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b2f1c12d4c
fastbook/clean/04_mnist_basics.ipynb
Sylvain Gugger b2f1c12d4c Update and add clean
2020-03-06 10:19:03 -08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"from fastai2.vision.all import *\n",
"from utils import *\n",
"\n",
"matplotlib.rc('image', cmap='Greys')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Under the hood: training a digit classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pixels: the foundations of computer vision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sidebar: Tenacity and deep learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## End sidebar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = untar_data(URLs.MNIST_SAMPLE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"Path.BASE_PATH = path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#3) [Path('valid'),Path('train'),Path('labels.csv')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path.ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#2) [Path('train/3'),Path('train/7')]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(path/'train').ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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{
"data": {
"text/plain": [
"(#6131) [Path('train/3/10.png'),Path('train/3/10000.png'),Path('train/3/10011.png'),Path('train/3/10031.png'),Path('train/3/10034.png'),Path('train/3/10042.png'),Path('train/3/10052.png'),Path('train/3/1007.png'),Path('train/3/10074.png'),Path('train/3/10091.png')...]"
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"source": [
"threes = (path/'train'/'3').ls().sorted()\n",
"sevens = (path/'train'/'7').ls().sorted()\n",
"threes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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],
"source": [
"im3_path = threes[1]\n",
"im3 = Image.open(im3_path)\n",
"im3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
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" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col8 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col9 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col10 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col11 {\n",
" font-size: 6pt;\n",
" background-color: #4e4e4e;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col12 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col13 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col14 {\n",
" font-size: 6pt;\n",
" background-color: #767676;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col15 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col1 {\n",
" font-size: 6pt;\n",
" background-color: #fcfcfc;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col2 {\n",
" font-size: 6pt;\n",
" background-color: #f6f6f6;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col3 {\n",
" font-size: 6pt;\n",
" background-color: #f6f6f6;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col4 {\n",
" font-size: 6pt;\n",
" background-color: #f8f8f8;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col5 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col6 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col7 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col8 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col9 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col10 {\n",
" font-size: 6pt;\n",
" background-color: #e8e8e8;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col11 {\n",
" font-size: 6pt;\n",
" background-color: #222222;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col12 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col13 {\n",
" font-size: 6pt;\n",
" background-color: #090909;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col14 {\n",
" font-size: 6pt;\n",
" background-color: #d0d0d0;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col15 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col1 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col2 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col3 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col4 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col5 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col6 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col7 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col8 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col9 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col10 {\n",
" font-size: 6pt;\n",
" background-color: #060606;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col11 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col12 {\n",
" font-size: 6pt;\n",
" background-color: #090909;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col13 {\n",
" font-size: 6pt;\n",
" background-color: #979797;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col14 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col15 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col1 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col2 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col3 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col4 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col5 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col6 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col7 {\n",
" font-size: 6pt;\n",
" background-color: #f8f8f8;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col8 {\n",
" font-size: 6pt;\n",
" background-color: #b6b6b6;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col9 {\n",
" font-size: 6pt;\n",
" background-color: #252525;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col10 {\n",
" font-size: 6pt;\n",
" background-color: #010101;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col11 {\n",
" font-size: 6pt;\n",
" background-color: #060606;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col12 {\n",
" font-size: 6pt;\n",
" background-color: #999999;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col13 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col14 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col15 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col1 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col2 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col3 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col4 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col5 {\n",
" font-size: 6pt;\n",
" background-color: #f9f9f9;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col6 {\n",
" font-size: 6pt;\n",
" background-color: #6b6b6b;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col7 {\n",
" font-size: 6pt;\n",
" background-color: #101010;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col8 {\n",
" font-size: 6pt;\n",
" background-color: #010101;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col9 {\n",
" font-size: 6pt;\n",
" background-color: #020202;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col10 {\n",
" font-size: 6pt;\n",
" background-color: #010101;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col11 {\n",
" font-size: 6pt;\n",
" background-color: #545454;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col12 {\n",
" font-size: 6pt;\n",
" background-color: #f1f1f1;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col13 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col14 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col15 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col1 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col2 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col3 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col4 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col5 {\n",
" font-size: 6pt;\n",
" background-color: #f7f7f7;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col6 {\n",
" font-size: 6pt;\n",
" background-color: #060606;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col7 {\n",
" font-size: 6pt;\n",
" background-color: #030303;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col8 {\n",
" font-size: 6pt;\n",
" background-color: #010101;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col9 {\n",
" font-size: 6pt;\n",
" background-color: #020202;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col10 {\n",
" font-size: 6pt;\n",
" background-color: #010101;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col11 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col12 {\n",
" font-size: 6pt;\n",
" background-color: #181818;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col13 {\n",
" font-size: 6pt;\n",
" background-color: #303030;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col14 {\n",
" font-size: 6pt;\n",
" background-color: #a9a9a9;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col15 {\n",
" font-size: 6pt;\n",
" background-color: #fefefe;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col16 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col0 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col1 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col2 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col3 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col4 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col5 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col6 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col7 {\n",
" font-size: 6pt;\n",
" background-color: #e8e8e8;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col8 {\n",
" font-size: 6pt;\n",
" background-color: #bababa;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col9 {\n",
" font-size: 6pt;\n",
" background-color: #bababa;\n",
" color: #000000;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col10 {\n",
" font-size: 6pt;\n",
" background-color: #393939;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col11 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col12 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col13 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col14 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col15 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col16 {\n",
" font-size: 6pt;\n",
" background-color: #000000;\n",
" color: #f1f1f1;\n",
" } #T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col17 {\n",
" font-size: 6pt;\n",
" background-color: #ffffff;\n",
" color: #000000;\n",
" }</style><table id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >0</th> <th class=\"col_heading level0 col1\" >1</th> <th class=\"col_heading level0 col2\" >2</th> <th class=\"col_heading level0 col3\" >3</th> <th class=\"col_heading level0 col4\" >4</th> <th class=\"col_heading level0 col5\" >5</th> <th class=\"col_heading level0 col6\" >6</th> <th class=\"col_heading level0 col7\" >7</th> <th class=\"col_heading level0 col8\" >8</th> <th class=\"col_heading level0 col9\" >9</th> <th class=\"col_heading level0 col10\" >10</th> <th class=\"col_heading level0 col11\" >11</th> <th class=\"col_heading level0 col12\" >12</th> <th class=\"col_heading level0 col13\" >13</th> <th class=\"col_heading level0 col14\" >14</th> <th class=\"col_heading level0 col15\" >15</th> <th class=\"col_heading level0 col16\" >16</th> <th class=\"col_heading level0 col17\" >17</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col0\" class=\"data row0 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col1\" class=\"data row0 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col2\" class=\"data row0 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col3\" class=\"data row0 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col4\" class=\"data row0 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col5\" class=\"data row0 col5\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col6\" class=\"data row0 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col7\" class=\"data row0 col7\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col8\" class=\"data row0 col8\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col9\" class=\"data row0 col9\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col10\" class=\"data row0 col10\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col11\" class=\"data row0 col11\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col12\" class=\"data row0 col12\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col13\" class=\"data row0 col13\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col14\" class=\"data row0 col14\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col15\" class=\"data row0 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col16\" class=\"data row0 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row0_col17\" class=\"data row0 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col0\" class=\"data row1 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col1\" class=\"data row1 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col2\" class=\"data row1 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col3\" class=\"data row1 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col4\" class=\"data row1 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col5\" class=\"data row1 col5\" >29</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col6\" class=\"data row1 col6\" >150</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col7\" class=\"data row1 col7\" >195</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col8\" class=\"data row1 col8\" >254</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col9\" class=\"data row1 col9\" >255</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col10\" class=\"data row1 col10\" >254</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col11\" class=\"data row1 col11\" >176</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col12\" class=\"data row1 col12\" >193</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col13\" class=\"data row1 col13\" >150</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col14\" class=\"data row1 col14\" >96</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col15\" class=\"data row1 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col16\" class=\"data row1 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row1_col17\" class=\"data row1 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col0\" class=\"data row2 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col1\" class=\"data row2 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col2\" class=\"data row2 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col3\" class=\"data row2 col3\" >48</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col4\" class=\"data row2 col4\" >166</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col5\" class=\"data row2 col5\" >224</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col6\" class=\"data row2 col6\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col7\" class=\"data row2 col7\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col8\" class=\"data row2 col8\" >234</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col9\" class=\"data row2 col9\" >196</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col10\" class=\"data row2 col10\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col11\" class=\"data row2 col11\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col12\" class=\"data row2 col12\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col13\" class=\"data row2 col13\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col14\" class=\"data row2 col14\" >233</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col15\" class=\"data row2 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col16\" class=\"data row2 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row2_col17\" class=\"data row2 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col0\" class=\"data row3 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col1\" class=\"data row3 col1\" >93</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col2\" class=\"data row3 col2\" >244</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col3\" class=\"data row3 col3\" >249</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col4\" class=\"data row3 col4\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col5\" class=\"data row3 col5\" >187</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col6\" class=\"data row3 col6\" >46</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col7\" class=\"data row3 col7\" >10</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col8\" class=\"data row3 col8\" >8</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col9\" class=\"data row3 col9\" >4</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col10\" class=\"data row3 col10\" >10</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col11\" class=\"data row3 col11\" >194</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col12\" class=\"data row3 col12\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col13\" class=\"data row3 col13\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col14\" class=\"data row3 col14\" >233</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col15\" class=\"data row3 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col16\" class=\"data row3 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row3_col17\" class=\"data row3 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col0\" class=\"data row4 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col1\" class=\"data row4 col1\" >107</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col2\" class=\"data row4 col2\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col3\" class=\"data row4 col3\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col4\" class=\"data row4 col4\" >230</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col5\" class=\"data row4 col5\" >48</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col6\" class=\"data row4 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col7\" class=\"data row4 col7\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col8\" class=\"data row4 col8\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col9\" class=\"data row4 col9\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col10\" class=\"data row4 col10\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col11\" class=\"data row4 col11\" >192</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col12\" class=\"data row4 col12\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col13\" class=\"data row4 col13\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col14\" class=\"data row4 col14\" >156</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col15\" class=\"data row4 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col16\" class=\"data row4 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row4_col17\" class=\"data row4 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col0\" class=\"data row5 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col1\" class=\"data row5 col1\" >3</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col2\" class=\"data row5 col2\" >20</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col3\" class=\"data row5 col3\" >20</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col4\" class=\"data row5 col4\" >15</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col5\" class=\"data row5 col5\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col6\" class=\"data row5 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col7\" class=\"data row5 col7\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col8\" class=\"data row5 col8\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col9\" class=\"data row5 col9\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col10\" class=\"data row5 col10\" >43</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col11\" class=\"data row5 col11\" >224</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col12\" class=\"data row5 col12\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col13\" class=\"data row5 col13\" >245</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col14\" class=\"data row5 col14\" >74</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col15\" class=\"data row5 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col16\" class=\"data row5 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row5_col17\" class=\"data row5 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col0\" class=\"data row6 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col1\" class=\"data row6 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col2\" class=\"data row6 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col3\" class=\"data row6 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col4\" class=\"data row6 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col5\" class=\"data row6 col5\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col6\" class=\"data row6 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col7\" class=\"data row6 col7\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col8\" class=\"data row6 col8\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col9\" class=\"data row6 col9\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col10\" class=\"data row6 col10\" >249</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col11\" class=\"data row6 col11\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col12\" class=\"data row6 col12\" >245</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col13\" class=\"data row6 col13\" >126</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col14\" class=\"data row6 col14\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col15\" class=\"data row6 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col16\" class=\"data row6 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row6_col17\" class=\"data row6 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col0\" class=\"data row7 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col1\" class=\"data row7 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col2\" class=\"data row7 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col3\" class=\"data row7 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col4\" class=\"data row7 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col5\" class=\"data row7 col5\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col6\" class=\"data row7 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col7\" class=\"data row7 col7\" >14</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col8\" class=\"data row7 col8\" >101</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col9\" class=\"data row7 col9\" >223</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col10\" class=\"data row7 col10\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col11\" class=\"data row7 col11\" >248</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col12\" class=\"data row7 col12\" >124</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col13\" class=\"data row7 col13\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col14\" class=\"data row7 col14\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col15\" class=\"data row7 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col16\" class=\"data row7 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row7_col17\" class=\"data row7 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col0\" class=\"data row8 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col1\" class=\"data row8 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col2\" class=\"data row8 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col3\" class=\"data row8 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col4\" class=\"data row8 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col5\" class=\"data row8 col5\" >11</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col6\" class=\"data row8 col6\" >166</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col7\" class=\"data row8 col7\" >239</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col8\" class=\"data row8 col8\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col9\" class=\"data row8 col9\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col10\" class=\"data row8 col10\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col11\" class=\"data row8 col11\" >187</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col12\" class=\"data row8 col12\" >30</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col13\" class=\"data row8 col13\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col14\" class=\"data row8 col14\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col15\" class=\"data row8 col15\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col16\" class=\"data row8 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row8_col17\" class=\"data row8 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col0\" class=\"data row9 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col1\" class=\"data row9 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col2\" class=\"data row9 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col3\" class=\"data row9 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col4\" class=\"data row9 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col5\" class=\"data row9 col5\" >16</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col6\" class=\"data row9 col6\" >248</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col7\" class=\"data row9 col7\" >250</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col8\" class=\"data row9 col8\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col9\" class=\"data row9 col9\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col10\" class=\"data row9 col10\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col11\" class=\"data row9 col11\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col12\" class=\"data row9 col12\" >232</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col13\" class=\"data row9 col13\" >213</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col14\" class=\"data row9 col14\" >111</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col15\" class=\"data row9 col15\" >2</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col16\" class=\"data row9 col16\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row9_col17\" class=\"data row9 col17\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col0\" class=\"data row10 col0\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col1\" class=\"data row10 col1\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col2\" class=\"data row10 col2\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col3\" class=\"data row10 col3\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col4\" class=\"data row10 col4\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col5\" class=\"data row10 col5\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col6\" class=\"data row10 col6\" >0</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col7\" class=\"data row10 col7\" >43</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col8\" class=\"data row10 col8\" >98</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col9\" class=\"data row10 col9\" >98</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col10\" class=\"data row10 col10\" >208</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col11\" class=\"data row10 col11\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col12\" class=\"data row10 col12\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col13\" class=\"data row10 col13\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col14\" class=\"data row10 col14\" >253</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col15\" class=\"data row10 col15\" >187</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col16\" class=\"data row10 col16\" >22</td>\n",
" <td id=\"T_31b48dea_5f33_11ea_b5ec_637c5f146091row10_col17\" class=\"data row10 col17\" >0</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f439e8e23d0>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im3_t = tensor(im3)\n",
"df = pd.DataFrame(im3_t[4:15,4:22])\n",
"df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First try: pixel similarity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6131, 6265)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"seven_tensors = [tensor(Image.open(o)) for o in sevens]\n",
"three_tensors = [tensor(Image.open(o)) for o in threes]\n",
"len(three_tensors),len(seven_tensors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 72x72 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_image(three_tensors[1]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([6131, 28, 28])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stacked_sevens = torch.stack(seven_tensors).float()/255\n",
"stacked_threes = torch.stack(three_tensors).float()/255\n",
"stacked_threes.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(stacked_threes.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stacked_threes.ndim"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 72x72 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean3 = stacked_threes.mean(0)\n",
"show_image(mean3);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 72x72 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mean7 = stacked_sevens.mean(0)\n",
"show_image(mean7);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 72x72 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a_3 = stacked_threes[1]\n",
"show_image(a_3);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.1114), tensor(0.2021))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dist_3_abs = (a_3 - mean3).abs().mean()\n",
"dist_3_sqr = ((a_3 - mean3)**2).mean().sqrt()\n",
"dist_3_abs,dist_3_sqr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.1586), tensor(0.3021))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dist_7_abs = (a_3 - mean7).abs().mean()\n",
"dist_7_sqr = ((a_3 - mean7)**2).mean().sqrt()\n",
"dist_7_abs,dist_7_sqr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.1586), tensor(0.3021))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"F.l1_loss(a_3.float(),mean7), F.mse_loss(a_3,mean7).sqrt()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### NumPy arrays and PyTorch tensors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = [[1,2,3],[4,5,6]]\n",
"arr = array (data)\n",
"tns = tensor(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr # numpy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns # pytorch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([4, 5, 6])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([2, 5])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns[:,1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([5, 6])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns[1,1:3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[2, 3, 4],\n",
" [5, 6, 7]])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns+1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'torch.LongTensor'"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns.type()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[1.5000, 3.0000, 4.5000],\n",
" [6.0000, 7.5000, 9.0000]])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tns*1.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing metrics using broadcasting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([1010, 28, 28]), torch.Size([1028, 28, 28]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"valid_3_tens = torch.stack([tensor(Image.open(o)) \n",
" for o in (path/'valid'/'3').ls()])\n",
"valid_3_tens = valid_3_tens.float()/255\n",
"valid_7_tens = torch.stack([tensor(Image.open(o)) \n",
" for o in (path/'valid'/'7').ls()])\n",
"valid_7_tens = valid_7_tens.float()/255\n",
"valid_3_tens.shape,valid_7_tens.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.1114)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def mnist_distance(a,b): return (a-b).abs().mean((-1,-2))\n",
"mnist_distance(a_3, mean3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([0.1156, 0.1257, 0.1369, ..., 0.1493, 0.1366, 0.1107]),\n",
" torch.Size([1010]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"valid_3_dist = mnist_distance(valid_3_tens, mean3)\n",
"valid_3_dist, valid_3_dist.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([2, 3, 4])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tensor([1,2,3]) + tensor([1,1,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1010, 28, 28])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(valid_3_tens-mean3).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def is_3(x): return mnist_distance(x,mean3) < mnist_distance(x,mean7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(True), tensor(1.))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_3(a_3), is_3(a_3).float()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([True, True, True, ..., True, True, True])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_3(valid_3_tens)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(0.9168), tensor(0.9854), tensor(0.9511))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_3s = is_3(valid_3_tens).float() .mean()\n",
"accuracy_7s = (1 - is_3(valid_7_tens).float()).mean()\n",
"\n",
"accuracy_3s,accuracy_7s,(accuracy_3s+accuracy_7s)/2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stochastic Gradient descent (SGD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true
},
"outputs": [
{
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],
"text/plain": [
"<graphviz.files.Source at 0x7f439de1c290>"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gv('''\n",
"init->predict->loss->gradient->step->stop\n",
"step->predict[label=repeat]\n",
"''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f(x): return x**2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_function(f, 'x', 'x**2')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_function(f, 'x', 'x**2')\n",
"plt.scatter(-1.5, f(-1.5), color='red');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The gradient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xt = tensor(3.).requires_grad_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(9., grad_fn=<PowBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yt = f(xt)\n",
"yt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"yt.backward()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(6.)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xt.grad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 3., 4., 10.], requires_grad=True)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xt = tensor([3.,4.,10.]).requires_grad_()\n",
"xt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(125., grad_fn=<SumBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def f(x): return (x**2).sum()\n",
"\n",
"yt = f(xt)\n",
"yt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 6., 8., 20.])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yt.backward()\n",
"xt.grad"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stepping with a learning rate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### An end-to-end SGD example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time = torch.arange(0,20).float(); time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"speed = torch.randn(20)*3 + 0.75*(time-9.5)**2 + 1\n",
"plt.scatter(time,speed);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def f(t, params):\n",
" a,b,c = params\n",
" return a*(t**2) + (b*t) + c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mse(preds, targets): return ((preds-targets)**2).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"params = torch.randn(3).requires_grad_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"orig_params = params.clone()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"preds = f(time, params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def show_preds(preds, ax=None):\n",
" if ax is None: ax=plt.subplots()[1]\n",
" ax.scatter(time, speed)\n",
" ax.scatter(time, to_np(preds), color='red')\n",
" ax.set_ylim(-300,100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_preds(preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(25823.8086, grad_fn=<MeanBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss = mse(preds, speed)\n",
"loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-53195.8594, -3419.7146, -253.8908])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss.backward()\n",
"params.grad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-0.5320, -0.0342, -0.0025])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params.grad * 1e-5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-0.7658, -0.7506, 1.3525], requires_grad=True)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lr = 1e-5\n",
"params.data -= lr * params.grad.data\n",
"params.grad = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(5435.5366, grad_fn=<MeanBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = f(time,params)\n",
"mse(preds, speed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_preds(preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def apply_step(params, prn=True):\n",
" preds = f(time, params)\n",
" loss = mse(preds, speed)\n",
" loss.backward()\n",
" params.data -= lr * params.grad.data\n",
" params.grad = None\n",
" if prn: print(loss.item())\n",
" return preds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5435.53662109375\n",
"1577.4495849609375\n",
"847.3780517578125\n",
"709.22265625\n",
"683.0757446289062\n",
"678.12451171875\n",
"677.1839599609375\n",
"677.0025024414062\n",
"676.96435546875\n",
"676.9537353515625\n"
]
}
],
"source": [
"for i in range(10): apply_step(params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#hide\n",
"params = orig_params.detach().requires_grad_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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xkjT80fu66bmHEtu+9/HLuuvJ+feY3PXk3frexy/rspefaXr/yWUvP9O7e2wydE92juTjHFX/tdP3pm1L+DmGt96ljxbOP998tHCRhrfepR9ecrVmho6b1zYzdJx+eMnVmjrvy1p/ybV6fclyzcr0+pLlWl+9OPHZ9X+kTb/3x/PaNv3eH+uz6/9IUnRePH/Tt/TVm36k02/8W331ph+VopjqmLsP/CFpsaIbE8+o2/agpC1J7zv33HM90cMPu4+OuptFXx9+eG778LB79F9q9Bgennt9/fbaY3Q0uS1jtv3sdV+18e999MbHP3ms2vj3vu1nrye21d77hTv/wVfe+Lh/4c5/+GR7mUma8gxkpd1HpjKVNm9mzdtqf3dSFuP2swtkKqwyZCp1nmptaTLVi/Nb2pwmtbn7bEz7rFliW09+jl4c71b/xu38DnTw/1ieMpWbc1Q37+3V701M27afve5/etk6f23Jcv9Y5q8tWe5/etk6zlFd6CRTAw/VJzsi/UjSX1VDdp6kQ5JWJ72nZbCSpAldi5NDLz7EdSMpIISnM3k6UdUemclUUlsviq1WJ6pW+5qATIVThkx1lSf39B+2Q3+o7NVFyH4XcWkv0vSiSOvm3yNG3jKVi3NUt+8NWDC3g3NUWHktqE6U9Jikw5J2S/paq/d0fbKKE/dL3s1/nMi1vJ2oPGuZStLvnmSymgllyNRA8tRKVoq0bt6bpZ62bnrSA4+IyVumcnOOQmnlsqBK8+h7sNL+B4/cy9uJKu0jcyerXvQkD2C4II5WhkxlLk+90u8r/1nqaUtbpLl3NVyyGTIFhEVB1Utx/8G3858fH9RyqwwnKs/bySpNT7J798MFEUQZMpWrPOVNVnraurnQWvIeqjQPMoV+oqAaBIYZFVoZTlSetUyl1Spr3Q4X5KJIEGXIVCHyVCb9LNJatZfgHqo0DzKFfqKgGoRuPsQh88pwovKsZaobrT78pBkuyEWRoMqQqcLkCen1arKDJsgUEFYnmcrEwr5pJa6YPQhJCwYmLQw3O9vf/UTH8rRgYjcyl6leictq0sKQUm4W/c6DMmSqNHlCJpApIKw8LuxbDJVK9MFqdjb6Wv9hqtXCcSwMCPRPXFaTFobM0aLfAACgfyio+iXpgxofxoBsqFSkiYmo18ks+joxEW1PuiiyYYM0MzN/+8xMtF3iggkAAAVGQdUvSR/UWn0YA9A/9F4BAIAOUFD1U9wHtaQPYwCyoVe9VwAAINcoqLKA+6uAfAjdeyWRbwAAco6CKgu4vwrIt7S9V+QbAIDco6DKAu6vAvIvTe8V+QYAIPcoqLKC+6uAYkq6YMJwQAAAcm9o0DuAFkZGmi8mGjeMCED2VCrNF/lNyndtOGCtB6s2HLD2/QAAQCbQQ5V1ScOFJK5gA3nWzXBAsg8AQCZQUGVd0nAhbmgH8i3tcECyDwBAZlBQ5UHc/VXc0A7kX1y+WdsKAIBcoKDKMyasAIqrm7WtAABA31BQ5VmrBYEB5Ffata0k7q8CAKCPKKjyrNWEFQDyLc3aVtxfBQBAX1FQ5VnSFWyJq9RAUbEYOAAAmUFBlXdxV7C5Sg0UWzeLgXOxBQCAYCioioqr1EA5tXN/FRdbAAAIhoKqqJgFDCinVvdWcrEFAICgKKiKihkAgXJqdW9lq4stDAcEAKAjFFRFxQyAQHnF3V8lJV9sYTggAAAdo6AqKmYABNBM0sUWhgMCANAxCqoiYwZAAI2SLrZw7yUAAB2joCojrkID5RZ3saWdGQLp2QYAYB4KqjLiKjSAZpKGA9KzDQBAUxRUZcQMgACaSRoOSM82AABNUVCVETMAAogTNxyQnm0AAJqioCqjVjMAAkAj7q8CAKCpIAWVmV1rZlNm9r6ZPdCk/SIz22lmM2b2tJmN1rUdZ2b3m9nbZvaGmV0XYp/QQtI6NXwwGjgyhczJ+f1VZAoIi0wBc0L1UO2VdIek+xsbzOwkSY9KulnSiZKmJD1S95JbJZ0haVTShZJuMLNLAu0XOpWDD0YlQaaQLfm/v4pMAWGRKaAqSEHl7o+6+2OS/m+T5ssl7XD3H7v7e4pCtMbMVlXbr5R0u7sfcPdfSrpX0lUh9gsp5OODUeGRKWRSju+vIlNAWGQKmNOPe6hWS3qx9sTdD0v6taTVZrZM0qn17dU/r477ZmY2Xu1intq/f3+PdrnEcvDBCGQKGZP/mUODZYo8AZLIFEqmHwXV8ZIONWw7JOmEapsa2mttTbn7hLuPufvY8uXLg+4oVIQPRmVAppAt+Z85NFimyBMgiUyhZFoWVGb2jJl5zOO5Nv6OdyUtadi2RNI71TY1tNfaMAj5/2CUeWQKhTPgmUPJFBAWmQI607KgcvcL3N1iHue38XfskLSm9sTMFks6XdHY2gOS9tW3V/+8o7MfA8EwpXrPkSkUUtLMoT1GpoCwyBTQmVDTpg+Z2UJJx0g6xswWmtlQtXmbpLPMbG31NZskveTuO6vtD0raaGbLqjcrXiPpgRD7hZQG+MEIETIFhEWmgLDIFDAn1D1UGyUdkbRe0terf94oSe6+X9JaSZslHZD0eUlX1L33FkU3Kk5LelbSVnd/ItB+ITTWqOoXMgWERaaAsMgUUGXuPuh9SG1sbMynpqYGvRvlUVujqn5a9eHhUgwJNLPn3X1s0PvRa2QK/VKGTJEn9BOZAsLqJFP9mOUPRcEaVQAAAMA8FFRoH2tUAQAAAPNQUKF9rFEFAAAAzENBhfaxRhUAAAAwDwUV2scaVQAAAMA8Q61fAtSpVCigAAAAgCp6qBAOa1QBAACgZOihQhiNa1RNT0fPJXq0AAAAUFj0UCEM1qgCAABACVFQIQzWqAIAAEAJUVAhDNaoAgAAQAlRUCEM1qgCAABACVFQIQzWqAIAAEAJMcsfwmGNKgAAAJQMPVQAAAAAkBIFFfqHhX8BAABQMAz5Q3+w8C8AAAAKiB4q9AcL/wIAAKCAKKjQHyz8CwAAgAKioEJ/sPAvAAAACoiCCv3Bwr8AAAAoIAoq9AcL/wIAAKCAmOUP/cPCvwAAACgYeqgAAAAAICUKKmQDi/4CAAAghxjyh8Fj0V8AAADkFD1UGDwW/QUAAEBOUVBh8Fj0FwAAADlFQYXBY9FfAAAA5BQFFQaPRX8BAACQUxRUGDwW/QUAAEBOdV1QmdlxZnafmU2b2Ttm9oKZ/W7Day4ys51mNmNmT5vZaMP77zezt83sDTO7rtt9Qg5VKtKuXdLsbPS1xMUUmQLCIlNAWGQKmC9ED9WQpNckfVHSpyTdLOmvzWylJJnZSZIerW4/UdKUpEfq3n+rpDMkjUq6UNINZnZJgP0C8opMAWGRKSAsMgXU6bqgcvfD7n6ru+9y91l3f1zSq5LOrb7kckk73P3H7v6eohCtMbNV1fYrJd3u7gfc/ZeS7pV0Vbf7BeQVmQLCIlNAWGQKmC/4PVRmdoqk35K0o7pptaQXa+3ufljSryWtNrNlkk6tb6/+eXXo/UKOTU5KK1dKCxZEXycnB71HfUWmgLDIFBAWmULZBS2ozOxYSZOS/tLdd1Y3Hy/pUMNLD0k6odqmhvZaW9zfMW5mU2Y2tX///jA7juyanJTGx6Xpack9+jo+XpqiikwBYfU6U+QJZUOmgDYKKjN7xsw85vFc3esWSHpI0geSrq37Fu9KWtLwbZdIeqfapob2WltT7j7h7mPuPrZ8+fJWu4+827BBmpmZv21mJtqeU2QKCCtLmSJPKAIyBXSmZUHl7he4u8U8zpckMzNJ90k6RdJad/+w7lvskLSm9sTMFks6XdHY2gOS9tW3V/+8Q4Ak7d7d2fYcIFNAWGQKCItMAZ0JNeTvHkm/LelSdz/S0LZN0llmttbMFkraJOmlum7hByVtNLNl1ZsVr5H0QKD9Qt6NjHS2vTjIFBAWmQLCIlNAVYh1qEYl/StJn5X0hpm9W31UJMnd90taK2mzpAOSPi/pirpvcYuiGxWnJT0raau7P9HtfqEgNm+WhofnbxsejrYXFJkCwiJTQFhkCphvqNtv4O7TkqzFa/6bpFUxbe9L+mb1AcxXW+B3w4ZomN/ISFRMFXjhXzIFhEWmgLDIFDBf1wUV0HOVSqELKAAAAORX8HWoAAAAAKAsKKgAAAAAICUKKgAAAABIiYIK+TY5Ka1cKS1YEH2dnBz0HgEAAKBEmJQC+TU5KY2PSzMz0fPp6ei5xCQWAAAA6At6qJBfGzbMFVM1MzPRdgAAAKAPKKiQX7t3d7YdAAAACIyCCvk1MtLZdgAAACAwCirk1+bN0vDw/G3Dw9F2AAAAoA8oqJBflYo0MSGNjkpm0deJCSakAAAAQN8wyx/yrVKhgAIAAMDA0EMFAAAAAClRUAEAAABAShRUAAAAAJASBRWKa3JSWrlSWrAg+jo5Oeg9AgAAQMEwKQWKaXJSGh+XZmai59PT0XOJSSwAAAAQDD1UKKYNG+aKqZqZmWg7AAAAEAgFFYpp9+7OtgMAAAApUFChmEZGOtsOAAAApEBBhWLavFkaHp6/bXg42g4AAAAEQkGFYqpUpIkJaXRUMou+TkwwIQUAAACCYpY/FFelQgEFAACAnqKHCgAAAABSoqACAAAAgJQoqAAAAAAgJQoqAAAAAEiJggrlNDkprVwpLVgQfZ2cHPQeAQAAIIeY5Q/lMzkpjY9LMzPR8+np6LnErIAAAADoCD1UKJ8NG+aKqZqZmWg7AAAA0AEKKpTP7t2dbQcAAABiBCmozOxhM9tnZm+b2a/M7OqG9ovMbKeZzZjZ02Y2Wtd2nJndX33vG2Z2XYh9AmKNjHS2fQDIFBAWmQLCIlPAnFA9VHdKWunuSyT9vqQ7zOxcSTKzkyQ9KulmSSdKmpL0SN17b5V0hqRRSRdKusHMLgm0X8DRNm+WhofnbxsejrZnB5kCwiJTQFhkCqgKUlC5+w53f7/2tPo4vfr8ckk73P3H7v6eohCtMbNV1fYrJd3u7gfc/ZeS7pV0VYj9ApqqVKSJCWl0VDKLvk5MZGpCCjIFhEWmgLDIFDAn2D1UZvafzGxG0k5J+yT9pNq0WtKLtde5+2FJv5a02syWSTq1vr3659Wh9gtoqlKRdu2SZmejrxkqpmrIFBAWmQLCIlNAJNi06e7+b8zs30r6J5IukFS7anG8pP0NLz8k6YRqW+15Y1tTZjYuqTrHtd41s1fa2L2TJL3VxuvKiGMTr/7YjCa9sBfIVG5xbOIVPlPkqSc4PvHIVHP8zsTj2CRLlamWBZWZPSPpizHN2939/NoTd/9Y0nNm9nVJ/1rS9yW9K2lJw/uWSHqn2lZ7/l5DW1PuPiFpotV+N/wMU+4+1sl7yoJjE69Xx4ZMFRvHJl4ZMkWewuP4xCNTsfvP70wMjk2ytMen5ZA/d7/A3S3mcX7M24Y0N452h6Q1dTu6uNq2w90PKOoiXlP33jXV9wCFRKaAsMgUEBaZAjrT9T1UZnaymV1hZseb2TFmdrGkfynpqepLtkk6y8zWmtlCSZskveTuO6vtD0raaGbLqjcrXiPpgW73C8grMgWERaaAsMgUMF+ISSlcURfv65IOSPqupD9x9/8iSe6+X9JaSZur7Z+XdEXd+29RdKPitKRnJW119ycC7Fe9jrqKS4ZjE29Qx4ZM5RvHJh6Zao7fmWQcn3hkqjl+Z+JxbJKlOj7m7qF3BAAAAABKIdi06QAAAABQNhRUAAAAAJBSoQsqMzvRzLaZ2WEzmzazrw16nwbFzK41sykze9/MHmhou8jMdprZjJk9bWZ9X8tikMzsODO7r/o78o6ZvWBmv1vXXurjU49MzSFTzZGn9pGnOeQpHplqH5maQ6bi9SJThS6oJP1A0geSTpFUkXSPmZV1Je69ku6QdH/9RjM7SdKjkm6WdKKkKUmP9H3vBmtI0muK1tz4lKJj8ddmtpLjcxQyNYdMNUee2kee5pCneGSqfWRqDpmKFzxThZ2UwqI1Dw5IOsvdf1Xd9pCkPe6+fqA7N0Bmdoek09z9qurzcUlXufsXqs8XK1oh+py66U1Lx8xekvRtSf9IHB9JZCoOmWqNPB2NPDVHntpDpo5GppojU+3pNlNF7qH6LUkf10JV9aKksl6piLNa0XGRJLn7YUVTmZb2OJnZKZhPqP0AAAH6SURBVIp+f3aI41OPTLWH35k65CkWeWoPvzMNyFQsMtUefmcahMhUkQuq4yUdath2SNIJA9iXLOM41TGzYyVNSvrL6pUIjs8cjkV7OE5V5CkRx6I9HKc6ZCoRx6I9HKc6oTJV5ILqXUlLGrYtkfTOAPYlyzhOVWa2QNJDisZfX1vdzPGZw7FoD8dJ5KkNHIv2cJyqyFRLHIv2cJyqQmaqyAXVryQNmdkZddvWKOrOw5wdio6LpE/Gip6ukh0nMzNJ9ym6kXWtu39YbeL4zCFT7Sn97wx5agt5ag+/MyJTbSJT7eF3RuEzVdiCqjrm8VFJt5nZYjM7T9K/UFSJlo6ZDZnZQknHSDrGzBaa2ZCkbZLOMrO11fZNkl4q4Y2J90j6bUmXuvuRuu0cnyoyNR+ZSkSeWiBP85GnlshUC2RqPjLVUthMuXthH4qmO3xM0mFJuyV9bdD7NMBjcaskb3jcWm37Z5J2Sjoi6RlJKwe9v30+NqPV4/Geoq7e2qPC8TnqWJGpuWNBppofF/LU/rEiT3PHgjzFHxsy1f6xIlNzx4JMxR+b4Jkq7LTpAAAAANBrhR3yBwAAAAC9RkEFAAAAAClRUAEAAABAShRUAAAAAJASBRUAAAAApERBBQAAAAApUVABAAAAQEoUVAAAAACQEgUVAAAAAKT0/wFx0txpoz32QAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x216 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_,axs = plt.subplots(1,4,figsize=(12,3))\n",
"for ax in axs: show_preds(apply_step(params, False), ax)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summarizing gradient descent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MNIST loss function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trgts = tensor([1,0,1])\n",
"prds = tensor([0.9, 0.4, 0.2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mnist_loss(predictions, targets):\n",
" return torch.where(targets==1, 1-predictions, predictions).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0.1000, 0.4000, 0.8000])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.where(trgts==1, 1-prds, prds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.4333)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist_loss(prds,trgts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.2333)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist_loss(tensor([0.9, 0.4, 0.8]),trgts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sigmoid"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sigmoid(x): return 1/(1+torch.exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_function(torch.sigmoid, title='Sigmoid', min=-4, max=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mnist_loss(predictions, targets):\n",
" predictions = predictions.sigmoid()\n",
" return torch.where(targets==1, 1-predictions, predictions).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SGD and mini-batches"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[tensor([10, 13, 0, 4, 5]),\n",
" tensor([ 6, 14, 7, 8, 9]),\n",
" tensor([ 1, 3, 12, 11, 2])]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coll = range(15)\n",
"dl = DataLoader(coll, batch_size=5, shuffle=True)\n",
"list(dl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#26) [(0, 'a'),(1, 'b'),(2, 'c'),(3, 'd'),(4, 'e'),(5, 'f'),(6, 'g'),(7, 'h'),(8, 'i'),(9, 'j')...]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = L(enumerate(string.ascii_lowercase))\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(tensor([ 5, 21, 20, 13, 17, 7]), ('f', 'v', 'u', 'n', 'r', 'h')),\n",
" (tensor([ 4, 3, 9, 18, 11, 24]), ('e', 'd', 'j', 's', 'l', 'y')),\n",
" (tensor([14, 22, 15, 1, 16, 25]), ('o', 'w', 'p', 'b', 'q', 'z')),\n",
" (tensor([ 2, 19, 23, 8, 12, 6]), ('c', 't', 'x', 'i', 'm', 'g')),\n",
" (tensor([ 0, 10]), ('a', 'k'))]"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dl = DataLoader(ds, batch_size=6, shuffle=True)\n",
"list(dl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Putting it all together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_x = torch.cat([stacked_threes, stacked_sevens]).view(-1, 28*28)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([12396, 784]), torch.Size([12396, 1]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_y = tensor([1]*len(threes) + [0]*len(sevens)).unsqueeze(1)\n",
"train_x.shape,train_y.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([784]), tensor([1]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dset = list(zip(train_x,train_y))\n",
"x,y = dset[0]\n",
"x.shape,y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([256, 784]), torch.Size([256, 1]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dl = DataLoader(dset, batch_size=256)\n",
"xb,yb = first(dl)\n",
"xb.shape,yb.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"valid_x = torch.cat([valid_3_tens, valid_7_tens]).view(-1, 28*28)\n",
"valid_y = tensor([1]*len(valid_3_tens) + [0]*len(valid_7_tens)).unsqueeze(1)\n",
"valid_dset = list(zip(valid_x,valid_y))\n",
"valid_dl = DataLoader(valid_dset, batch_size=256)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def init_params(size, std=1.0): return (torch.randn(size)*std).requires_grad_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"weights = init_params((28*28,1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bias = init_params(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([4.5118], grad_fn=<AddBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(train_x[0]*weights.T).sum() + bias"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([4, 784])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch = train_x[:4]\n",
"batch.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 4.5118],\n",
" [ 3.6536],\n",
" [11.2975],\n",
" [14.1164]], grad_fn=<AddBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def linear1(xb): return xb@weights + bias\n",
"preds = linear1(batch)\n",
"preds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.0090, grad_fn=<MeanBackward0>)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss = mnist_loss(preds, train_y[:4])\n",
"loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([784, 1]), tensor(-0.0013), tensor([-0.0088]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loss.backward()\n",
"weights.grad.shape,weights.grad.mean(),bias.grad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def calc_grad(xb, yb, model):\n",
" preds = model(xb)\n",
" loss = mnist_loss(preds, yb)\n",
" loss.backward()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(-0.0025), tensor([-0.0177]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_grad(batch, train_y[:4], linear1)\n",
"weights.grad.mean(),bias.grad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor(-0.0038), tensor([-0.0265]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_grad(batch, train_y[:4], linear1)\n",
"weights.grad.mean(),bias.grad"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"weights.grad.zero_()\n",
"bias.grad.zero_();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train_epoch(model, lr, params):\n",
" for xb,yb in dl:\n",
" calc_grad(xb, yb, model)\n",
" for p in params:\n",
" p.data -= p.grad*lr\n",
" p.grad.zero_()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[True],\n",
" [True],\n",
" [True],\n",
" [True]])"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(preds>0.0).float() == train_y[:4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def batch_accuracy(xb, yb):\n",
" preds = xb.sigmoid()\n",
" correct = (preds>0.5) == yb\n",
" return correct.float().mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(1.)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"batch_accuracy(linear1(batch), train_y[:4])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def validate_epoch(model):\n",
" accs = [batch_accuracy(model(xb), yb) for xb,yb in valid_dl]\n",
" return round(torch.stack(accs).mean().item(), 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4403"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validate_epoch(linear1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4992"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr = 1.\n",
"params = weights,bias\n",
"train_epoch(linear1, lr, params)\n",
"validate_epoch(linear1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.6772 0.8081 0.914 0.9453 0.9565 0.9619 0.9624 0.9633 0.9658 0.9677 0.9702 0.9716 0.9721 0.9736 0.9741 0.9745 0.9765 0.977 0.977 0.9765 "
]
}
],
"source": [
"for i in range(20):\n",
" train_epoch(linear1, lr, params)\n",
" print(validate_epoch(linear1), end=' ')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating an optimizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"linear_model = nn.Linear(28*28,1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([1, 784]), torch.Size([1]))"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w,b = linear_model.parameters()\n",
"w.shape,b.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class BasicOptim:\n",
" def __init__(self,params,lr): self.params,self.lr = list(params),lr\n",
"\n",
" def step(self, *args, **kwargs):\n",
" for p in self.params: p.data -= p.grad.data * self.lr\n",
"\n",
" def zero_grad(self, *args, **kwargs):\n",
" for p in self.params: p.grad = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"opt = BasicOptim(linear_model.parameters(), lr)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train_epoch(model):\n",
" for xb,yb in dl:\n",
" calc_grad(xb, yb, model)\n",
" opt.step()\n",
" opt.zero_grad()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6714"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validate_epoch(linear_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train_model(model, epochs):\n",
" for i in range(epochs):\n",
" train_epoch(model)\n",
" print(validate_epoch(model), end=' ')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.4932 0.7935 0.8477 0.9165 0.9346 0.9482 0.956 0.9634 0.9658 0.9673 0.9702 0.9717 0.9731 0.9751 0.9756 0.9765 0.9775 0.978 0.9785 0.9785 "
]
}
],
"source": [
"train_model(linear_model, 20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.4932 0.771 0.8594 0.918 0.9355 0.9492 0.9575 0.9634 0.9658 0.9682 0.9692 0.9717 0.9731 0.9751 0.9756 0.977 0.977 0.9785 0.9785 0.9785 "
]
}
],
"source": [
"linear_model = nn.Linear(28*28,1)\n",
"opt = SGD(linear_model.parameters(), lr)\n",
"train_model(linear_model, 20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dls = DataLoaders(dl, valid_dl)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = Learner(dls, nn.Linear(28*28,1), opt_func=SGD,\n",
" loss_func=mnist_loss, metrics=batch_accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>batch_accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.636918</td>\n",
" <td>0.503445</td>\n",
" <td>0.495584</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.500283</td>\n",
" <td>0.192597</td>\n",
" <td>0.839549</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.184349</td>\n",
" <td>0.182295</td>\n",
" <td>0.833660</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.081278</td>\n",
" <td>0.107260</td>\n",
" <td>0.912169</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.043316</td>\n",
" <td>0.078320</td>\n",
" <td>0.932777</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.028503</td>\n",
" <td>0.062712</td>\n",
" <td>0.946025</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.022414</td>\n",
" <td>0.052999</td>\n",
" <td>0.955348</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.019704</td>\n",
" <td>0.046531</td>\n",
" <td>0.962218</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.018323</td>\n",
" <td>0.041979</td>\n",
" <td>0.965653</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.017486</td>\n",
" <td>0.038622</td>\n",
" <td>0.966634</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit(10, lr=lr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding a non-linearity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simple_net(xb): \n",
" res = xb@w1 + b1\n",
" res = res.max(tensor(0.0))\n",
" res = res@w2 + b2\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"w1 = init_params((28*28,30))\n",
"b1 = init_params(30)\n",
"w2 = init_params((30,1))\n",
"b2 = init_params(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_function(F.relu)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"simple_net = nn.Sequential(\n",
" nn.Linear(28*28,30),\n",
" nn.ReLU(),\n",
" nn.Linear(30,1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn = Learner(dls, simple_net, opt_func=SGD,\n",
" loss_func=mnist_loss, metrics=batch_accuracy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>batch_accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.294820</td>\n",
" <td>0.416238</td>\n",
" <td>0.504907</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.141692</td>\n",
" <td>0.216893</td>\n",
" <td>0.816487</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.079073</td>\n",
" <td>0.110840</td>\n",
" <td>0.921001</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.052444</td>\n",
" <td>0.075782</td>\n",
" <td>0.941119</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.040078</td>\n",
" <td>0.059658</td>\n",
" <td>0.957802</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.033729</td>\n",
" <td>0.050542</td>\n",
" <td>0.962709</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.030057</td>\n",
" <td>0.044751</td>\n",
" <td>0.965653</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.027653</td>\n",
" <td>0.040775</td>\n",
" <td>0.967615</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.025914</td>\n",
" <td>0.037867</td>\n",
" <td>0.969087</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.024563</td>\n",
" <td>0.035642</td>\n",
" <td>0.970069</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.023465</td>\n",
" <td>0.033873</td>\n",
" <td>0.972031</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.022547</td>\n",
" <td>0.032421</td>\n",
" <td>0.972031</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.021761</td>\n",
" <td>0.031202</td>\n",
" <td>0.973013</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.021081</td>\n",
" <td>0.030153</td>\n",
" <td>0.974485</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.020482</td>\n",
" <td>0.029238</td>\n",
" <td>0.974485</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.019949</td>\n",
" <td>0.028429</td>\n",
" <td>0.975957</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.019472</td>\n",
" <td>0.027706</td>\n",
" <td>0.976938</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.019039</td>\n",
" <td>0.027055</td>\n",
" <td>0.977429</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.018645</td>\n",
" <td>0.026466</td>\n",
" <td>0.977920</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.018283</td>\n",
" <td>0.025931</td>\n",
" <td>0.977920</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.017950</td>\n",
" <td>0.025441</td>\n",
" <td>0.978901</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.017641</td>\n",
" <td>0.024991</td>\n",
" <td>0.979882</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.017353</td>\n",
" <td>0.024576</td>\n",
" <td>0.979882</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.017084</td>\n",
" <td>0.024192</td>\n",
" <td>0.980373</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.016832</td>\n",
" <td>0.023837</td>\n",
" <td>0.980864</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.016595</td>\n",
" <td>0.023506</td>\n",
" <td>0.981354</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.016371</td>\n",
" <td>0.023198</td>\n",
" <td>0.981354</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.016159</td>\n",
" <td>0.022910</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.015959</td>\n",
" <td>0.022641</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.015768</td>\n",
" <td>0.022389</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.015587</td>\n",
" <td>0.022154</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.015414</td>\n",
" <td>0.021932</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.015249</td>\n",
" <td>0.021725</td>\n",
" <td>0.981845</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.015092</td>\n",
" <td>0.021529</td>\n",
" <td>0.982336</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.014941</td>\n",
" <td>0.021345</td>\n",
" <td>0.982336</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.014796</td>\n",
" <td>0.021171</td>\n",
" <td>0.982826</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.014658</td>\n",
" <td>0.021007</td>\n",
" <td>0.982826</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.014524</td>\n",
" <td>0.020852</td>\n",
" <td>0.982826</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.014396</td>\n",
" <td>0.020704</td>\n",
" <td>0.983317</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.014272</td>\n",
" <td>0.020564</td>\n",
" <td>0.983317</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit(40, 0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(L(learn.recorder.values).itemgot(2));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.983316957950592"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.recorder.values[-1][2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.125685</td>\n",
" <td>0.026256</td>\n",
" <td>0.992640</td>\n",
" <td>00:11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dls = ImageDataLoaders.from_folder(path)\n",
"learn = cnn_learner(dls, resnet18, pretrained=False,\n",
" loss_func=F.cross_entropy, metrics=accuracy)\n",
"learn.fit_one_cycle(1, 0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jargon recap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### _Choose Your Own Adventure_ reminder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questionnaire"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Further research"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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