{ "cells": [ { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import sklearn.model_selection\n", "from datasets import load_dataset" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Found cached dataset wine (C:/Users/macty/.cache/huggingface/datasets/mstz___wine/wine/1.0.0/7c3844cac7ac7a22d5fbbaf60fc1d4e9c9deb1b9b9c4dbae6a7b1a962dbc96d8)\n", "100%|██████████| 1/1 [00:00<00:00, 500.10it/s]\n" ] } ], "source": [ "dataset = load_dataset(\"mstz/wine\", \"wine\")" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality', 'is_red'],\n", " num_rows: 6497\n", "})" ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"train\"]" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [], "source": [ "wine_dataset = pd.DataFrame(dataset[\"train\"])" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
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47.40.700.001.90.07611.034.00.99783.510.569.450
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" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n", "0 7.4 0.70 0.00 1.9 0.076 \n", "1 7.8 0.88 0.00 2.6 0.098 \n", "2 7.8 0.76 0.04 2.3 0.092 \n", "3 11.2 0.28 0.56 1.9 0.075 \n", "4 7.4 0.70 0.00 1.9 0.076 \n", "\n", " free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n", "0 11.0 34.0 0.9978 3.51 0.56 \n", "1 25.0 67.0 0.9968 3.20 0.68 \n", "2 15.0 54.0 0.9970 3.26 0.65 \n", "3 17.0 60.0 0.9980 3.16 0.58 \n", "4 11.0 34.0 0.9978 3.51 0.56 \n", "\n", " alcohol quality is_red \n", "0 9.4 5 0 \n", "1 9.8 5 0 \n", "2 9.8 5 0 \n", "3 9.8 6 0 \n", "4 9.4 5 0 " ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_dataset.head()# podgląd danych" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
count6497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.000000
mean7.2153070.3396660.3186335.4432350.05603430.525319115.7445740.9946973.2185010.53126810.4918015.8183780.753886
std1.2964340.1646360.1453184.7578040.03503417.74940056.5218550.0029990.1607870.1488061.1927120.8732550.430779
min3.8000000.0800000.0000000.6000000.0090001.0000006.0000000.9871102.7200000.2200008.0000003.0000000.000000
25%6.4000000.2300000.2500001.8000000.03800017.00000077.0000000.9923403.1100000.4300009.5000005.0000001.000000
50%7.0000000.2900000.3100003.0000000.04700029.000000118.0000000.9948903.2100000.51000010.3000006.0000001.000000
75%7.7000000.4000000.3900008.1000000.06500041.000000156.0000000.9969903.3200000.60000011.3000006.0000001.000000
max15.9000001.5800001.66000065.8000000.611000289.000000440.0000001.0389804.0100002.00000014.9000009.0000001.000000
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" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar \\\n", "count 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 7.215307 0.339666 0.318633 5.443235 \n", "std 1.296434 0.164636 0.145318 4.757804 \n", "min 3.800000 0.080000 0.000000 0.600000 \n", "25% 6.400000 0.230000 0.250000 1.800000 \n", "50% 7.000000 0.290000 0.310000 3.000000 \n", "75% 7.700000 0.400000 0.390000 8.100000 \n", "max 15.900000 1.580000 1.660000 65.800000 \n", "\n", " chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n", "count 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 0.056034 30.525319 115.744574 0.994697 \n", "std 0.035034 17.749400 56.521855 0.002999 \n", "min 0.009000 1.000000 6.000000 0.987110 \n", "25% 0.038000 17.000000 77.000000 0.992340 \n", "50% 0.047000 29.000000 118.000000 0.994890 \n", "75% 0.065000 41.000000 156.000000 0.996990 \n", "max 0.611000 289.000000 440.000000 1.038980 \n", "\n", " pH sulphates alcohol quality is_red \n", "count 6497.000000 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 3.218501 0.531268 10.491801 5.818378 0.753886 \n", "std 0.160787 0.148806 1.192712 0.873255 0.430779 \n", "min 2.720000 0.220000 8.000000 3.000000 0.000000 \n", "25% 3.110000 0.430000 9.500000 5.000000 1.000000 \n", "50% 3.210000 0.510000 10.300000 6.000000 1.000000 \n", "75% 3.320000 0.600000 11.300000 6.000000 1.000000 \n", "max 4.010000 2.000000 14.900000 9.000000 1.000000 " ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_dataset.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine_dataset[\"is_red\"].value_counts().plot(kind=\"bar\")\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.2964337577998153" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_dataset[\"fixed_acidity\"].std()" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([], dtype=int64), array([], dtype=int64))" ] }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.where(pd.isnull(wine_dataset))## sprawdzanie czy istnieją puste wartości" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "for column in wine_dataset.columns:\n", " wine_dataset[column] = wine_dataset[column] / wine_dataset[column].abs().max() # normalizacja" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
count6497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.0000006497.000000
mean0.4537930.2149780.1919480.0827240.0917080.1056240.2630560.9573780.8026190.2656340.7041480.6464860.753886
std0.0815370.1042000.0875410.0723070.0573380.0614170.1284590.0028860.0400970.0744030.0800480.0970280.430779
min0.2389940.0506330.0000000.0091190.0147300.0034600.0136360.9500760.6783040.1100000.5369130.3333330.000000
25%0.4025160.1455700.1506020.0273560.0621930.0588240.1750000.9551100.7755610.2150000.6375840.5555561.000000
50%0.4402520.1835440.1867470.0455930.0769230.1003460.2681820.9575640.8004990.2550000.6912750.6666671.000000
75%0.4842770.2531650.2349400.1231000.1063830.1418690.3545450.9595850.8279300.3000000.7583890.6666671.000000
max1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", "
" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar \\\n", "count 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 0.453793 0.214978 0.191948 0.082724 \n", "std 0.081537 0.104200 0.087541 0.072307 \n", "min 0.238994 0.050633 0.000000 0.009119 \n", "25% 0.402516 0.145570 0.150602 0.027356 \n", "50% 0.440252 0.183544 0.186747 0.045593 \n", "75% 0.484277 0.253165 0.234940 0.123100 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n", "count 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 0.091708 0.105624 0.263056 0.957378 \n", "std 0.057338 0.061417 0.128459 0.002886 \n", "min 0.014730 0.003460 0.013636 0.950076 \n", "25% 0.062193 0.058824 0.175000 0.955110 \n", "50% 0.076923 0.100346 0.268182 0.957564 \n", "75% 0.106383 0.141869 0.354545 0.959585 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " pH sulphates alcohol quality is_red \n", "count 6497.000000 6497.000000 6497.000000 6497.000000 6497.000000 \n", "mean 0.802619 0.265634 0.704148 0.646486 0.753886 \n", "std 0.040097 0.074403 0.080048 0.097028 0.430779 \n", "min 0.678304 0.110000 0.536913 0.333333 0.000000 \n", "25% 0.775561 0.215000 0.637584 0.555556 1.000000 \n", "50% 0.800499 0.255000 0.691275 0.666667 1.000000 \n", "75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 " ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_dataset.describe(include='all') # sprawdzanie wartości po znormalizowaniu" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "652 1.000000\n", "442 0.981132\n", "557 0.981132\n", "554 0.974843\n", "555 0.974843\n", "243 0.943396\n", "244 0.943396\n", "544 0.899371\n", "3125 0.893082\n", "374 0.880503\n", "Name: fixed_acidity, dtype: float64" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_dataset[\"fixed_acidity\"].nlargest(10) #sprawdza czy najwyższe wartości mają sens" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 4408\n", "0.0 1439\n", "Name: is_red, dtype: int64" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset[\"is_red\"])\n", "wine_train[\"is_red\"].value_counts() \n", "# podzielenie na train i test" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 490\n", "0.0 160\n", "Name: is_red, dtype: int64" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_test[\"is_red\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [], "source": [ "wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test[\"is_red\"]) # podzielenie na test i validation" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 245\n", "0.0 80\n", "Name: is_red, dtype: int64" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_test[\"is_red\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 245\n", "0.0 80\n", "Name: is_red, dtype: int64" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_val[\"is_red\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_theme()" ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(wine_dataset.columns)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [], "source": [ "#sns.pairplot(data=wine_dataset, hue=\"is_red\")" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
count325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000
mean0.4482440.2170690.1806300.0789900.0887420.1030240.2574620.9572550.8035530.2638770.7039300.6461540.753846
std0.0743010.1076270.0780460.0700450.0514000.0547500.1251650.0027860.0398080.0722750.0787040.0950140.431433
min0.3144650.0632910.0000000.0121580.0310970.0103810.0204550.9511160.7132170.1300000.5704700.3333330.000000
25%0.4025160.1455700.1445780.0273560.0605560.0588240.1681820.9551680.7755610.2100000.6375840.5555561.000000
50%0.4339620.1772150.1807230.0425530.0785600.1003460.2613640.9574780.8004990.2500000.6912750.6666671.000000
75%0.4716980.2531650.2228920.1139820.1014730.1418690.3431820.9593540.8279300.3000000.7583890.6666671.000000
max0.8176100.5696200.4457830.3343470.6792140.2318340.5750000.9652640.9177060.5850000.9395971.0000001.000000
\n", "
" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar \\\n", "count 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.448244 0.217069 0.180630 0.078990 \n", "std 0.074301 0.107627 0.078046 0.070045 \n", "min 0.314465 0.063291 0.000000 0.012158 \n", "25% 0.402516 0.145570 0.144578 0.027356 \n", "50% 0.433962 0.177215 0.180723 0.042553 \n", "75% 0.471698 0.253165 0.222892 0.113982 \n", "max 0.817610 0.569620 0.445783 0.334347 \n", "\n", " chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n", "count 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.088742 0.103024 0.257462 0.957255 \n", "std 0.051400 0.054750 0.125165 0.002786 \n", "min 0.031097 0.010381 0.020455 0.951116 \n", "25% 0.060556 0.058824 0.168182 0.955168 \n", "50% 0.078560 0.100346 0.261364 0.957478 \n", "75% 0.101473 0.141869 0.343182 0.959354 \n", "max 0.679214 0.231834 0.575000 0.965264 \n", "\n", " pH sulphates alcohol quality is_red \n", "count 325.000000 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.803553 0.263877 0.703930 0.646154 0.753846 \n", "std 0.039808 0.072275 0.078704 0.095014 0.431433 \n", "min 0.713217 0.130000 0.570470 0.333333 0.000000 \n", "25% 0.775561 0.210000 0.637584 0.555556 1.000000 \n", "50% 0.800499 0.250000 0.691275 0.666667 1.000000 \n", "75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n", "max 0.917706 0.585000 0.939597 1.000000 1.000000 " ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_test.describe()" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
count5847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.0000005847.000000
mean0.4538480.2150610.1922350.0823310.0921610.1056590.2628940.9573640.8025690.2657980.7043260.6467320.753891
std0.0817420.1043150.0880360.0719820.0586190.0617490.1282560.0028820.0398800.0748640.0798520.0969280.430780
min0.2389940.0506330.0000000.0091190.0147300.0034600.0136360.9500760.6783040.1100000.5369130.3333330.000000
25%0.4025160.1455700.1506020.0273560.0621930.0588240.1761360.9550710.7755610.2150000.6375840.5555561.000000
50%0.4402520.1835440.1867470.0455930.0769230.1003460.2681820.9575160.8004990.2550000.6912750.6666671.000000
75%0.4842770.2531650.2349400.1231000.1063830.1418690.3534090.9595810.8279300.3000000.7583890.6666671.000000
max1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
\n", "
" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar \\\n", "count 5847.000000 5847.000000 5847.000000 5847.000000 \n", "mean 0.453848 0.215061 0.192235 0.082331 \n", "std 0.081742 0.104315 0.088036 0.071982 \n", "min 0.238994 0.050633 0.000000 0.009119 \n", "25% 0.402516 0.145570 0.150602 0.027356 \n", "50% 0.440252 0.183544 0.186747 0.045593 \n", "75% 0.484277 0.253165 0.234940 0.123100 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n", "count 5847.000000 5847.000000 5847.000000 5847.000000 \n", "mean 0.092161 0.105659 0.262894 0.957364 \n", "std 0.058619 0.061749 0.128256 0.002882 \n", "min 0.014730 0.003460 0.013636 0.950076 \n", "25% 0.062193 0.058824 0.176136 0.955071 \n", "50% 0.076923 0.100346 0.268182 0.957516 \n", "75% 0.106383 0.141869 0.353409 0.959581 \n", "max 1.000000 1.000000 1.000000 1.000000 \n", "\n", " pH sulphates alcohol quality is_red \n", "count 5847.000000 5847.000000 5847.000000 5847.000000 5847.000000 \n", "mean 0.802569 0.265798 0.704326 0.646732 0.753891 \n", "std 0.039880 0.074864 0.079852 0.096928 0.430780 \n", "min 0.678304 0.110000 0.536913 0.333333 0.000000 \n", "25% 0.775561 0.215000 0.637584 0.555556 1.000000 \n", "50% 0.800499 0.255000 0.691275 0.666667 1.000000 \n", "75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 " ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_train.describe()" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityis_red
count325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000325.000000
mean0.4583550.2114120.1980910.0935210.0865370.1075960.2715560.9577570.8025700.2644460.7011600.6423930.753846
std0.0846210.0987490.0868620.0793460.0351410.0618050.1351850.0030310.0441830.0680860.0849390.1009570.431433
min0.2955970.0569620.0000000.0121580.0196400.0103810.0181820.9504130.7157110.1400000.5637580.3333330.000000
25%0.4025160.1455700.1566270.0303950.0638300.0553630.1795450.9554560.7730670.2150000.6308720.5555561.000000
50%0.4465410.1835440.1867470.0691490.0785600.1003460.2840910.9579780.8004990.2500000.6845640.6666671.000000
75%0.4905660.2531650.2409640.1337390.0982000.1557090.3704550.9600280.8279300.3050000.7583890.6666671.000000
max0.9433960.7468350.4457830.4802430.2782320.2664360.5704550.9723961.0000000.5700000.9395970.8888891.000000
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" ], "text/plain": [ " fixed_acidity volatile_acidity citric_acid residual_sugar \\\n", "count 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.458355 0.211412 0.198091 0.093521 \n", "std 0.084621 0.098749 0.086862 0.079346 \n", "min 0.295597 0.056962 0.000000 0.012158 \n", "25% 0.402516 0.145570 0.156627 0.030395 \n", "50% 0.446541 0.183544 0.186747 0.069149 \n", "75% 0.490566 0.253165 0.240964 0.133739 \n", "max 0.943396 0.746835 0.445783 0.480243 \n", "\n", " chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n", "count 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.086537 0.107596 0.271556 0.957757 \n", "std 0.035141 0.061805 0.135185 0.003031 \n", "min 0.019640 0.010381 0.018182 0.950413 \n", "25% 0.063830 0.055363 0.179545 0.955456 \n", "50% 0.078560 0.100346 0.284091 0.957978 \n", "75% 0.098200 0.155709 0.370455 0.960028 \n", "max 0.278232 0.266436 0.570455 0.972396 \n", "\n", " pH sulphates alcohol quality is_red \n", "count 325.000000 325.000000 325.000000 325.000000 325.000000 \n", "mean 0.802570 0.264446 0.701160 0.642393 0.753846 \n", "std 0.044183 0.068086 0.084939 0.100957 0.431433 \n", "min 0.715711 0.140000 0.563758 0.333333 0.000000 \n", "25% 0.773067 0.215000 0.630872 0.555556 1.000000 \n", "50% 0.800499 0.250000 0.684564 0.666667 1.000000 \n", "75% 0.827930 0.305000 0.758389 0.666667 1.000000 \n", "max 1.000000 0.570000 0.939597 0.888889 1.000000 " ] }, "execution_count": 204, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_val.describe()" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [], "source": [ "import torch\n", "from torch import nn\n", "from torch.utils.data import DataLoader, Dataset" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [], "source": [ "class TabularDataset(Dataset):\n", " def __init__(self, data):\n", " self.data = data.values.astype('float32')\n", "\n", " def __getitem__(self, index):\n", " x = torch.tensor(self.data[index, :-1])\n", " y = torch.tensor(self.data[index, -1])\n", " return x, y\n", "\n", " def __len__(self):\n", " return len(self.data)" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [], "source": [ "batch_size = 64\n", "train_dataset = TabularDataset(wine_train)\n", "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", "test_dataset = TabularDataset(wine_test)\n", "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "class TabularModel(nn.Module):\n", " def __init__(self, input_dim, hidden_dim, output_dim):\n", " super(TabularModel, self).__init__()\n", " self.fc1 = nn.Linear(input_dim, hidden_dim)\n", " self.relu = nn.ReLU()\n", " self.fc2 = nn.Linear(hidden_dim, output_dim)\n", " self.softmax = nn.Softmax(dim=1)\n", " \n", " def forward(self, x):\n", " out = self.fc1(x)\n", " out = self.relu(out)\n", " out = self.fc2(out)\n", " out = self.softmax(out)\n", " return out" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "input_dim = wine_train.shape[1] - 1\n", "hidden_dim = 32\n", "output_dim = 2\n", "model = TabularModel(input_dim, hidden_dim, output_dim)\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = torch.optim.Adam(model.parameters())" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [], "source": [ "model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=32, output_dim=2)\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[0.3481, 0.6519],\n", " [0.3542, 0.6458],\n", " 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9.9955e-01],\n", " [9.7316e-01, 2.6838e-02],\n", " [9.8779e-01, 1.2210e-02],\n", " [9.8779e-01, 1.2210e-02],\n", " [1.7848e-03, 9.9822e-01],\n", " [2.3148e-03, 9.9769e-01],\n", " [9.9596e-01, 4.0384e-03],\n", " [4.4047e-03, 9.9560e-01],\n", " [3.0841e-03, 9.9692e-01],\n", " [3.5615e-04, 9.9964e-01],\n", " [9.5472e-01, 4.5277e-02],\n", " [4.5747e-03, 9.9543e-01],\n", " [5.8169e-03, 9.9418e-01],\n", " [4.4226e-04, 9.9956e-01],\n", " [1.0649e-01, 8.9351e-01],\n", " [3.1887e-04, 9.9968e-01],\n", " [9.5619e-01, 4.3805e-02],\n", " [1.4280e-03, 9.9857e-01],\n", " [2.1977e-04, 9.9978e-01],\n", " [1.0070e-03, 9.9899e-01],\n", " [8.0497e-02, 9.1950e-01],\n", " [1.5383e-03, 9.9846e-01],\n", " [9.9666e-01, 3.3424e-03],\n", " [1.4943e-05, 9.9999e-01],\n", " [1.0271e-02, 9.8973e-01],\n", " [9.9250e-01, 7.4986e-03],\n", " [3.6976e-05, 9.9996e-01],\n", " [2.4828e-03, 9.9752e-01],\n", " [9.6655e-04, 9.9903e-01],\n", " [9.0903e-01, 9.0970e-02],\n", " [9.9146e-01, 8.5450e-03],\n", " [1.4143e-02, 9.8586e-01],\n", " [1.9733e-03, 9.9803e-01],\n", " [6.1057e-03, 9.9389e-01],\n", " [2.4914e-01, 7.5086e-01],\n", " [1.7626e-02, 9.8237e-01],\n", " [4.7076e-04, 9.9953e-01],\n", " [2.5157e-04, 9.9975e-01],\n", " [8.8862e-01, 1.1138e-01],\n", " [6.7045e-03, 9.9330e-01],\n", " [4.0782e-03, 9.9592e-01],\n", " [2.1458e-03, 9.9785e-01],\n", " [2.8065e-04, 9.9972e-01],\n", " [9.1160e-05, 9.9991e-01],\n", " [8.3275e-02, 9.1672e-01],\n", " [5.9047e-03, 9.9410e-01],\n", " [1.9583e-01, 8.0417e-01],\n", " [1.2647e-04, 9.9987e-01],\n", " [6.1681e-03, 9.9383e-01],\n", " [1.1079e-02, 9.8892e-01],\n", " [9.9610e-01, 3.9036e-03],\n", " [6.7882e-03, 9.9321e-01],\n", " [8.4967e-01, 1.5033e-01],\n", " [9.7952e-01, 2.0480e-02],\n", " [6.3102e-05, 9.9994e-01],\n", " [5.1083e-01, 4.8917e-01],\n", " [1.1721e-01, 8.8279e-01],\n", " [2.3085e-02, 9.7692e-01],\n", " [4.9637e-03, 9.9504e-01],\n", " [9.7536e-01, 2.4635e-02],\n", " [2.2152e-04, 9.9978e-01],\n", " [8.7806e-01, 1.2194e-01]], grad_fn=)\n", 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4.1946e-04],\n", " [1.4788e-04, 9.9985e-01],\n", " [2.3514e-06, 1.0000e+00],\n", " [2.3059e-07, 1.0000e+00]], grad_fn=)\n", "Epoch 5, loss: 0.3333\n", "tensor([[1.8384e-06, 1.0000e+00],\n", " [9.9368e-08, 1.0000e+00],\n", " [1.4667e-08, 1.0000e+00],\n", " [9.9988e-01, 1.1644e-04],\n", " [4.5409e-03, 9.9546e-01],\n", " [2.4269e-08, 1.0000e+00],\n", " [2.9496e-09, 1.0000e+00],\n", " [2.0250e-02, 9.7975e-01],\n", " [8.6884e-03, 9.9131e-01],\n", " [3.4345e-02, 9.6565e-01],\n", " [5.8906e-07, 1.0000e+00],\n", " [9.8673e-03, 9.9013e-01],\n", " [7.6039e-04, 9.9924e-01],\n", " [4.5641e-07, 1.0000e+00],\n", " [4.5702e-08, 1.0000e+00],\n", " [3.0380e-04, 9.9970e-01],\n", " [6.8699e-05, 9.9993e-01],\n", " [1.5577e-03, 9.9844e-01],\n", " [9.9510e-07, 1.0000e+00],\n", " [8.6406e-01, 1.3594e-01],\n", " [6.2319e-06, 9.9999e-01],\n", " [9.9979e-01, 2.1462e-04],\n", " [1.0708e-04, 9.9989e-01],\n", " [2.1072e-03, 9.9789e-01],\n", " [1.0000e+00, 5.7493e-07],\n", " [1.0383e-02, 9.8962e-01],\n", " 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1.4116e-01],\n", " [1.4560e-04, 9.9985e-01],\n", " [1.0760e-04, 9.9989e-01],\n", " [1.5437e-08, 1.0000e+00],\n", " [8.8908e-05, 9.9991e-01],\n", " [9.8491e-07, 1.0000e+00],\n", " [9.9694e-01, 3.0556e-03],\n", " [3.2841e-04, 9.9967e-01],\n", " [4.4795e-08, 1.0000e+00],\n", " [9.9185e-01, 8.1548e-03]], grad_fn=)\n", "tensor([[1.4180e-01, 8.5820e-01],\n", " [9.9962e-01, 3.7891e-04],\n", " [9.9249e-01, 7.5126e-03],\n", " [9.9963e-01, 3.7482e-04],\n", " [2.9551e-05, 9.9997e-01],\n", " [1.7173e-06, 1.0000e+00],\n", " [1.6124e-03, 9.9839e-01],\n", " [5.1558e-08, 1.0000e+00],\n", " [9.9157e-05, 9.9990e-01],\n", " [1.2268e-03, 9.9877e-01],\n", " [1.0361e-04, 9.9990e-01],\n", " [9.9980e-01, 1.9567e-04],\n", " [9.9973e-01, 2.6517e-04],\n", " [2.9407e-06, 1.0000e+00],\n", " [9.9978e-01, 2.2274e-04],\n", " [3.9762e-04, 9.9960e-01],\n", " [3.7774e-05, 9.9996e-01],\n", " [1.6360e-08, 1.0000e+00],\n", " [9.9989e-01, 1.1303e-04],\n", " [2.1290e-06, 1.0000e+00],\n", " [9.8483e-01, 1.5172e-02],\n", " 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1.2320e-04],\n", " [9.9950e-01, 5.0344e-04],\n", " [9.9973e-01, 2.7147e-04],\n", " [2.1138e-03, 9.9789e-01],\n", " [1.0000e+00, 2.9969e-07],\n", " [1.2976e-04, 9.9987e-01],\n", " [4.1379e-05, 9.9996e-01],\n", " [1.0490e-07, 1.0000e+00],\n", " [7.1857e-02, 9.2814e-01],\n", " [9.9596e-01, 4.0433e-03],\n", " [1.8240e-06, 1.0000e+00],\n", " [2.3017e-06, 1.0000e+00],\n", " [3.2816e-06, 1.0000e+00],\n", " [2.8746e-07, 1.0000e+00],\n", " [4.9035e-04, 9.9951e-01]], grad_fn=)\n", "tensor([[2.4041e-05, 9.9998e-01],\n", " [9.8181e-03, 9.9018e-01],\n", " [7.0532e-06, 9.9999e-01],\n", " [1.3318e-07, 1.0000e+00],\n", " [3.2521e-04, 9.9967e-01],\n", " [3.0009e-07, 1.0000e+00],\n", " [3.1202e-06, 1.0000e+00],\n", " [2.6972e-05, 9.9997e-01],\n", " [6.2126e-04, 9.9938e-01],\n", " [8.5538e-06, 9.9999e-01],\n", " [9.9979e-01, 2.1361e-04],\n", " [2.3285e-03, 9.9767e-01],\n", " [1.2538e-03, 9.9875e-01],\n", " [6.8936e-04, 9.9931e-01],\n", " [1.8360e-05, 9.9998e-01],\n", " [1.9677e-02, 9.8032e-01],\n", " 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9.9991e-01],\n", " [1.6294e-04, 9.9984e-01],\n", " [1.6357e-05, 9.9998e-01],\n", " [3.4977e-07, 1.0000e+00],\n", " [8.4491e-11, 1.0000e+00],\n", " [2.1605e-07, 1.0000e+00],\n", " [3.9322e-10, 1.0000e+00],\n", " [9.9996e-01, 3.7495e-05],\n", " [1.4835e-06, 1.0000e+00],\n", " [9.9992e-01, 8.1115e-05],\n", " [9.9312e-01, 6.8796e-03],\n", " [9.9997e-01, 3.1288e-05],\n", " [2.8013e-08, 1.0000e+00],\n", " [3.8737e-04, 9.9961e-01],\n", " [9.8527e-01, 1.4732e-02],\n", " [1.8600e-04, 9.9981e-01],\n", " [9.9975e-01, 2.4987e-04],\n", " [8.5068e-09, 1.0000e+00],\n", " [9.9979e-01, 2.1063e-04]], grad_fn=)\n", "tensor([[1.9091e-05, 9.9998e-01],\n", " [9.9550e-01, 4.4997e-03],\n", " [6.8171e-04, 9.9932e-01],\n", " [9.9785e-01, 2.1466e-03],\n", " [9.9689e-01, 3.1145e-03],\n", " [9.9991e-01, 9.4169e-05],\n", " [4.4717e-03, 9.9553e-01],\n", " [9.9718e-01, 2.8228e-03],\n", " [1.5223e-07, 1.0000e+00],\n", " [7.6451e-06, 9.9999e-01],\n", " [2.0652e-07, 1.0000e+00],\n", " [9.9447e-06, 9.9999e-01],\n", " [9.9994e-01, 6.1389e-05],\n", " [1.6863e-08, 1.0000e+00],\n", " [5.8476e-05, 9.9994e-01],\n", " [9.9979e-01, 2.0847e-04],\n", " [7.4127e-05, 9.9993e-01],\n", " [3.8302e-08, 1.0000e+00],\n", " [3.1945e-06, 1.0000e+00],\n", " [9.9811e-01, 1.8854e-03],\n", " [9.9996e-01, 4.2112e-05],\n", " [9.8002e-01, 1.9979e-02],\n", " [9.9986e-01, 1.3675e-04]], grad_fn=)\n", "Epoch 9, loss: 0.3318\n", "tensor([[1.3097e-08, 1.0000e+00],\n", " [4.3593e-06, 1.0000e+00],\n", " [2.9546e-07, 1.0000e+00],\n", " [4.4692e-06, 1.0000e+00],\n", " [2.1499e-08, 1.0000e+00],\n", " [5.7561e-09, 1.0000e+00],\n", " [1.2878e-01, 8.7122e-01],\n", " [2.9655e-10, 1.0000e+00],\n", " [9.6827e-04, 9.9903e-01],\n", " [9.9093e-01, 9.0702e-03],\n", " [5.5616e-09, 1.0000e+00],\n", " [1.5966e-07, 1.0000e+00],\n", " [8.3907e-01, 1.6093e-01],\n", " [9.9945e-01, 5.4953e-04],\n", " [9.6706e-06, 9.9999e-01],\n", " [5.9649e-06, 9.9999e-01],\n", " [1.0690e-11, 1.0000e+00],\n", " [1.0947e-05, 9.9999e-01],\n", " [6.4260e-05, 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5.7316e-05],\n", " [5.2976e-06, 9.9999e-01],\n", " [4.3932e-05, 9.9996e-01],\n", " [2.3389e-08, 1.0000e+00],\n", " [1.6708e-10, 1.0000e+00],\n", " [9.9867e-01, 1.3250e-03],\n", " [5.9271e-12, 1.0000e+00],\n", " [9.9977e-01, 2.3309e-04],\n", " [9.9973e-01, 2.7066e-04],\n", " [9.9986e-01, 1.3647e-04],\n", " [4.3629e-08, 1.0000e+00],\n", " [7.4866e-08, 1.0000e+00],\n", " [3.0407e-07, 1.0000e+00],\n", " [7.1399e-06, 9.9999e-01],\n", " [4.4710e-09, 1.0000e+00]], grad_fn=)\n", "tensor([[3.9524e-10, 1.0000e+00],\n", " [2.2145e-05, 9.9998e-01],\n", " [3.5451e-07, 1.0000e+00],\n", " [1.1105e-08, 1.0000e+00],\n", " [2.7099e-08, 1.0000e+00],\n", " [8.8058e-01, 1.1942e-01],\n", " [9.9438e-01, 5.6162e-03],\n", " [4.2115e-11, 1.0000e+00],\n", " [9.9991e-01, 9.0667e-05],\n", " [1.3165e-03, 9.9868e-01],\n", " [6.1541e-07, 1.0000e+00],\n", " [3.4375e-07, 1.0000e+00],\n", " [9.6478e-01, 3.5222e-02],\n", " [3.1094e-04, 9.9969e-01],\n", " [2.2407e-05, 9.9998e-01],\n", " [2.3149e-09, 1.0000e+00],\n", " 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[1.1214e-09, 1.0000e+00],\n", " [3.3802e-01, 6.6198e-01],\n", " [3.0414e-04, 9.9970e-01],\n", " [1.4805e-06, 1.0000e+00],\n", " [9.9998e-01, 1.9558e-05],\n", " [9.9987e-01, 1.2636e-04],\n", " [2.1138e-07, 1.0000e+00],\n", " [1.8424e-02, 9.8158e-01],\n", " [1.7303e-08, 1.0000e+00],\n", " [2.1627e-08, 1.0000e+00],\n", " [1.1591e-05, 9.9999e-01],\n", " [3.6747e-10, 1.0000e+00],\n", " [1.1497e-10, 1.0000e+00],\n", " [9.2001e-08, 1.0000e+00],\n", " [8.2934e-06, 9.9999e-01],\n", " [2.1525e-08, 1.0000e+00],\n", " [4.4200e-04, 9.9956e-01],\n", " [5.4454e-07, 1.0000e+00],\n", " [1.4876e-06, 1.0000e+00],\n", " [5.5135e-08, 1.0000e+00],\n", " [2.3807e-04, 9.9976e-01]], grad_fn=)\n", "tensor([[1.2369e-08, 1.0000e+00],\n", " [6.6178e-09, 1.0000e+00],\n", " [2.2626e-09, 1.0000e+00],\n", " [1.9047e-12, 1.0000e+00],\n", " [2.0730e-10, 1.0000e+00],\n", " [9.7042e-11, 1.0000e+00],\n", " [9.9989e-01, 1.1349e-04],\n", " [3.0380e-05, 9.9997e-01],\n", " [1.0908e-04, 9.9989e-01],\n", " [2.4768e-07, 1.0000e+00],\n", " [2.5424e-07, 1.0000e+00],\n", " [1.7181e-05, 9.9998e-01],\n", " [7.5548e-08, 1.0000e+00],\n", " [2.2445e-03, 9.9776e-01],\n", " [1.4009e-07, 1.0000e+00],\n", " [1.1239e-10, 1.0000e+00],\n", " [8.7819e-01, 1.2181e-01],\n", " [9.9988e-01, 1.1497e-04],\n", " [5.3894e-10, 1.0000e+00],\n", " [9.9992e-01, 8.2254e-05],\n", " [1.5670e-09, 1.0000e+00],\n", " [9.8786e-01, 1.2141e-02],\n", " [1.0672e-07, 1.0000e+00]], grad_fn=)\n", "Finished Training\n" ] } ], "source": [ "num_epochs = 10\n", "for epoch in range(num_epochs):\n", " running_loss = 0.0\n", " for i, data in enumerate(train_dataloader, 0):\n", " inputs, labels = data\n", " labels = labels.type(torch.LongTensor)\n", " optimizer.zero_grad()\n", " outputs = model(inputs)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", " running_loss += loss.item()\n", "\n", " # Print the loss every 1000 mini-batches\n", " if (epoch%2) == 0:\n", " print(f'Epoch {epoch + 1}, loss: {running_loss / len(train_dataloader):.4f}')\n", "\n", "print('Finished Training')" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[1.7202e-10, 1.0000e+00],\n", " [1.2536e-05, 9.9999e-01],\n", " [1.6356e-06, 1.0000e+00],\n", " [8.5210e-09, 1.0000e+00],\n", " [2.6330e-06, 1.0000e+00],\n", " [3.1719e-11, 1.0000e+00],\n", " [7.5173e-12, 1.0000e+00],\n", " [1.4972e-09, 1.0000e+00],\n", " [9.9978e-01, 2.1951e-04],\n", " [9.9896e-01, 1.0420e-03],\n", " [2.6575e-11, 1.0000e+00],\n", " [3.9438e-01, 6.0562e-01],\n", " [2.0028e-10, 1.0000e+00],\n", " [2.2875e-11, 1.0000e+00],\n", " [9.0060e-01, 9.9397e-02],\n", " [1.6835e-04, 9.9983e-01],\n", " [6.5582e-07, 1.0000e+00],\n", " [1.1467e-06, 1.0000e+00],\n", " [7.9620e-11, 1.0000e+00],\n", " [5.2297e-07, 1.0000e+00],\n", " [2.2945e-09, 1.0000e+00],\n", " [1.8827e-14, 1.0000e+00],\n", " [3.6914e-12, 1.0000e+00],\n", " [1.0232e-05, 9.9999e-01],\n", " [9.9996e-01, 4.3504e-05],\n", " [1.2202e-07, 1.0000e+00],\n", " [9.5178e-08, 1.0000e+00],\n", " [1.0603e-02, 9.8940e-01],\n", " [9.4093e-01, 5.9069e-02],\n", " [9.9953e-01, 4.6727e-04],\n", " [4.0027e-05, 9.9996e-01],\n", " [1.0232e-05, 9.9999e-01],\n", " [9.9990e-01, 9.7410e-05],\n", " [6.9317e-10, 1.0000e+00],\n", " [7.1531e-01, 2.8469e-01],\n", " [4.5204e-08, 1.0000e+00],\n", " [1.7716e-03, 9.9823e-01],\n", " [9.5942e-06, 9.9999e-01],\n", " [7.0563e-06, 9.9999e-01],\n", " [2.9637e-08, 1.0000e+00],\n", " [4.3060e-07, 1.0000e+00],\n", " [9.8936e-09, 1.0000e+00],\n", " [5.7116e-03, 9.9429e-01],\n", " [6.6022e-10, 1.0000e+00],\n", " [9.8973e-05, 9.9990e-01],\n", " [1.1994e-04, 9.9988e-01],\n", " [1.5506e-11, 1.0000e+00],\n", " [9.9975e-01, 2.4858e-04],\n", " [4.5068e-05, 9.9995e-01],\n", " [4.7508e-09, 1.0000e+00],\n", " [1.3963e-08, 1.0000e+00],\n", " [1.5434e-04, 9.9985e-01],\n", " [1.1042e-05, 9.9999e-01],\n", " [5.0842e-11, 1.0000e+00],\n", " [1.4754e-07, 1.0000e+00],\n", " [1.3403e-07, 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2.1934e-05],\n", " [8.1731e-07, 1.0000e+00],\n", " [2.4994e-06, 1.0000e+00],\n", " [1.4337e-07, 1.0000e+00],\n", " [3.4392e-09, 1.0000e+00],\n", " [1.9588e-10, 1.0000e+00],\n", " [5.2363e-02, 9.4764e-01],\n", " [2.2923e-04, 9.9977e-01],\n", " [3.1012e-04, 9.9969e-01],\n", " [9.9985e-01, 1.4686e-04],\n", " [1.7467e-10, 1.0000e+00],\n", " [1.8465e-10, 1.0000e+00],\n", " [9.9990e-01, 1.0210e-04],\n", " [1.4477e-07, 1.0000e+00],\n", " [8.0056e-11, 1.0000e+00],\n", " [2.5159e-05, 9.9997e-01],\n", " [1.1317e-06, 1.0000e+00]])\n", "tensor([[1.1592e-10, 1.0000e+00],\n", " [2.0072e-07, 1.0000e+00],\n", " [5.5997e-07, 1.0000e+00],\n", " [9.9974e-01, 2.6457e-04],\n", " [2.4987e-04, 9.9975e-01]])\n", "Accuracy on test set: 98 %\n" ] } ], "source": [ "correct = 0\n", "total = 0\n", "with torch.no_grad():\n", " for data in test_dataloader:\n", " inputs, labels = data\n", " outputs = model(inputs.float())\n", " _, predicted = torch.max(outputs.data, 1)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "print('Accuracy on test set: %d %%' % (100 * correct / total))" ] } ], "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": "3.11.2" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }