From 573de783d68e90bf7d7c6461e3f60da9571e5c78 Mon Sep 17 00:00:00 2001 From: Awin Huang Date: Tue, 14 Nov 2023 10:25:23 +0800 Subject: [PATCH] vault backup: 2023-11-14 10:25:23 --- .obsidian/workspace.json | 37 +++++++++++++------ 04. Programming/Machine Learning.md | 14 +++---- 04. Programming/categorical_crossentropy.md | 3 +- ...at use categorical_crossentropy and softmax.md | 31 ++++++++++++++++ 4 files changed, 65 insertions(+), 20 deletions(-) create mode 100644 05. 資料收集/An example that use categorical_crossentropy and softmax.md diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index a581526..e69ab8f 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -34,9 +34,21 @@ }, "pinned": true } + }, + { + "id": "26dbd5276842579d", + "type": "leaf", + "state": { + "type": "markdown", + "state": { + "file": "04. Programming/Machine Learning.md", + "mode": "source", + "source": true + } + } } ], - "currentTab": 1 + "currentTab": 2 } ], "direction": "vertical" @@ -65,7 +77,7 @@ "state": { "type": "search", "state": { - "query": "numpy ", + "query": "softma", "matchingCase": false, "explainSearch": false, "collapseAll": false, @@ -82,7 +94,8 @@ "state": {} } } - ] + ], + "currentTab": 1 } ], "direction": "horizontal", @@ -102,7 +115,7 @@ "state": { "type": "backlink", "state": { - "file": "00. Inbox/01. TODO.md", + "file": "04. Programming/Machine Learning.md", "collapseAll": false, "extraContext": false, "sortOrder": "alphabetical", @@ -127,7 +140,7 @@ "state": { "type": "outline", "state": { - "file": "00. Inbox/01. TODO.md" + "file": "04. Programming/Machine Learning.md" } } }, @@ -155,12 +168,12 @@ "state": { "type": "file-properties", "state": { - "file": "00. Inbox/01. TODO.md" + "file": "04. Programming/Machine Learning.md" } } } ], - "currentTab": 5 + "currentTab": 3 }, { "id": "ae4bf98badbfc7ee", @@ -198,13 +211,16 @@ "periodic-notes:Open today": false } }, - "active": "3b4577823cedf427", + "active": "26dbd5276842579d", "lastOpenFiles": [ + "04. Programming/categorical_crossentropy.md", + "05. 資料收集/An example that use categorical_crossentropy and softmax.md", + "04. Programming/Machine Learning.md", + "00. Inbox/01. TODO.md", "01. 個人/01. Daily/2023-11-13(週一).md", "05. 資料收集/讀書筆記/20231113 - 戰爭裡的世界史.md", "05. 資料收集/讀書筆記/20230801 - 蘇格拉底哲學特快車.md", "01. 個人/01. Daily/2023-11-12(週日).md", - "00. Inbox/01. TODO.md", "00. Inbox/除草劑.md", "05. 資料收集/Tool Setup/Software/Visual Studio Code.md", "01. 個人/01. Daily/2023-11-11(週六).md", @@ -224,10 +240,7 @@ "01. 個人/01. Daily/2023-10-26(週四).md", "未命名.canvas", "04. Programming/Python/matplotlib.md", - "01. 個人/01. Daily/2023/08/2023-08-01(週二).md", "01. 個人/01. Daily/2023/09", - "01. 個人/01. Daily/2023-10-24(週二).md", - "01. 個人/01. Daily/2023/09/2023-09-04(週一).md", "01. 個人/01. Daily/2023/08", "01. 個人/01. Daily/2023/05", "00. Inbox/My Mindmap.canvas", diff --git a/04. Programming/Machine Learning.md b/04. Programming/Machine Learning.md index 698eed6..01fefda 100644 --- a/04. Programming/Machine Learning.md +++ b/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 | -|:-----------:|:--------:|:--------------------------------------:| -| 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）
sparse_categorical_crossentropy | -| 多標籤分類 | sigmoid | binary_crossentropy | -| 回歸求值 | None | mse（均方誤差） | -| 回歸求0~1值 | sigmoid | mse或binary_crossentropy | +| 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） | | +| 單標籤多元分類 | softmax | [[categorical_crossentropy]]（分類交叉熵）
sparse_categorical_crossentropy | 範例：[[An example that use categorical_crossentropy and softmax]] | +| 多標籤分類 | sigmoid | binary_crossentropy | | +| 回歸求值 | None | mse（均方誤差） | | +| 回歸求0~1值 | sigmoid | mse或binary_crossentropy | | diff --git a/04. Programming/categorical_crossentropy.md b/04. Programming/categorical_crossentropy.md index e7526ac..8e20879 100644 --- a/04. Programming/categorical_crossentropy.md +++ b/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。 \ No newline at end of file +- 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]] \ No newline at end of file diff --git a/05. 資料收集/An example that use categorical_crossentropy and softmax.md b/05. 資料收集/An example that use categorical_crossentropy and softmax.md new file mode 100644 index 0000000..ec3aacc --- /dev/null +++ b/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']) +``` \ No newline at end of file