mirror of
https://github.com/frankwxu/AI4DigitalForensics.git
synced 2026-02-20 13:40:40 +00:00
add a few lectures
This commit is contained in:
Frank Xu
@@ -1,2 +1,4 @@
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pip install ipywidgets
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pip install scikit-learn
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pip install scikit-learn
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pip install ultralytics
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pip install ultralytics opencv-python
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@@ -19,7 +19,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 62,
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"id": "323b62e8",
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"metadata": {},
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"outputs": [
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@@ -29,7 +29,7 @@
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"6"
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]
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},
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"execution_count": 2,
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"execution_count": 62,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -40,7 +40,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 63,
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"id": "6cdd612e",
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"metadata": {},
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"outputs": [
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@@ -50,7 +50,7 @@
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"8"
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]
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},
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"execution_count": 3,
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"execution_count": 63,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -61,7 +61,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 64,
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"id": "5e7c9a36",
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"metadata": {},
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"outputs": [
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@@ -71,7 +71,7 @@
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"1"
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]
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},
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"execution_count": 4,
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"execution_count": 64,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -82,7 +82,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 65,
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"id": "c497160d",
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"metadata": {},
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"outputs": [
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@@ -101,7 +101,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 66,
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"id": "e81329e5",
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"metadata": {},
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"outputs": [
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@@ -111,7 +111,7 @@
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"[1, 2, 3]"
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]
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},
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"execution_count": 6,
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"execution_count": 66,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -123,7 +123,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 67,
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"id": "0f886f3a",
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"metadata": {},
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"outputs": [
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@@ -133,7 +133,7 @@
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"1"
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]
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},
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"execution_count": 7,
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"execution_count": 67,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -144,7 +144,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 68,
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"id": "bd694c5c",
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"metadata": {},
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"outputs": [
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@@ -154,7 +154,7 @@
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"[1, 2, 3, 4]"
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]
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},
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"execution_count": 8,
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"execution_count": 68,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -166,7 +166,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 69,
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"id": "f47c854d",
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"metadata": {},
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"outputs": [
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@@ -176,7 +176,7 @@
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"4"
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]
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},
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"execution_count": 9,
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"execution_count": 69,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -187,7 +187,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 70,
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"id": "e2d7b456",
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"metadata": {},
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"outputs": [
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@@ -197,7 +197,7 @@
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"(1, 2, 3)"
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]
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},
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"execution_count": 10,
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"execution_count": 70,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -209,7 +209,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 71,
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"id": "4e37998a",
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"metadata": {},
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"outputs": [
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@@ -219,7 +219,7 @@
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"1"
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]
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},
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"execution_count": 11,
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"execution_count": 71,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -238,7 +238,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 72,
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"id": "d9c0f925",
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"metadata": {},
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"outputs": [
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@@ -248,7 +248,7 @@
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"[1, 4, 9]"
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]
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},
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"execution_count": 1,
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"execution_count": 72,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -259,7 +259,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 73,
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"id": "799c1063",
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"metadata": {},
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"outputs": [
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@@ -269,7 +269,7 @@
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"[1, 4, 9, 16, 25]"
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]
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},
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"execution_count": 4,
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -282,7 +282,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 74,
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"id": "7a9d00ab",
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"metadata": {},
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"outputs": [
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@@ -292,7 +292,7 @@
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"[0, 2, 4, 6, 8]"
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]
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},
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"execution_count": 6,
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -305,7 +305,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 75,
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"id": "0b3a893b",
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"metadata": {},
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"outputs": [
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@@ -315,7 +315,7 @@
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"[1, 2]"
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]
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},
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"execution_count": 8,
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"execution_count": 75,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -337,7 +337,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 76,
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"id": "0df3abc9",
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"metadata": {},
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"outputs": [],
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@@ -347,7 +347,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 77,
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"id": "2a7a1daf",
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"metadata": {},
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"outputs": [
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@@ -357,7 +357,7 @@
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"array([1, 2, 3, 4, 5, 6])"
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]
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},
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"execution_count": 13,
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"execution_count": 77,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -369,7 +369,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 78,
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"id": "1f8e61c2",
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"metadata": {},
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"outputs": [
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@@ -379,7 +379,7 @@
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"np.int64(1)"
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]
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},
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"execution_count": 14,
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"execution_count": 78,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -390,7 +390,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"id": "61e20947",
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"metadata": {},
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"outputs": [],
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@@ -400,7 +400,7 @@
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},
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{
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"cell_type": "code",
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"id": "18251de4",
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"metadata": {},
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"outputs": [
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@@ -410,7 +410,7 @@
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"np.float32(2.0)"
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]
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},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"id": "11f35a16",
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"metadata": {},
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"outputs": [
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"array([1, 2, 3])"
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]
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},
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"metadata": {},
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},
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"array([4, 5, 6])"
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]
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},
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},
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" [ 9, 10, 11, 12]])"
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]
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},
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},
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"np.int64(8)"
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]
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},
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"metadata": {},
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}
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"a[1, 3]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3d2d0b6f",
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"metadata": {},
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"source": [
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"The function calculates the step size as:\n",
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"\n",
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"step = (stop - start) / (num - 1) = (10 - (-10)) / (11 - 1) = 20/10 ≈ 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"id": "53d75c62",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([-10., -8., -6., -4., -2., 0., 2., 4., 6., 8., 10.])"
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]
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},
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"execution_count": 85,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Range of z values\n",
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"# np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)\n",
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"np.linspace(-10, 10, 11)"
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]
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},
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{
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"cell_type": "markdown",
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},
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},
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"True"
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"outputs": [
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]
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"outputs": [
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@@ -738,7 +771,7 @@
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"dtype('int64')"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"execution_count": 93,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -750,7 +783,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 94,
|
||||
"id": "a043cd1c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -760,7 +793,7 @@
|
||||
"dtype('float32')"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"execution_count": 94,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -780,7 +813,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 95,
|
||||
"id": "75b22e31",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -790,7 +823,7 @@
|
||||
"array([0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"execution_count": 95,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -801,7 +834,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 96,
|
||||
"id": "4b542c1f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -811,7 +844,7 @@
|
||||
"array([1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -822,7 +855,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 97,
|
||||
"id": "c22cdd54",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -832,7 +865,7 @@
|
||||
"array([0, 1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"execution_count": 97,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -843,7 +876,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 98,
|
||||
"id": "367eacd6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -853,7 +886,7 @@
|
||||
"array([2, 4, 6, 8])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"execution_count": 98,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -864,19 +897,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"execution_count": 99,
|
||||
"id": "0fa059e6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0.01135293, 0.54482248, 0.33628695, 0.81612888],\n",
|
||||
" [0.00168462, 0.97332481, 0.86424808, 0.01566007],\n",
|
||||
" [0.32076747, 0.3997841 , 0.24520416, 0.00826635]])"
|
||||
"array([[0.84849457, 0.2408882 , 0.26897315, 0.88789736],\n",
|
||||
" [0.17325996, 0.06966163, 0.81198917, 0.24941808],\n",
|
||||
" [0.70722305, 0.64643472, 0.07015388, 0.19063198]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"execution_count": 99,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -887,19 +920,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 100,
|
||||
"id": "eae03b14",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[6, 6, 4, 5],\n",
|
||||
" [8, 4, 1, 3],\n",
|
||||
" [3, 4, 9, 6]], dtype=int32)"
|
||||
"array([[ 1, 1, 10, 1],\n",
|
||||
" [ 8, 10, 1, 1],\n",
|
||||
" [ 8, 1, 4, 8]], dtype=int32)"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"execution_count": 100,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -910,18 +943,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 101,
|
||||
"id": "86c88e48",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1.61548569, -1.27751798, 2.23823677, 1.87533372],\n",
|
||||
" [ 1.88733913, 0.59567694, -0.74642328, 0.29124792]])"
|
||||
"array([[-0.06960131, 0.94400631, -0.05980746, -0.81806564],\n",
|
||||
" [-0.06661582, -0.71274669, 0.9149406 , 0.63540989]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"execution_count": 101,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -944,7 +977,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"execution_count": 102,
|
||||
"id": "b26242be",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -954,7 +987,7 @@
|
||||
"(6,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"execution_count": 102,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -966,7 +999,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"execution_count": 103,
|
||||
"id": "18db087b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -976,7 +1009,7 @@
|
||||
"(1, 6)"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"execution_count": 103,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -988,7 +1021,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"execution_count": 104,
|
||||
"id": "f213bce6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -998,7 +1031,7 @@
|
||||
"array([[1, 2, 3, 4, 5, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"execution_count": 104,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1009,7 +1042,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"execution_count": 105,
|
||||
"id": "25c7b822",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1024,7 +1057,7 @@
|
||||
" [6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"execution_count": 105,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1035,7 +1068,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"execution_count": 106,
|
||||
"id": "bbd43b79",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1046,7 +1079,7 @@
|
||||
" [4, 5, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"execution_count": 106,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1071,7 +1104,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"execution_count": 107,
|
||||
"id": "9af72192",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1082,7 +1115,7 @@
|
||||
" [4, 5]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"execution_count": 107,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1098,7 +1131,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": 108,
|
||||
"id": "4216c512",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1109,7 +1142,7 @@
|
||||
" [ 4, 14]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 43,
|
||||
"execution_count": 108,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1124,7 +1157,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 109,
|
||||
"id": "0eec244e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1136,7 +1169,7 @@
|
||||
" [6, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"execution_count": 109,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1152,7 +1185,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 110,
|
||||
"id": "bb8d128a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1162,7 +1195,7 @@
|
||||
"array([1.6, 3.2])"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"execution_count": 110,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1179,7 +1212,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 111,
|
||||
"id": "af55cce3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1191,7 +1224,7 @@
|
||||
" [10, 20, 30]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1244,7 +1277,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 112,
|
||||
"id": "0194c4b4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1254,7 +1287,7 @@
|
||||
"np.int64(1)"
|
||||
]
|
||||
},
|
||||
"execution_count": 47,
|
||||
"execution_count": 112,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1267,7 +1300,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 113,
|
||||
"id": "5b201131",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1277,7 +1310,7 @@
|
||||
"6"
|
||||
]
|
||||
},
|
||||
"execution_count": 48,
|
||||
"execution_count": 113,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1290,7 +1323,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 114,
|
||||
"id": "e73fbd22",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1300,7 +1333,7 @@
|
||||
"array([5, 7])"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"execution_count": 114,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1314,7 +1347,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 115,
|
||||
"id": "01229cfb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -1326,7 +1359,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 116,
|
||||
"id": "fd882d6c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1336,7 +1369,7 @@
|
||||
"np.float64(2.5)"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"execution_count": 116,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1348,7 +1381,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 117,
|
||||
"id": "ba88c2f7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1358,7 +1391,7 @@
|
||||
"array([2., 3.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 52,
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1370,7 +1403,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 118,
|
||||
"id": "b49ff53c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1380,7 +1413,7 @@
|
||||
"array([1.5, 3.5])"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"execution_count": 118,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1392,7 +1425,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 119,
|
||||
"id": "fa48f9a5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1402,7 +1435,7 @@
|
||||
"array([1.5, 3.5])"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"execution_count": 119,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1415,7 +1448,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 120,
|
||||
"id": "3ee7e4c9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -1440,7 +1473,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 121,
|
||||
"id": "6d37c412",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1476,7 +1509,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 122,
|
||||
"id": "b0a7a95f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1510,7 +1543,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 123,
|
||||
"id": "713cbe61",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1553,7 +1586,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 124,
|
||||
"id": "0ef952bd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1601,7 +1634,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 125,
|
||||
"id": "5afe68f8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1634,7 +1667,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 126,
|
||||
"id": "a1c381ad",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1670,7 +1703,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 127,
|
||||
"id": "5085fc72",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1710,7 +1743,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 128,
|
||||
"id": "37870aa6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
"id": "41d7e9ff",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### PyTorch Fundamentals Part A\n",
|
||||
"## PyTorch Fundamentals Part A\n",
|
||||
"\n",
|
||||
"- A PyTorch tensor is a multi-dimensional array (0D to nD) that contains elements of a single data type (e.g., integers, floats). \n",
|
||||
"- Tensors are used to represent scalars, vectors, matrices, or higher-dimensional data and are optimized for mathematical operations, automatic differentiation, and GPU computation"
|
||||
@@ -13,7 +13,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 48,
|
||||
"id": "739c5173",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -23,7 +23,7 @@
|
||||
"'2.6.0+cu126'"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -38,12 +38,12 @@
|
||||
"id": "75acf7d8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Multi-dimensional"
|
||||
"### Multi-dimensional"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 49,
|
||||
"id": "0e82be1e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -53,7 +53,7 @@
|
||||
"tensor(5)"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -66,7 +66,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 50,
|
||||
"id": "7c239759",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -76,7 +76,7 @@
|
||||
"0"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -87,7 +87,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 51,
|
||||
"id": "d176548d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -97,7 +97,7 @@
|
||||
"torch.Size([])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -108,7 +108,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 52,
|
||||
"id": "07e03145",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -118,7 +118,7 @@
|
||||
"5"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -129,7 +129,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 53,
|
||||
"id": "41fcc46e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -139,7 +139,7 @@
|
||||
"tensor([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -152,7 +152,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 54,
|
||||
"id": "f9894c37",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -162,7 +162,7 @@
|
||||
"1"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -173,7 +173,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 55,
|
||||
"id": "7dc166eb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -183,7 +183,7 @@
|
||||
"torch.Size([3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -195,7 +195,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 56,
|
||||
"id": "2581817b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -206,7 +206,7 @@
|
||||
" [ 9, 10]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -220,7 +220,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 57,
|
||||
"id": "46961042",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -230,7 +230,7 @@
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -241,7 +241,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 58,
|
||||
"id": "9669fda8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -251,7 +251,7 @@
|
||||
"torch.Size([2, 2])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -262,7 +262,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 59,
|
||||
"id": "15297945",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -274,7 +274,7 @@
|
||||
" [2, 4, 5]]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -289,7 +289,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 60,
|
||||
"id": "5bbed071",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -299,7 +299,7 @@
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -310,7 +310,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 61,
|
||||
"id": "483d25c7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -320,7 +320,7 @@
|
||||
"torch.Size([1, 3, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -331,7 +331,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 62,
|
||||
"id": "c4e76ef2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -341,7 +341,7 @@
|
||||
"torch.Size([1, 3, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -352,7 +352,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"execution_count": 63,
|
||||
"id": "b56abf50",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -364,7 +364,7 @@
|
||||
" [6, 9]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -376,7 +376,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 64,
|
||||
"id": "cdd39ae8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -391,7 +391,7 @@
|
||||
" [9]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -403,7 +403,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 65,
|
||||
"id": "adf1ab41",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -415,7 +415,7 @@
|
||||
" [2., 4., 5.]]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -430,7 +430,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 66,
|
||||
"id": "a368079f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -440,7 +440,7 @@
|
||||
"torch.float32"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -451,20 +451,20 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 67,
|
||||
"id": "4d00ea95",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(tensor([[0.7310, 0.5572],\n",
|
||||
" [0.9469, 0.2378]]),\n",
|
||||
" tensor([[0.2700, 0.9798],\n",
|
||||
" [0.4980, 0.8848]]))"
|
||||
"(tensor([[0.6636, 0.4190],\n",
|
||||
" [0.4294, 0.9632]]),\n",
|
||||
" tensor([[0.0473, 0.9045],\n",
|
||||
" [0.2971, 0.3203]]))"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -477,15 +477,266 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "02a00747",
|
||||
"id": "3852bb53",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Operation"
|
||||
"### Slicing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 68,
|
||||
"id": "aeed7a0a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([1, 2])"
|
||||
]
|
||||
},
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x=torch.tensor([1, 2, 3, 4, 5, 6])\n",
|
||||
"x[0:2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"id": "721ce7eb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([1, 2])"
|
||||
]
|
||||
},
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x=torch.tensor([1, 2, 3, 4, 5, 6])\n",
|
||||
"x[:2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"id": "6423f4d2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor(6)"
|
||||
]
|
||||
},
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x=torch.tensor([1, 2, 3, 4, 5, 6])\n",
|
||||
"x[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"id": "0125386f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([3, 4, 5, 6])"
|
||||
]
|
||||
},
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x=torch.tensor([1, 2, 3, 4, 5, 6])\n",
|
||||
"x[2:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"id": "97373387",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([[1, 2],\n",
|
||||
" [4, 5]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 72,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Example 2D tensor\n",
|
||||
"x = torch.tensor([[1, 2, 3],\n",
|
||||
" [4, 5, 6],\n",
|
||||
" [7, 8, 9]])\n",
|
||||
"\n",
|
||||
"# Syntax: x[row_slice, column_slice]\n",
|
||||
"\n",
|
||||
"# Slice the first two rows and the first two columns\n",
|
||||
"x[:2, :2] # tensor([[1, 2],\n",
|
||||
" # [4, 5]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"id": "bba6b1b4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([4, 5, 6])"
|
||||
]
|
||||
},
|
||||
"execution_count": 73,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Slice the second row\n",
|
||||
"x[1, :] # tensor([4, 5, 6])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 74,
|
||||
"id": "12a96c84",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([3, 6, 9])"
|
||||
]
|
||||
},
|
||||
"execution_count": 74,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Slice the third column\n",
|
||||
"x[:, 2] # tensor([3, 6, 9])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"id": "a0f73c88",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([[5, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Example 2D tensor\n",
|
||||
"x = torch.tensor([[1, 2, 3],\n",
|
||||
" [4, 5, 6],\n",
|
||||
" [7, 8, 9]])\n",
|
||||
"\n",
|
||||
"# Get a submatrix from row 1 to 2 (exclusive of 2), and column 1 to 3\n",
|
||||
"x[1:2, 1:3] # tensor([[5, 6]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a3c1d8b5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### sum, mean, "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"id": "da5391eb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([False, False, True, True])"
|
||||
]
|
||||
},
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"a=torch.tensor([0, 1, 1, 1])\n",
|
||||
"b=torch.tensor([1, 0, 1, 1])\n",
|
||||
"a.eq(b)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 77,
|
||||
"id": "78ed8e4b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor(2)"
|
||||
]
|
||||
},
|
||||
"execution_count": 77,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# convert tensor tensor([False, False, True, True]) to [0, 0, 1, 1]\n",
|
||||
"a.eq(b).sum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "02a00747",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Operation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 78,
|
||||
"id": "45267f2f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -498,7 +749,7 @@
|
||||
" [7, 8]]))"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"execution_count": 78,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -513,7 +764,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 79,
|
||||
"id": "193a7828",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -524,7 +775,7 @@
|
||||
" [10, 12]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"execution_count": 79,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -535,7 +786,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 80,
|
||||
"id": "1ce81689",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -546,7 +797,7 @@
|
||||
" [21, 32]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"execution_count": 80,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -558,7 +809,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 81,
|
||||
"id": "62f8cde3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -568,7 +819,7 @@
|
||||
"tensor([11, 12, 13])"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"execution_count": 81,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -583,7 +834,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 82,
|
||||
"id": "2098ad78",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -594,7 +845,7 @@
|
||||
" [4, 5, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"execution_count": 82,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -607,7 +858,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 83,
|
||||
"id": "883321f8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -619,7 +870,7 @@
|
||||
" [5, 6]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"execution_count": 83,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -633,7 +884,7 @@
|
||||
"id": "9d716eb9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Comparison to NumPy Arrays\n",
|
||||
"## Comparison to NumPy Arrays\n",
|
||||
"Tensors are similar to NumPy arrays but add:\n",
|
||||
"- GPU support.\n",
|
||||
"- Automatic differentiation (requires_grad).\n",
|
||||
@@ -642,7 +893,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 84,
|
||||
"id": "2a3fd4ae",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -652,7 +903,7 @@
|
||||
"tensor([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"execution_count": 84,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -665,7 +916,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 85,
|
||||
"id": "df247bd3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -675,7 +926,7 @@
|
||||
"array([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"execution_count": 85,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -687,7 +938,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 86,
|
||||
"id": "9ada07ab",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -697,7 +948,7 @@
|
||||
"tensor([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"execution_count": 86,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -717,7 +968,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 87,
|
||||
"id": "30c9ea9f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -727,7 +978,7 @@
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"execution_count": 87,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -740,7 +991,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 88,
|
||||
"id": "dd523b3e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -750,7 +1001,7 @@
|
||||
"'cuda'"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"execution_count": 88,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -763,7 +1014,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 89,
|
||||
"id": "11d1a029",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -780,7 +1031,7 @@
|
||||
"tensor([1, 2, 3], device='cuda:0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"execution_count": 89,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -799,7 +1050,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 90,
|
||||
"id": "db5249d0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -807,7 +1058,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Weife\\AppData\\Local\\Temp\\ipykernel_54156\\3540074575.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
"C:\\Users\\Weife\\AppData\\Local\\Temp\\ipykernel_111340\\3540074575.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
" y = torch.tensor(x, device=device) # directly create a tensor on GPU\n"
|
||||
]
|
||||
}
|
||||
|
||||
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lectures/07_binary_classification_n_to_1/0_ML_workflow.pptx
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@@ -0,0 +1,484 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "31ee256c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Breast cancer prediction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "53af081c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"from sklearn.model_selection import train_test_split"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "536078f0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Load and preprocess breast cancer dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "06746e3c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\"\"\"Load and preprocess breast cancer dataset.\"\"\"\n",
|
||||
"# Load dataset\n",
|
||||
"data = load_breast_cancer()\n",
|
||||
"X, y = data.data, data.target"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3477485c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Understand inputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "76d4d576",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(569, 30)"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "fddcc037",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n",
|
||||
" 3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,\n",
|
||||
" 8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,\n",
|
||||
" 3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,\n",
|
||||
" 1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X[0, :]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "070dcd69",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(569,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "c4632c29",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"np.int64(0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b74373cb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" ### Split dataset into training and testing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "0675a8c7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||||
" X, y, test_size=0.2, random_state=1234\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "bfe70bd9",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(455, 30)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "a4df0052",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(114, 30)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_test.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d597a997",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Scale fetures\n",
|
||||
"Scaling features, as done in the code with StandardScaler, transforms the input data so that each feature has a mean of 0 and a standard deviation of 1. This is also known as standardization. The purpose of scaling features in this context is to:\n",
|
||||
"\n",
|
||||
"- Improve Model Convergence: Many machine learning algorithms, including neural networks optimized with gradient-based methods like SGD, converge faster when features are on a similar scale. Unscaled features with different ranges can cause gradients to vary widely, slowing down or destabilizing training.\n",
|
||||
"- Ensure Fair Feature Influence: Features with larger numerical ranges could disproportionately influence the model compared to features with smaller ranges. Standardization ensures all features contribute equally to the model's predictions.\n",
|
||||
"- Enhance Numerical Stability: Large or highly variable feature values can lead to numerical instability in computations, especially in deep learning frameworks like PyTorch. Scaling mitigates this risk."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "3aeb88da",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Scale features\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"X_train = scaler.fit_transform(X_train)\n",
|
||||
"X_test = scaler.transform(X_test)\n",
|
||||
"\n",
|
||||
"# Convert to PyTorch tensors\n",
|
||||
"X_train = torch.from_numpy(X_train.astype(np.float32))\n",
|
||||
"X_test = torch.from_numpy(X_test.astype(np.float32))\n",
|
||||
"y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)\n",
|
||||
"y_test = torch.from_numpy(y_test.astype(np.float32)).view(-1, 1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "3b10079f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"torch.Size([455, 30])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "13f4059c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([-0.3618, -0.2652, -0.3172, -0.4671, 1.8038, 1.1817, -0.5169, 0.1065,\n",
|
||||
" -0.3901, 1.3914, 0.1437, -0.1208, 0.1601, -0.1326, -0.5863, -0.1248,\n",
|
||||
" -0.5787, 0.1091, -0.2819, -0.1889, -0.2571, -0.2403, -0.2442, -0.3669,\n",
|
||||
" 0.5449, 0.2481, -0.7109, -0.0797, -0.5280, 0.2506])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train[0,:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b0b15d2f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Binary Classifier model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "e1b50a04",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class BinaryClassifier(nn.Module):\n",
|
||||
" \"\"\"Simple neural network for binary classification.\"\"\"\n",
|
||||
" def __init__(self, input_features):\n",
|
||||
" super(BinaryClassifier, self).__init__()\n",
|
||||
" self.linear = nn.Linear(input_features, 1)\n",
|
||||
" \n",
|
||||
" def forward(self, x):\n",
|
||||
" return torch.sigmoid(self.linear(x))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "49694959",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"torch.Size([455, 30])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X_train.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "14873622",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### show binary classification model \n",
|
||||
"- the number of input features\n",
|
||||
"- the number of output features"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "466f6c41",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"BinaryClassifier(\n",
|
||||
" (linear): Linear(in_features=30, out_features=1, bias=True)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"n_features = X_train.shape[1]\n",
|
||||
"model = BinaryClassifier(n_features)\n",
|
||||
"model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c66978b5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Train the model with given parameters.\n",
|
||||
"\n",
|
||||
"- forward pass: prediction\n",
|
||||
"- loss: error\n",
|
||||
"- autograd: weight change direction\n",
|
||||
"- stochastic gradient descent (optimizer): update weights\n",
|
||||
"- optimizer.zero_grad()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "1d1d7868",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch [10/100], Loss: 0.4627\n",
|
||||
"Epoch [20/100], Loss: 0.4105\n",
|
||||
"Epoch [30/100], Loss: 0.3721\n",
|
||||
"Epoch [40/100], Loss: 0.3424\n",
|
||||
"Epoch [50/100], Loss: 0.3186\n",
|
||||
"Epoch [60/100], Loss: 0.2990\n",
|
||||
"Epoch [70/100], Loss: 0.2825\n",
|
||||
"Epoch [80/100], Loss: 0.2683\n",
|
||||
"Epoch [90/100], Loss: 0.2560\n",
|
||||
"Epoch [100/100], Loss: 0.2452\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"num_epochs=100\n",
|
||||
"learning_rate=0.01\n",
|
||||
"\n",
|
||||
"\"\"\"Train the model with given parameters.\"\"\"\n",
|
||||
"criterion = nn.BCELoss()\n",
|
||||
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
|
||||
"\n",
|
||||
"for epoch in range(num_epochs):\n",
|
||||
" # Forward pass\n",
|
||||
" y_pred = model(X_train)\n",
|
||||
" loss = criterion(y_pred, y_train)\n",
|
||||
" \n",
|
||||
" # Backward pass and optimization\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" \n",
|
||||
" # Log progress\n",
|
||||
" if (epoch + 1) % 10 == 0:\n",
|
||||
" print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1a59248d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Evaluate model performance on test set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "eeddd812",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Test Accuracy: 0.8947\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with torch.no_grad():\n",
|
||||
" y_pred = model(X_test)\n",
|
||||
" y_pred_classes = y_pred.round() # Values 𝑥 ≥ 0.5 are rounded to 1, else 0\n",
|
||||
" accuracy = y_pred_classes.eq(y_test).sum() / float(y_test.shape[0])\n",
|
||||
" print(f'\\nTest Accuracy: {accuracy:.4f}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1dc4fcd3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
lectures/07_binary_classification_n_to_1/1_DataLoader.pptx
Normal file
BIN
lectures/07_binary_classification_n_to_1/1_DataLoader.pptx
Normal file
Binary file not shown.
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "53af081c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training model...\n",
|
||||
"Epoch [10/100], Loss: 0.6247\n",
|
||||
"Epoch [20/100], Loss: 0.4940\n",
|
||||
"Epoch [30/100], Loss: 0.4156\n",
|
||||
"Epoch [40/100], Loss: 0.3641\n",
|
||||
"Epoch [50/100], Loss: 0.3277\n",
|
||||
"Epoch [60/100], Loss: 0.3005\n",
|
||||
"Epoch [70/100], Loss: 0.2794\n",
|
||||
"Epoch [80/100], Loss: 0.2624\n",
|
||||
"Epoch [90/100], Loss: 0.2483\n",
|
||||
"Epoch [100/100], Loss: 0.2364\n",
|
||||
"\n",
|
||||
"Test Accuracy: 0.9211\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
"import numpy as np\n",
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"def prepare_data():\n",
|
||||
" \"\"\"Load and preprocess breast cancer dataset.\"\"\"\n",
|
||||
" # Load dataset\n",
|
||||
" data = load_breast_cancer()\n",
|
||||
" X, y = data.data, data.target\n",
|
||||
" \n",
|
||||
" # Split dataset\n",
|
||||
" X_train, X_test, y_train, y_test = train_test_split(\n",
|
||||
" X, y, test_size=0.2, random_state=1234\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" # Scale features\n",
|
||||
" scaler = StandardScaler()\n",
|
||||
" X_train = scaler.fit_transform(X_train)\n",
|
||||
" X_test = scaler.transform(X_test)\n",
|
||||
" \n",
|
||||
" # Convert to PyTorch tensors\n",
|
||||
" X_train = torch.from_numpy(X_train.astype(np.float32))\n",
|
||||
" X_test = torch.from_numpy(X_test.astype(np.float32))\n",
|
||||
" y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)\n",
|
||||
" y_test = torch.from_numpy(y_test.astype(np.float32)).view(-1, 1)\n",
|
||||
" \n",
|
||||
" return X_train, X_test, y_train, y_test\n",
|
||||
"\n",
|
||||
"class BinaryClassifier(nn.Module):\n",
|
||||
" \"\"\"Simple neural network for binary classification.\"\"\"\n",
|
||||
" def __init__(self, input_features):\n",
|
||||
" super(BinaryClassifier, self).__init__()\n",
|
||||
" self.linear = nn.Linear(input_features, 1)\n",
|
||||
" \n",
|
||||
" def forward(self, x):\n",
|
||||
" return torch.sigmoid(self.linear(x))\n",
|
||||
"\n",
|
||||
"def train_model(model, X_train, y_train, num_epochs=100, learning_rate=0.01):\n",
|
||||
" \"\"\"Train the model with given parameters.\"\"\"\n",
|
||||
" criterion = nn.BCELoss()\n",
|
||||
" optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
|
||||
" \n",
|
||||
" for epoch in range(num_epochs):\n",
|
||||
" # Forward pass\n",
|
||||
" y_pred = model(X_train)\n",
|
||||
" loss = criterion(y_pred, y_train)\n",
|
||||
" \n",
|
||||
" # Backward pass and optimization\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" \n",
|
||||
" # Log progress\n",
|
||||
" if (epoch + 1) % 10 == 0:\n",
|
||||
" print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
|
||||
"\n",
|
||||
"def evaluate_model(model, X_test, y_test):\n",
|
||||
" \"\"\"Evaluate model performance on test set.\"\"\"\n",
|
||||
" with torch.no_grad():\n",
|
||||
" y_pred = model(X_test)\n",
|
||||
" y_pred_classes = y_pred.round()\n",
|
||||
" accuracy = y_pred_classes.eq(y_test).sum() / float(y_test.shape[0])\n",
|
||||
" return accuracy.item()\n",
|
||||
"\n",
|
||||
"def main():\n",
|
||||
" # Prepare data\n",
|
||||
" X_train, X_test, y_train, y_test = prepare_data()\n",
|
||||
" \n",
|
||||
" # Initialize model\n",
|
||||
" n_features = X_train.shape[1]\n",
|
||||
" model = BinaryClassifier(n_features)\n",
|
||||
" \n",
|
||||
" # Train model\n",
|
||||
" print(\"Training model...\")\n",
|
||||
" train_model(model, X_train, y_train)\n",
|
||||
" \n",
|
||||
" # Evaluate model\n",
|
||||
" accuracy = evaluate_model(model, X_test, y_test)\n",
|
||||
" print(f'\\nTest Accuracy: {accuracy:.4f}')\n",
|
||||
"\n",
|
||||
"main()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "76d4d576",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
188
lectures/07_binary_classification_n_to_1/2_dataloader.ipynb
Normal file
188
lectures/07_binary_classification_n_to_1/2_dataloader.ipynb
Normal file
@@ -0,0 +1,188 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "52950b67",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"First sample - Features: tensor([1.4230e+01, 1.7100e+00, 2.4300e+00, 1.5600e+01, 1.2700e+02, 2.8000e+00,\n",
|
||||
" 3.0600e+00, 2.8000e-01, 2.2900e+00, 5.6400e+00, 1.0400e+00, 3.9200e+00,\n",
|
||||
" 1.0650e+03]), Label: tensor([1.])\n",
|
||||
"Sample batch - Features: torch.Size([4, 13]), Labels: torch.Size([4, 1])\n",
|
||||
"Total samples: 178, Iterations per epoch: 45\n",
|
||||
"Epoch: 1/2, Step 5/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 10/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 15/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 20/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 25/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 30/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 35/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 40/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 1/2, Step 45/45 | Inputs torch.Size([2, 13]) | Labels torch.Size([2, 1])\n",
|
||||
"Epoch: 2/2, Step 5/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 10/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 15/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 20/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 25/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 30/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 35/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 40/45 | Inputs torch.Size([4, 13]) | Labels torch.Size([4, 1])\n",
|
||||
"Epoch: 2/2, Step 45/45 | Inputs torch.Size([2, 13]) | Labels torch.Size([2, 1])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torchvision\n",
|
||||
"from torch.utils.data import Dataset, DataLoader\n",
|
||||
"import numpy as np\n",
|
||||
"import math\n",
|
||||
"\n",
|
||||
"# Custom Dataset class for Wine dataset\n",
|
||||
"class WineDataset(Dataset):\n",
|
||||
" def __init__(self, data_path='data/wine.csv'):\n",
|
||||
" \"\"\"\n",
|
||||
" Initialize the dataset by loading wine data from a CSV file.\n",
|
||||
" \n",
|
||||
" Args:\n",
|
||||
" data_path (str): Path to the wine CSV file\n",
|
||||
" \"\"\"\n",
|
||||
" # Load data from CSV, skipping header row\n",
|
||||
" xy = np.loadtxt(data_path, delimiter=',', dtype=np.float32, skiprows=1)\n",
|
||||
" self.n_samples = xy.shape[0]\n",
|
||||
" \n",
|
||||
" # Split into features (all columns except first) and labels (first column)\n",
|
||||
" self.x_data = torch.from_numpy(xy[:, 1:]) # Shape: [n_samples, n_features]\n",
|
||||
" self.y_data = torch.from_numpy(xy[:, [0]]) # Shape: [n_samples, 1]\n",
|
||||
"\n",
|
||||
" def __getitem__(self, index):\n",
|
||||
" \"\"\"\n",
|
||||
" Enable indexing to retrieve a specific sample.\n",
|
||||
" \n",
|
||||
" Args:\n",
|
||||
" index (int): Index of the sample to retrieve\n",
|
||||
" \n",
|
||||
" Returns:\n",
|
||||
" tuple: (features, label) for the specified index\n",
|
||||
" \"\"\"\n",
|
||||
" return self.x_data[index], self.y_data[index]\n",
|
||||
"\n",
|
||||
" def __len__(self):\n",
|
||||
" \"\"\"\n",
|
||||
" Return the total number of samples in the dataset.\n",
|
||||
" \n",
|
||||
" Returns:\n",
|
||||
" int: Number of samples\n",
|
||||
" \"\"\"\n",
|
||||
" return self.n_samples\n",
|
||||
"\n",
|
||||
"# Create dataset instance\n",
|
||||
"dataset = WineDataset()\n",
|
||||
"\n",
|
||||
"# Access and print first sample\n",
|
||||
"features, labels = dataset[0]\n",
|
||||
"print(f\"First sample - Features: {features}, Label: {labels}\")\n",
|
||||
"\n",
|
||||
"\"\"\"\n",
|
||||
"Create a DataLoader for the wine dataset.\n",
|
||||
"\n",
|
||||
"Args:\n",
|
||||
" dataset (Dataset): The dataset to load\n",
|
||||
" batch_size (int): Number of samples per batch\n",
|
||||
" shuffle (bool): Whether to shuffle the data\n",
|
||||
" num_workers (int): Number of subprocesses for data loading\n",
|
||||
" \n",
|
||||
"Returns:\n",
|
||||
" DataLoader: Configured DataLoader instance\n",
|
||||
"\"\"\"\n",
|
||||
"train_loader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=0)\n",
|
||||
"\n",
|
||||
"# Examine one batch\n",
|
||||
"dataiter = iter(train_loader)\n",
|
||||
"features, labels = next(dataiter)\n",
|
||||
"print(f\"Sample batch - Features: {features.shape}, Labels: {labels.shape}\")\n",
|
||||
"\n",
|
||||
"# Training loop parameters\n",
|
||||
"num_epochs = 2\n",
|
||||
"total_samples = len(dataset)\n",
|
||||
"n_iterations = math.ceil(total_samples / 4)\n",
|
||||
"print(f\"Total samples: {total_samples}, Iterations per epoch: {n_iterations}\")\n",
|
||||
"\n",
|
||||
"# Dummy training loop\n",
|
||||
"for epoch in range(num_epochs):\n",
|
||||
" for i, (inputs, labels) in enumerate(train_loader):\n",
|
||||
" # Training step\n",
|
||||
" if (i + 1) % 5 == 0:\n",
|
||||
" print(f'Epoch: {epoch+1}/{num_epochs}, Step {i+1}/{n_iterations} | '\n",
|
||||
" f'Inputs {inputs.shape} | Labels {labels.shape}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "37095d28",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 9.91M/9.91M [00:02<00:00, 4.92MB/s]\n",
|
||||
"100%|██████████| 28.9k/28.9k [00:00<00:00, 3.21MB/s]\n",
|
||||
"100%|██████████| 1.65M/1.65M [00:00<00:00, 9.59MB/s]\n",
|
||||
"100%|██████████| 4.54k/4.54k [00:00<00:00, 9.73MB/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"MNIST batch - Inputs: torch.Size([3, 1, 28, 28]), Targets: torch.Size([3])\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Example with MNIST dataset\n",
|
||||
"train_dataset = torchvision.datasets.MNIST(root='./data',\n",
|
||||
" train=True,\n",
|
||||
" transform=torchvision.transforms.ToTensor(),\n",
|
||||
" download=True)\n",
|
||||
"\n",
|
||||
"mnist_loader = DataLoader(dataset=train_dataset,\n",
|
||||
" batch_size=3,\n",
|
||||
" shuffle=True)\n",
|
||||
"\n",
|
||||
"# Examine MNIST batch\n",
|
||||
"dataiter = iter(mnist_loader)\n",
|
||||
"inputs, targets = next(dataiter)\n",
|
||||
"print(f\"MNIST batch - Inputs: {inputs.shape}, Targets: {targets.shape}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
179
lectures/07_binary_classification_n_to_1/data/wine.csv
Normal file
179
lectures/07_binary_classification_n_to_1/data/wine.csv
Normal file
@@ -0,0 +1,179 @@
|
||||
Wine,Alcohol,Malic.acid,Ash,Acl,Mg,Phenols,Flavanoids,Nonflavanoid.phenols,Proanth,Color.int,Hue,OD,Proline
|
||||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
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lectures/20_pose_detection_yolo/bus.jpg
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lectures/20_pose_detection_yolo/dance.mp4
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lectures/20_pose_detection_yolo/dance.mp4
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lectures/20_pose_detection_yolo/pose_output.jpg
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428
lectures/20_pose_detection_yolo/post_detection.ipynb
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Reference in New Issue
Block a user