlow
支援的任何平台
（
見第
19
章
）。
TensorFlow
函式與圖
在
TensorFlow 1
中
，
圖是不可避免的
（
以及隨之而來的複雜性 ...
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