From e320d57f99bb80397b9acb00811d20b58bdb0643 Mon Sep 17 00:00:00 2001 From: yukun-hh Date: Sun, 15 Mar 2026 14:42:56 +0800 Subject: [PATCH] d2l chapter 3 over --- test.ipynb | 1079 +++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 908 insertions(+), 171 deletions(-) diff --git a/test.ipynb b/test.ipynb index 3e0e16e..e2910e6 100644 --- a/test.ipynb +++ b/test.ipynb @@ -6,8 +6,8 @@ "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.716891442Z", - "start_time": "2026-03-14T11:37:45.891202979Z" + "end_time": "2026-03-15T06:40:53.937066295Z", + "start_time": "2026-03-15T06:40:52.962818496Z" } }, "source": [ @@ -24,8 +24,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.748820470Z", - "start_time": "2026-03-14T11:37:46.717673396Z" + "end_time": "2026-03-15T06:40:53.993520411Z", + "start_time": "2026-03-15T06:40:53.939990190Z" } }, "cell_type": "code", @@ -35,20 +35,20 @@ { "data": { "text/plain": [ - "tensor([[[ 1.1696, -0.5395],\n", - " [-1.2794, -1.0168],\n", - " [ 3.2351, 0.6066],\n", - " [ 1.5116, -0.1253]],\n", + "tensor([[[ 0.5509, -1.6216],\n", + " [ 0.1083, 0.4464],\n", + " [ 1.8819, 0.4029],\n", + " [-0.0733, 2.6961]],\n", "\n", - " [[-0.1823, 0.1887],\n", - " [ 0.0186, -1.5205],\n", - " [-0.3032, 0.1184],\n", - " [-0.1708, 1.2866]],\n", + " [[-2.0316, 0.7172],\n", + " [-0.3774, 0.5248],\n", + " [-0.0134, 0.3256],\n", + " [ 0.3433, 0.1697]],\n", "\n", - " [[ 0.1142, 0.0435],\n", - " [-0.4102, -0.4663],\n", - " [ 0.2203, 0.3123],\n", - " [ 1.9645, 1.8992]]])" + " [[ 1.1434, 0.6595],\n", + " [ 0.2386, -0.6560],\n", + " [ 1.3177, -0.6876],\n", + " [-1.0916, -0.6199]]])" ] }, "execution_count": 2, @@ -61,8 +61,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.795608911Z", - "start_time": "2026-03-14T11:37:46.749770547Z" + "end_time": "2026-03-15T06:40:54.023331390Z", + "start_time": "2026-03-15T06:40:53.996610811Z" } }, "cell_type": "code", @@ -97,8 +97,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.812288160Z", - "start_time": "2026-03-14T11:37:46.803470930Z" + "end_time": "2026-03-15T06:40:54.081323347Z", + "start_time": "2026-03-15T06:40:54.028230344Z" } }, "cell_type": "code", @@ -128,8 +128,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.908394350Z", - "start_time": "2026-03-14T11:37:46.858309741Z" + "end_time": "2026-03-15T06:40:54.538103014Z", + "start_time": "2026-03-15T06:40:54.136512769Z" } }, "cell_type": "code", @@ -154,8 +154,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:46.990018992Z", - "start_time": "2026-03-14T11:37:46.944214131Z" + "end_time": "2026-03-15T06:40:54.691375092Z", + "start_time": "2026-03-15T06:40:54.563788479Z" } }, "cell_type": "code", @@ -182,8 +182,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.097040164Z", - "start_time": "2026-03-14T11:37:46.993944041Z" + "end_time": "2026-03-15T06:40:54.732824469Z", + "start_time": "2026-03-15T06:40:54.714530988Z" } }, "cell_type": "code", @@ -206,8 +206,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.138922799Z", - "start_time": "2026-03-14T11:37:47.109432980Z" + "end_time": "2026-03-15T06:40:54.811604177Z", + "start_time": "2026-03-15T06:40:54.734220773Z" } }, "cell_type": "code", @@ -235,8 +235,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.162491839Z", - "start_time": "2026-03-14T11:37:47.139968456Z" + "end_time": "2026-03-15T06:40:54.853161280Z", + "start_time": "2026-03-15T06:40:54.813874506Z" } }, "cell_type": "code", @@ -273,8 +273,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.186754928Z", - "start_time": "2026-03-14T11:37:47.168374155Z" + "end_time": "2026-03-15T06:40:54.885351255Z", + "start_time": "2026-03-15T06:40:54.855249881Z" } }, "cell_type": "code", @@ -305,8 +305,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.214268957Z", - "start_time": "2026-03-14T11:37:47.192209367Z" + "end_time": "2026-03-15T06:40:54.918539225Z", + "start_time": "2026-03-15T06:40:54.887053932Z" } }, "cell_type": "code", @@ -334,8 +334,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.309990671Z", - "start_time": "2026-03-14T11:37:47.241157425Z" + "end_time": "2026-03-15T06:40:55.091544877Z", + "start_time": "2026-03-15T06:40:54.972764187Z" } }, "cell_type": "code", @@ -360,8 +360,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.535195222Z", - "start_time": "2026-03-14T11:37:47.409682869Z" + "end_time": "2026-03-15T06:40:55.318112438Z", + "start_time": "2026-03-15T06:40:55.208719181Z" } }, "cell_type": "code", @@ -393,8 +393,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.648470258Z", - "start_time": "2026-03-14T11:37:47.558566189Z" + "end_time": "2026-03-15T06:40:55.396075402Z", + "start_time": "2026-03-15T06:40:55.345582838Z" } }, "cell_type": "code", @@ -411,7 +411,7 @@ { "data": { "text/plain": [ - "(140539332541136, 140539333492432)" + "(140556050244048, 140556050244432)" ] }, "execution_count": 14, @@ -430,8 +430,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.690032963Z", - "start_time": "2026-03-14T11:37:47.651693108Z" + "end_time": "2026-03-15T06:40:55.435745645Z", + "start_time": "2026-03-15T06:40:55.410554891Z" } }, "cell_type": "code", @@ -458,8 +458,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.778795013Z", - "start_time": "2026-03-14T11:37:47.722390317Z" + "end_time": "2026-03-15T06:40:55.547870490Z", + "start_time": "2026-03-15T06:40:55.455797448Z" } }, "cell_type": "code", @@ -493,8 +493,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:47.852744522Z", - "start_time": "2026-03-14T11:37:47.810917322Z" + "end_time": "2026-03-15T06:40:55.646393885Z", + "start_time": "2026-03-15T06:40:55.571643582Z" } }, "cell_type": "code", @@ -533,8 +533,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.039859489Z", - "start_time": "2026-03-14T11:37:47.861768656Z" + "end_time": "2026-03-15T06:40:55.715281021Z", + "start_time": "2026-03-15T06:40:55.664043259Z" } }, "cell_type": "code", @@ -560,8 +560,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.070161220Z", - "start_time": "2026-03-14T11:37:48.042514455Z" + "end_time": "2026-03-15T06:40:55.752354225Z", + "start_time": "2026-03-15T06:40:55.717824570Z" } }, "cell_type": "code", @@ -590,7 +590,7 @@ "output_type": "stream", "text": [ "I am silly\n", - "执行时间: 0.000630秒\n" + "执行时间: 0.000183秒\n" ] } ], @@ -599,8 +599,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.092871646Z", - "start_time": "2026-03-14T11:37:48.071687740Z" + "end_time": "2026-03-15T06:40:55.822293071Z", + "start_time": "2026-03-15T06:40:55.771678228Z" } }, "cell_type": "code", @@ -623,8 +623,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.100506481Z", - "start_time": "2026-03-14T11:37:48.094144409Z" + "end_time": "2026-03-15T06:40:55.831732982Z", + "start_time": "2026-03-15T06:40:55.824867487Z" } }, "cell_type": "code", @@ -639,8 +639,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.121524857Z", - "start_time": "2026-03-14T11:37:48.101329494Z" + "end_time": "2026-03-15T06:40:55.870751082Z", + "start_time": "2026-03-15T06:40:55.832780649Z" } }, "cell_type": "code", @@ -666,8 +666,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.148136131Z", - "start_time": "2026-03-14T11:37:48.129950999Z" + "end_time": "2026-03-15T06:40:55.897976833Z", + "start_time": "2026-03-15T06:40:55.872431759Z" } }, "cell_type": "code", @@ -693,8 +693,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.175519287Z", - "start_time": "2026-03-14T11:37:48.153141505Z" + "end_time": "2026-03-15T06:40:55.913347196Z", + "start_time": "2026-03-15T06:40:55.901720461Z" } }, "cell_type": "code", @@ -722,8 +722,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.190165115Z", - "start_time": "2026-03-14T11:37:48.176741284Z" + "end_time": "2026-03-15T06:40:55.985375574Z", + "start_time": "2026-03-15T06:40:55.929676346Z" } }, "cell_type": "code", @@ -762,8 +762,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.210632850Z", - "start_time": "2026-03-14T11:37:48.200923142Z" + "end_time": "2026-03-15T06:40:56.018025749Z", + "start_time": "2026-03-15T06:40:55.987257926Z" } }, "cell_type": "code", @@ -786,8 +786,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.229459200Z", - "start_time": "2026-03-14T11:37:48.218856646Z" + "end_time": "2026-03-15T06:40:56.046029736Z", + "start_time": "2026-03-15T06:40:56.019720336Z" } }, "cell_type": "code", @@ -817,8 +817,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.269115256Z", - "start_time": "2026-03-14T11:37:48.244909724Z" + "end_time": "2026-03-15T06:40:56.086132664Z", + "start_time": "2026-03-15T06:40:56.049185612Z" } }, "cell_type": "code", @@ -845,8 +845,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.300440123Z", - "start_time": "2026-03-14T11:37:48.279382427Z" + "end_time": "2026-03-15T06:40:56.121364505Z", + "start_time": "2026-03-15T06:40:56.091095688Z" } }, "cell_type": "code", @@ -873,8 +873,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.318972768Z", - "start_time": "2026-03-14T11:37:48.301409242Z" + "end_time": "2026-03-15T06:40:56.150868516Z", + "start_time": "2026-03-15T06:40:56.126763544Z" } }, "cell_type": "code", @@ -884,7 +884,7 @@ { "data": { "text/plain": [ - "tensor([0., 0., 1., 0., 0., 0.])" + "tensor([0., 1., 0., 0., 0., 0.])" ] }, "execution_count": 30, @@ -897,8 +897,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.337802576Z", - "start_time": "2026-03-14T11:37:48.320185416Z" + "end_time": "2026-03-15T06:40:56.197988031Z", + "start_time": "2026-03-15T06:40:56.152490623Z" } }, "cell_type": "code", @@ -926,8 +926,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.463214605Z", - "start_time": "2026-03-14T11:37:48.358849572Z" + "end_time": "2026-03-15T06:40:56.313824234Z", + "start_time": "2026-03-15T06:40:56.200143209Z" } }, "cell_type": "code", @@ -966,7 +966,7 @@ "text/plain": [ "
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" + "image/png": 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" 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+1064,7 @@ { "data": { "text/plain": [ - "'0.00046 sec'" + "'0.00041 sec'" ] }, "execution_count": 35, @@ -1077,8 +1077,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.687129040Z", - "start_time": "2026-03-14T11:37:48.680082631Z" + "end_time": "2026-03-15T06:40:56.507308186Z", + "start_time": "2026-03-15T06:40:56.500119417Z" } }, "cell_type": "code", @@ -1095,8 +1095,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.705878968Z", - "start_time": "2026-03-14T11:37:48.687875003Z" + "end_time": "2026-03-15T06:40:56.533559761Z", + "start_time": "2026-03-15T06:40:56.508247056Z" } }, "cell_type": "code", @@ -1155,8 +1155,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.820167717Z", - "start_time": "2026-03-14T11:37:48.707125239Z" + "end_time": "2026-03-15T06:40:56.625694875Z", + "start_time": "2026-03-15T06:40:56.535032024Z" } }, "cell_type": "code", @@ -1176,7 +1176,7 @@ "text/plain": [ "
" ], - "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-14T19:37:48.782465\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-15T14:40:56.601846\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, "output_type": "display_data", @@ -1190,8 +1190,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:48.849464685Z", - "start_time": "2026-03-14T11:37:48.839470354Z" + "end_time": "2026-03-15T06:40:56.659870613Z", + "start_time": "2026-03-15T06:40:56.640905156Z" } }, "cell_type": "code", @@ -1211,8 +1211,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.046153840Z", - "start_time": "2026-03-14T11:37:48.850521592Z" + "end_time": "2026-03-15T06:40:56.700642098Z", + "start_time": "2026-03-15T06:40:56.665034974Z" } }, "cell_type": "code", @@ -1228,8 +1228,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.114421568Z", - "start_time": "2026-03-14T11:37:49.049220234Z" + "end_time": "2026-03-15T06:40:56.766436205Z", + "start_time": "2026-03-15T06:40:56.704907868Z" } }, "cell_type": "code", @@ -1242,7 +1242,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 41, @@ -1254,7 +1254,7 @@ "text/plain": [ "
" ], - "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-14T19:37:49.085396\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-15T14:40:56.736997\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, "output_type": "display_data", @@ -1268,8 +1268,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.133683224Z", - "start_time": "2026-03-14T11:37:49.125021829Z" + "end_time": "2026-03-15T06:40:56.788076510Z", + "start_time": "2026-03-15T06:40:56.778166680Z" } }, "cell_type": "code", @@ -1297,8 +1297,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.152287703Z", - "start_time": "2026-03-14T11:37:49.134387652Z" + "end_time": "2026-03-15T06:40:56.811093546Z", + "start_time": "2026-03-15T06:40:56.788825920Z" } }, "cell_type": "code", @@ -1321,8 +1321,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.183095346Z", - "start_time": "2026-03-14T11:37:49.153518090Z" + "end_time": "2026-03-15T06:40:56.833447562Z", + "start_time": "2026-03-15T06:40:56.813045993Z" } }, "cell_type": "code", @@ -1338,26 +1338,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[-0.3577, 0.6754],\n", - " [-0.1904, -0.6314],\n", - " [-1.5305, -0.2903],\n", - " [ 2.0532, -0.3528],\n", - " [ 0.4056, -0.7645],\n", - " [-0.7985, 1.3492],\n", - " [-0.4550, 0.1608],\n", - " [ 1.1672, -0.5057],\n", - " [ 0.3912, -2.4489],\n", - " [ 1.9930, 1.6857]]) \n", - " tensor([[ 1.1766],\n", - " [ 5.9634],\n", - " [ 2.1265],\n", - " [ 9.5015],\n", - " [ 7.6220],\n", - " [-1.9910],\n", - " [ 2.7378],\n", - " [ 8.2718],\n", - " [13.2983],\n", - " [ 2.4553]])\n" + "tensor([[-1.8455, -0.9126],\n", + " [-0.0299, 1.6530],\n", + " [-2.3513, 0.6457],\n", + " [ 0.1707, 0.8342],\n", + " [ 0.2096, -0.4362],\n", + " [ 0.6160, 1.7403],\n", + " [ 0.4242, 0.0484],\n", + " [-1.4459, 0.7434],\n", + " [ 0.5302, -0.5594],\n", + " [-0.5957, -1.5179]]) \n", + " tensor([[ 3.6056],\n", + " [-1.4872],\n", + " [-2.6965],\n", + " [ 1.6980],\n", + " [ 6.1001],\n", + " [-0.4750],\n", + " [ 4.9009],\n", + " [-1.2270],\n", + " [ 7.1486],\n", + " [ 8.1680]])\n" ] } ], @@ -1366,8 +1366,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.350363398Z", - "start_time": "2026-03-14T11:37:49.184018282Z" + "end_time": "2026-03-15T06:40:57.028635227Z", + "start_time": "2026-03-15T06:40:56.852110495Z" } }, "cell_type": "code", @@ -1387,26 +1387,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, train loss: 0.052015\n", - "epoch 2, train loss: 0.000228\n", + "epoch 1, train loss: 0.030682\n", + "epoch 2, train loss: 0.000100\n", "epoch 3, train loss: 0.000049\n", - "epoch 4, train loss: 0.000048\n", - "epoch 5, train loss: 0.000048\n", - "epoch 6, train loss: 0.000048\n", - "epoch 7, train loss: 0.000048\n", - "epoch 8, train loss: 0.000048\n", - "epoch 9, train loss: 0.000048\n", - "epoch 10, train loss: 0.000048\n", - "epoch 11, train loss: 0.000048\n", - "epoch 12, train loss: 0.000048\n", - "epoch 13, train loss: 0.000048\n", - "epoch 14, train loss: 0.000048\n", - "epoch 15, train loss: 0.000048\n", - "epoch 16, train loss: 0.000048\n", - "epoch 17, train loss: 0.000048\n", - "epoch 18, train loss: 0.000048\n", - "epoch 19, train loss: 0.000048\n", - "epoch 20, train loss: 0.000048\n" + "epoch 4, train loss: 0.000049\n", + "epoch 5, train loss: 0.000049\n", + "epoch 6, train loss: 0.000049\n", + "epoch 7, train loss: 0.000049\n", + "epoch 8, train loss: 0.000049\n", + "epoch 9, train loss: 0.000049\n", + "epoch 10, train loss: 0.000049\n", + "epoch 11, train loss: 0.000049\n", + "epoch 12, train loss: 0.000049\n", + "epoch 13, train loss: 0.000049\n", + "epoch 14, train loss: 0.000049\n", + "epoch 15, train loss: 0.000049\n", + "epoch 16, train loss: 0.000049\n", + "epoch 17, train loss: 0.000049\n", + "epoch 18, train loss: 0.000049\n", + "epoch 19, train loss: 0.000049\n", + "epoch 20, train loss: 0.000049\n" ] } ], @@ -1415,8 +1415,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.378897333Z", - "start_time": "2026-03-14T11:37:49.353047845Z" + "end_time": "2026-03-15T06:40:57.085057122Z", + "start_time": "2026-03-15T06:40:57.045310900Z" } }, "cell_type": "code", @@ -1430,8 +1430,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "w的估计误差: tensor([-0.0004, 0.0002], grad_fn=)\n", - "b的估计误差: tensor([-0.0005], grad_fn=)\n" + "w的估计误差: tensor([-0.0002, 0.0003], grad_fn=)\n", + "b的估计误差: tensor([0.0002], grad_fn=)\n" ] } ], @@ -1440,8 +1440,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:40:57.656654704Z", - "start_time": "2026-03-14T11:40:57.637331612Z" + "end_time": "2026-03-15T06:40:57.146334186Z", + "start_time": "2026-03-15T06:40:57.086581923Z" } }, "cell_type": "code", @@ -1458,13 +1458,13 @@ ], "id": "560d537dcbb5a335", "outputs": [], - "execution_count": 51 + "execution_count": 47 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:40:58.976005043Z", - "start_time": "2026-03-14T11:40:58.927183259Z" + "end_time": "2026-03-15T06:40:57.187559751Z", + "start_time": "2026-03-15T06:40:57.147857709Z" } }, "cell_type": "code", @@ -1482,18 +1482,18 @@ "tensor([0.])" ] }, - "execution_count": 52, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 52 + "execution_count": 48 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:41:52.186040634Z", - "start_time": "2026-03-14T11:41:52.126046372Z" + "end_time": "2026-03-15T06:40:57.523547926Z", + "start_time": "2026-03-15T06:40:57.191697380Z" } }, "cell_type": "code", @@ -1516,26 +1516,763 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.000091\n", - "epoch 2, loss 0.000091\n", - "epoch 3, loss 0.000091\n" + "epoch 1, loss 0.599957\n", + "epoch 2, loss 0.011503\n", + "epoch 3, loss 0.000325\n" ] } ], + "execution_count": 49 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.637796611Z", + "start_time": "2026-03-15T06:40:57.539577276Z" + } + }, + "cell_type": "code", + "source": [ + "import torchvision\n", + "from torchvision import transforms\n", + "trans =transforms.ToTensor()\n", + "mnist_train = torchvision.datasets.FashionMNIST(root=\"./data\",train=True,transform=trans,download=False)\n", + "mnist_test = torchvision.datasets.FashionMNIST(root=\"./data\",train=False,transform=trans,download=False)\n" + ], + "id": "bd4e8a65ccd03177", + "outputs": [], + "execution_count": 50 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.669099255Z", + "start_time": "2026-03-15T06:40:57.639295575Z" + } + }, + "cell_type": "code", + "source": [ + "use_svg_display()\n", + "len(mnist_train),len(mnist_test)" + ], + "id": "ed2c915af7f6a76a", + "outputs": [ + { + "data": { + "text/plain": [ + "(60000, 10000)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 51 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.717512337Z", + "start_time": "2026-03-15T06:40:57.672873756Z" + } + }, + "cell_type": "code", + "source": "mnist_train[0][0].shape", + "id": "4df1cbc292aa5981", + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 28, 28])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 52 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.739205016Z", + "start_time": "2026-03-15T06:40:57.719179425Z" + } + }, + "cell_type": "code", + "source": [ + "def get_fashion_mnist_labels(labels):\n", + " text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n", + "'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n", + " return [text_labels[int(i)] for i in labels]\n" + ], + "id": "332f3d6da0bafbe8", + "outputs": [], + "execution_count": 53 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.764139878Z", + "start_time": "2026-03-15T06:40:57.754725053Z" + } + }, + "cell_type": "code", + "source": [ + "def show_images(imgs, num_rows, num_cols, titles=None, scale=1): #@save\n", + " \"\"\"绘制图像列表\"\"\"\n", + " figsize = (num_cols * scale, num_rows * scale)\n", + " _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n", + " axes = axes.flatten()\n", + " for i, (ax, img) in enumerate(zip(axes, imgs)):\n", + " if torch.is_tensor(img):\n", + " # 图片张量\n", + " ax.imshow(img.numpy())\n", + " else:\n", + " # PIL图片\n", + " ax.imshow(img)\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)\n", + " if titles:\n", + " ax.set_title(titles[i])\n", + " return axes" + ], + "id": "c83202fe9b0ab487", + "outputs": [], + "execution_count": 54 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:40:57.957479055Z", + "start_time": "2026-03-15T06:40:57.765366722Z" + } + }, + "cell_type": "code", + "source": [ + "X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))\n", + "print(X.shape)\n", + "show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y));" + ], + "id": "cf4abd8370d55416", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([18, 1, 28, 28])\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-15T14:40:57.897836\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 55 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:00.262791420Z", + "start_time": "2026-03-15T06:40:57.970609683Z" + } + }, + "cell_type": "code", + "source": [ + "batch_size = 256\n", + "def get_dataloader_workers():\n", + " \"\"\"使用4个进程来读取数据\"\"\"\n", + " return 4\n", + "\n", + "train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True,\n", + "num_workers=get_dataloader_workers())\n", + "timer = Timer()\n", + "for X, y in train_iter:\n", + " continue\n", + "f'{timer.stop():.2f} sec'" + ], + "id": "552769fbffc16142", + "outputs": [ + { + "data": { + "text/plain": [ + "'2.26 sec'" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 56 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:00.481904106Z", + "start_time": "2026-03-15T06:41:00.308644956Z" + } + }, + "cell_type": "code", + "source": [ + "def load_data_fashion_mnist(batch_size, resize=None):\n", + " \"\"\"下载Fashion-MNIST数据集，然后将其加载到内存中\"\"\"\n", + " trans = [transforms.ToTensor()]\n", + " if resize:\n", + " trans.insert(0, transforms.Resize(resize))\n", + " trans = transforms.Compose(trans)\n", + " mnist_train = torchvision.datasets.FashionMNIST(\n", + " root=\"./data\", train=True, transform=trans, download=False)\n", + " mnist_test = torchvision.datasets.FashionMNIST(\n", + " root=\"./data\", train=False, transform=trans, download=False)\n", + " return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n", + " num_workers=get_dataloader_workers()),\n", + " data.DataLoader(mnist_test, batch_size, shuffle=False,\n", + " num_workers=get_dataloader_workers()))" + ], + "id": "aa81880abd86cae6", + "outputs": [], + "execution_count": 57 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.218194285Z", + "start_time": "2026-03-15T06:41:00.632922086Z" + } + }, + "cell_type": "code", + "source": [ + "train_iter, test_iter = load_data_fashion_mnist(32, resize=64)\n", + "for X, y in train_iter:\n", + " print(X.shape, X.dtype, y.shape, y.dtype)\n", + " break" + ], + "id": "4248a103f745154", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64\n" + ] + } + ], + "execution_count": 58 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.319582689Z", + "start_time": "2026-03-15T06:41:02.252523696Z" + } + }, + "cell_type": "code", + "source": [ + "from IPython import display\n", + "batch_size = 256\n", + "train_iter, test_iter = load_data_fashion_mnist(32)\n", + "num_inputs = 784\n", + "num_outputs = 10\n", + "W = torch.normal(0, 0.01, size=(num_inputs, num_outputs), requires_grad=True)\n", + "b = torch.zeros(num_outputs, requires_grad=True)\n", + "X = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n", + "X.sum(0, keepdim=True), X.sum(1, keepdim=True)\n" + ], + "id": "94c52f0cca88ef48", + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([[5., 7., 9.]]),\n", + " tensor([[ 6.],\n", + " [15.]]))" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 59 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.375197243Z", + "start_time": "2026-03-15T06:41:02.320989852Z" + } + }, + "cell_type": "code", + "source": [ + "def softmax(X):\n", + " X_exp = torch.exp(X)\n", + " partition = X_exp.sum(1, keepdim=True)\n", + " return X_exp / partition # 这里应用了广播机制\n", + "X = torch.normal(0, 1, (2, 5))\n", + "X_prob = softmax(X)\n", + "X_prob, X_prob.sum(1)" + ], + "id": "c4ab34373c5a664e", + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([[0.4360, 0.2195, 0.2044, 0.0829, 0.0571],\n", + " [0.0678, 0.3243, 0.2988, 0.0572, 0.2519]]),\n", + " tensor([1.0000, 1.0000]))" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 60 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.400574302Z", + "start_time": "2026-03-15T06:41:02.379951465Z" + } + }, + "cell_type": "code", + "source": [ + "def net(X):\n", + " return softmax(torch.matmul(X.reshape((-1, W.shape[0])), W) + b)" + ], + "id": "6eacc53b2b9738af", + "outputs": [], + "execution_count": 61 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.433540648Z", + "start_time": "2026-03-15T06:41:02.402525751Z" + } + }, + "cell_type": "code", + "source": [ + "y = torch.tensor([0, 2])\n", + "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n", + "y_hat[[0, 1], y]" + ], + "id": "698449b4dafb545c", + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.1000, 0.5000])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 62 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.473364236Z", + "start_time": "2026-03-15T06:41:02.438405972Z" + } + }, + "cell_type": "code", + "source": [ + "def cross_entropy(y_hat, y):\n", + " return - torch.log(y_hat[range(len(y_hat)), y])\n", + "cross_entropy(y_hat, y)" + ], + "id": "1720369fc8568c8c", + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([2.3026, 0.6931])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "execution_count": 63 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-03-14T11:37:49.859894220Z", - "start_time": "2026-03-14T11:37:49.844955750Z" + "end_time": "2026-03-15T06:41:02.495413592Z", + "start_time": "2026-03-15T06:41:02.475628534Z" } }, "cell_type": "code", - "source": "", - "id": "bd4e8a65ccd03177", + "source": [ + "def accuracy(y_hat, y): #@save\n", + " \"\"\"计算预测正确的数量\"\"\"\n", + " if len(y_hat.shape) > 1 and y_hat.shape[1] > 1:\n", + " y_hat = y_hat.argmax(axis=1)\n", + " cmp = y_hat.type(y.dtype) == y\n", + " return float(cmp.type(y.dtype).sum())\n", + "\n", + "accuracy(y_hat, y)/len(y)" + ], + "id": "e65719500a64ed87", + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 64 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.505437086Z", + "start_time": "2026-03-15T06:41:02.497345583Z" + } + }, + "cell_type": "code", + "source": [ + "class Accumulator: #@save\n", + " \"\"\"在n个变量上累加\"\"\"\n", + " def __init__(self, n):\n", + " self.data = [0.0] * n\n", + " def add(self, *args):\n", + " self.data = [a + float(b) for a, b in zip(self.data, args)]\n", + " def reset(self):\n", + " self.data = [0.0] * len(self.data)\n", + " def __getitem__(self, idx):\n", + " return self.data[idx]\n" + ], + "id": "f1eebb35ff2e9fea", "outputs": [], - "execution_count": 49 + "execution_count": 65 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:02.526181989Z", + "start_time": "2026-03-15T06:41:02.506819558Z" + } + }, + "cell_type": "code", + "source": [ + "def evaluate_accuracy(net, data_iter): #@save\n", + " \"\"\"计算在指定数据集上模型的精度\"\"\"\n", + " if isinstance(net, torch.nn.Module):\n", + " net.eval() # 将模型设置为评估模式\n", + " metric = Accumulator(2) # 正确预测数、预测总数\n", + " with torch.no_grad():\n", + " for X, y in data_iter:\n", + " metric.add(accuracy(net(X), y), y.numel())\n", + " return metric[0] / metric[1]\n" + ], + "id": "bc2beb5f2d6afe7e", + "outputs": [], + "execution_count": 66 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:06.805858093Z", + "start_time": "2026-03-15T06:41:02.527858694Z" + } + }, + "cell_type": "code", + "source": "evaluate_accuracy(net, test_iter)", + "id": "65bcfb7e40c1a98b", + "outputs": [ + { + "data": { + "text/plain": [ + "0.0498" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 67 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:06.843120784Z", + "start_time": "2026-03-15T06:41:06.827746920Z" + } + }, + "cell_type": "code", + "source": [ + "def train_epoch_ch3(net, train_iter, loss, updater): #@save\n", + " \"\"\"训练模型一个迭代周期（定义见第3章）\"\"\"\n", + " # 将模型设置为训练模式\n", + " if isinstance(net, torch.nn.Module):\n", + " net.train()\n", + " # 训练损失总和、训练准确度总和、样本数\n", + " metric = Accumulator(3)\n", + " for X, y in train_iter:\n", + " # 计算梯度并更新参数\n", + " y_hat = net(X)\n", + " l = loss(y_hat, y)\n", + " if isinstance(updater, torch.optim.Optimizer):\n", + " # 使用PyTorch内置的优化器和损失函数\n", + " updater.zero_grad()\n", + " l.mean().backward()\n", + " updater.step()\n", + " else:\n", + " # 使用定制的优化器和损失函数\n", + " l.sum().backward()\n", + " updater(X.shape[0])\n", + " metric.add(float(l.sum()), accuracy(y_hat, y), y.numel())\n", + "# 返回训练损失和训练精度\n", + " return metric[0] / metric[2], metric[1] / metric[2]" + ], + "id": "2faf1dcc6c023a53", + "outputs": [], + "execution_count": 68 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:06.897158404Z", + "start_time": "2026-03-15T06:41:06.851017516Z" + } + }, + "cell_type": "code", + "source": [ + "class Animator: #@save\n", + " \"\"\"在动画中绘制数据\"\"\"\n", + " def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None,\n", + " ylim=None, xscale='linear', yscale='linear',\n", + " fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1,\n", + " figsize=(3.5, 2.5)):\n", + " # 增量地绘制多条线\n", + " if legend is None:\n", + " legend = []\n", + " use_svg_display()\n", + " self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)\n", + " if nrows * ncols == 1:\n", + " self.axes = [self.axes, ]\n", + " # 使用lambda函数捕获参数\n", + " self.config_axes = lambda: set_axes(\n", + " self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend)\n", + " self.X, self.Y, self.fmts = None, None, fmts\n", + " def add(self, x, y):\n", + " # 向图表中添加多个数据点\n", + " if not hasattr(y, \"__len__\"):\n", + " y = [y]\n", + " n = len(y)\n", + " if not hasattr(x, \"__len__\"):\n", + " x = [x] * n\n", + " if not self.X:\n", + " self.X = [[] for _ in range(n)]\n", + " if not self.Y:\n", + " self.Y = [[] for _ in range(n)]\n", + " for i, (a, b) in enumerate(zip(x, y)):\n", + " if a is not None and b is not None:\n", + " self.X[i].append(a)\n", + " self.Y[i].append(b)\n", + " self.axes[0].cla()\n", + " for x, y, fmt in zip(self.X, self.Y, self.fmts):\n", + " self.axes[0].plot(x, y, fmt)\n", + " self.config_axes()\n", + " display.display(self.fig)\n", + " display.clear_output(wait=True)\n" + ], + "id": "7cd5367ab43c5e5f", + "outputs": [], + "execution_count": 69 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:06.913685898Z", + "start_time": "2026-03-15T06:41:06.898876289Z" + } + }, + "cell_type": "code", + "source": [ + "def train_ch3(net, train_iter, test_iter, loss, num_epochs, updater): #@save\n", + " \"\"\"训练模型（定义见第3章）\"\"\"\n", + " animator = Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0.3, 0.9],\n", + " legend=['train loss', 'train acc', 'test acc'])\n", + " for epoch in range(num_epochs):\n", + " train_metrics = train_epoch_ch3(net, train_iter, loss, updater)\n", + " test_acc = evaluate_accuracy(net, test_iter)\n", + " animator.add(epoch + 1, train_metrics + (test_acc,))\n", + " train_loss, train_acc = train_metrics\n", + " assert train_loss < 0.5, train_loss\n", + " assert train_acc <= 1 and train_acc > 0.7, train_acc\n", + " assert test_acc <= 1 and test_acc > 0.7, test_acc" + ], + "id": "b02a143c75fad40", + "outputs": [], + "execution_count": 70 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:06.945935582Z", + "start_time": "2026-03-15T06:41:06.916434775Z" + } + }, + "cell_type": "code", + "source": [ + "lr = 0.1\n", + "def updater(batch_size):\n", + " return sgd([W, b], lr, batch_size)" + ], + "id": "6a97b70779276b61", + "outputs": [], + "execution_count": 71 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:41:54.907637364Z", + "start_time": "2026-03-15T06:41:06.946861739Z" + } + }, + "cell_type": "code", + "source": [ + "num_epochs = 10\n", + "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, updater)" + ], + "id": "df3cceb72faee402", + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001B[31m---------------------------------------------------------------------------\u001B[39m", + "\u001B[31mKeyboardInterrupt\u001B[39m Traceback (most recent call last)", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[72]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m 1\u001B[39m num_epochs = \u001B[32m10\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mtrain_ch3\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnet\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtest_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcross_entropy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_epochs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupdater\u001B[49m\u001B[43m)\u001B[49m\n", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[70]\u001B[39m\u001B[32m, line 6\u001B[39m, in \u001B[36mtrain_ch3\u001B[39m\u001B[34m(net, train_iter, test_iter, loss, num_epochs, updater)\u001B[39m\n\u001B[32m 3\u001B[39m animator = Animator(xlabel=\u001B[33m'\u001B[39m\u001B[33mepoch\u001B[39m\u001B[33m'\u001B[39m, xlim=[\u001B[32m1\u001B[39m, num_epochs], ylim=[\u001B[32m0.3\u001B[39m, \u001B[32m0.9\u001B[39m],\n\u001B[32m 4\u001B[39m legend=[\u001B[33m'\u001B[39m\u001B[33mtrain loss\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mtrain acc\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mtest acc\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 5\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m epoch \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(num_epochs):\n\u001B[32m----> \u001B[39m\u001B[32m6\u001B[39m train_metrics = \u001B[43mtrain_epoch_ch3\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnet\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtrain_iter\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mloss\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupdater\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 7\u001B[39m test_acc = evaluate_accuracy(net, test_iter)\n\u001B[32m 8\u001B[39m animator.add(epoch + \u001B[32m1\u001B[39m, train_metrics + (test_acc,))\n", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[68]\u001B[39m\u001B[32m, line 10\u001B[39m, in \u001B[36mtrain_epoch_ch3\u001B[39m\u001B[34m(net, train_iter, loss, updater)\u001B[39m\n\u001B[32m 7\u001B[39m metric = Accumulator(\u001B[32m3\u001B[39m)\n\u001B[32m 8\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m X, y \u001B[38;5;129;01min\u001B[39;00m train_iter:\n\u001B[32m 9\u001B[39m \u001B[38;5;66;03m# 计算梯度并更新参数\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m10\u001B[39m y_hat = \u001B[43mnet\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 11\u001B[39m l = loss(y_hat, y)\n\u001B[32m 12\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(updater, torch.optim.Optimizer):\n\u001B[32m 13\u001B[39m \u001B[38;5;66;03m# 使用PyTorch内置的优化器和损失函数\u001B[39;00m\n", + "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[61]\u001B[39m\u001B[32m, line 2\u001B[39m, in \u001B[36mnet\u001B[39m\u001B[34m(X)\u001B[39m\n\u001B[32m 1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mnet\u001B[39m(X):\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m softmax(\u001B[43mtorch\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmatmul\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m.\u001B[49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43m-\u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mW\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mW\u001B[49m\u001B[43m)\u001B[49m + b)\n", + "\u001B[31mKeyboardInterrupt\u001B[39m: " + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-15T14:41:54.863039\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 72 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:42:03.186617720Z", + "start_time": "2026-03-15T06:42:01.659427155Z" + } + }, + "cell_type": "code", + "source": [ + "def predict_ch3(net, test_iter, n=6): #@save\n", + " \"\"\"预测标签（定义见第3章）\"\"\"\n", + " for (X, y),i in zip(test_iter,range(1)):\n", + " trues = get_fashion_mnist_labels(y)\n", + " preds = get_fashion_mnist_labels(net(X).argmax(axis=1))\n", + " titles = [true +'\\n' + pred for true, pred in zip(trues, preds)]\n", + " show_images(\n", + " X[0:n].reshape((n, 28, 28)), 1, n, titles=titles[0:n])\n", + "\n", + "predict_ch3(net, test_iter)\n" + ], + "id": "94f6177bfb40eece", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n \n \n \n \n 2026-03-15T14:42:03.146128\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 73 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-03-15T06:42:05.791711771Z", + "start_time": "2026-03-15T06:42:05.741066616Z" + } + }, + "cell_type": "code", + "source": [ + "batch_size = 256\n", + "train_iter, test_iter = load_data_fashion_mnist(batch_size)" + ], + "id": "4a0bfd0479ec7386", + "outputs": [], + "execution_count": 74 + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "net = nn.Sequential(nn.Flatten(), nn.Linear(784, 10))\n", + "def init_weights(m):\n", + " if type(m) == nn.Linear:\n", + " nn.init.normal_(m.weight, std=0.01)\n", + "\n", + "net.apply(init_weights);\n", + "loss = nn.CrossEntropyLoss(reduction='none')\n", + "trainer = torch.optim.SGD(net.parameters(), lr=0.1)\n", + "num_epochs = 10\n", + "train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)" + ], + "id": "b9808d88f5e6827b", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "c25dd146307f58e0", + "outputs": [], + "execution_count": null } ], "metadata": {