vault backup: 2023-11-14 10:25:23

.obsidian/workspace.json

@@ -34,9 +34,21 @@
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+          },
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+            "type": "leaf",
+            "state": {
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+                "mode": "source",
+                "source": true
+              }
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@@ -65,7 +77,7 @@
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@@ -82,7 +94,8 @@
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@@ -102,7 +115,7 @@
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@@ -127,7 +140,7 @@
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@@ -155,12 +168,12 @@
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@@ -198,13 +211,16 @@
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+    "05. 資料收集/An example that use categorical_crossentropy and softmax.md",
+    "04. Programming/Machine Learning.md",
+    "00. Inbox/01. TODO.md",
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04. Programming/Machine Learning.md

@@ -1,11 +1,11 @@
 
 ## 問題分類
 
-|  問題類型   | 啟動函數 |                損失函數                |
+|    問題類型    | 啟動函數 |                                    損失函數                                    |                                範例                                |
-|:-----------:|:--------:|:--------------------------------------:|
+|:--------------:|:--------:|:------------------------------------------------------------------------------:|:------------------------------------------------------------------:|
-|  二元分類   | sigmoid  |   binary_crossentropy（二元交叉熵）    | 
+|    二元分類    | sigmoid  |                       binary_crossentropy（二元交叉熵）                        |                                                                    |
-|  單標籤多元分類   | softmax  | [[categorical_crossentropy]]（分類交叉熵）<br> sparse_categorical_crossentropy |
+| 單標籤多元分類 | softmax  | [[categorical_crossentropy]]（分類交叉熵）<br> sparse_categorical_crossentropy | 範例：[[An example that use categorical_crossentropy and softmax]] |
-| 多標籤分類  | sigmoid  |          binary_crossentropy           |
+|   多標籤分類   | sigmoid  |                              binary_crossentropy                               |                                                                    |
-|  回歸求值   |   None   |            mse（均方誤差）             |
+|    回歸求值    |   None   |                                mse（均方誤差）                                 |                                                                    |
-| 回歸求0~1值 | sigmoid  |        mse或binary_crossentropy        |
+|  回歸求0~1值   | sigmoid  |                            mse或binary_crossentropy                            |                                                                    |
 

04. Programming/categorical_crossentropy.md

@@ -1,2 +1,3 @@
 - 僅適用於 one-hot 編碼。
 - 如果輸出不是 one-hot，而是整數標籤，也就是直接輸出 0、1、2，而不是一個array（`[0, 0, 0, 1, 0]` 之類），那就需要 sparse_categorical_crossentropy。
+- 範例：[[An example that use categorical_crossentropy and softmax]]

05. 資料收集/An example that use categorical_crossentropy and softmax.md

@@ -0,0 +1,31 @@
+An example that use [[categorical_crossentropy]] and softmax
+
+```python
+dropRatio = 0.1
+
+model = Sequential()
+model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(self.DATA_LEN, 1)))
+model.add(Dropout(dropRatio))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
+model.add(Dropout(dropRatio))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
+model.add(Dropout(dropRatio))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Dropout(dropRatio))
+model.add(Conv1D(filters=256, kernel_size=3, activation='relu'))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Dropout(dropRatio))
+model.add(Flatten())
+model.add(Dropout(dropRatio))
+model.add(Dense(units=128, activation='relu'))
+model.add(Dropout(dropRatio))
+model.add(Dense(units=64, activation='relu'))
+model.add(Dropout(dropRatio))
+model.add(Dense(units=32, activation='relu'))
+model.add(Dense(units=len(self.DEVICE_LABEL), activation='softmax'))
+model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.01), metrics=['accuracy'])
+```