135 KiB
135 KiB
In [ ]:
from numpy.conftest import dtype !pip install tensorboard
In [1]:
%load_ext tensorboard %tensorboard --logdir runs --host 0.0.0.0 --port 6006
<script>
(function() {
const frame = document.getElementById("tensorboard-frame-e2d2c2813765bfcb");
const url = new URL("/", window.location);
const port = 6006;
if (port) {
url.port = port;
}
frame.src = url;
})();
</script>
In [2]:
from torch.utils.tensorboard import SummaryWriter import numpy as np # 创建一个SummaryWriter对象 writer = SummaryWriter() for n_iter in range(100): writer.add_scalar('Loss/train', np.random.random(), n_iter) writer.add_scalar('Loss/test', np.random.random(), n_iter) writer.add_scalar('Accuracy/train', np.random.random(), n_iter) writer.add_scalar('Accuracy/test', np.random.random(), n_iter) img = np.zeros((3, 100, 100)) img[0] = np.arange(0, 10000).reshape(100, 100) / 10000 img[1] = np.arange(0, 10000).reshape(100, 100) / 10000 writer.add_image('my_image', img, 0) writer.close()
In [5]:
import random import numpy as np import torch from torch import nn # 模型定义 class LinearModel(nn.Module): def __init__(self): super().__init__() self.weight = nn.Parameter(torch.randn(1)) self.bias = nn.Parameter(torch.randn(1)) def forward(self, input): return (input * self.weight) + self.bias # 数据 w = 2 b = 3 xlim = [-10, 10] x_train = np.random.randint(xlim[0], xlim[1], size=30) y_train = [w * x + b + random.randint(0, 2) for x in x_train]
In [7]:
from torch.utils.tensorboard import SummaryWriter # 训练 model = LinearModel() optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, weight_decay=1e-4, momentum=0.9) y_train = torch.tensor(y_train, dtype=torch.float32) writer = SummaryWriter() for epoch in range(500): input = torch.from_numpy(x_train) out = model(input) loss = nn.MSELoss()(out, y_train) model.zero_grad() loss.backward() optimizer.step() # 打印损失 print(f'Epoch {epoch}, Loss: {loss.item()}') # 记录损失 writer.add_scalar('Loss/train', loss, epoch)
/tmp/ipykernel_15/3398267037.py:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). y_train = torch.tensor(y_train, dtype=torch.float32)
Epoch 0, Loss: 47.03325271606445 Epoch 1, Loss: 47.01589584350586 Epoch 2, Loss: 46.98295593261719 Epoch 3, Loss: 46.93598556518555 Epoch 4, Loss: 46.87645721435547 Epoch 5, Loss: 46.80564880371094 Epoch 6, Loss: 46.724754333496094 Epoch 7, Loss: 46.63483810424805 Epoch 8, Loss: 46.53687286376953 Epoch 9, Loss: 46.43171310424805 Epoch 10, Loss: 46.32015609741211 Epoch 11, Loss: 46.20289993286133 Epoch 12, Loss: 46.08058166503906 Epoch 13, Loss: 45.95378494262695 Epoch 14, Loss: 45.8230094909668 Epoch 15, Loss: 45.68872833251953 Epoch 16, Loss: 45.55135726928711 Epoch 17, Loss: 45.411277770996094 Epoch 18, Loss: 45.26881790161133 Epoch 19, Loss: 45.12428283691406 Epoch 20, Loss: 44.97793960571289 Epoch 21, Loss: 44.830039978027344 Epoch 22, Loss: 44.68079376220703 Epoch 23, Loss: 44.5303955078125 Epoch 24, Loss: 44.379024505615234 Epoch 25, Loss: 44.22684860229492 Epoch 26, Loss: 44.07398986816406 Epoch 27, Loss: 43.920589447021484 Epoch 28, Loss: 43.76675033569336 Epoch 29, Loss: 43.61259078979492 Epoch 30, Loss: 43.45817565917969 Epoch 31, Loss: 43.30360794067383 Epoch 32, Loss: 43.14896774291992 Epoch 33, Loss: 42.99429702758789 Epoch 34, Loss: 42.83967208862305 Epoch 35, Loss: 42.68513488769531 Epoch 36, Loss: 42.53075408935547 Epoch 37, Loss: 42.37654495239258 Epoch 38, Loss: 42.22256851196289 Epoch 39, Loss: 42.06884765625 Epoch 40, Loss: 41.9154052734375 Epoch 41, Loss: 41.76228332519531 Epoch 42, Loss: 41.60950469970703 Epoch 43, Loss: 41.45708465576172 Epoch 44, Loss: 41.305049896240234 Epoch 45, Loss: 41.15340805053711 Epoch 46, Loss: 41.00218963623047 Epoch 47, Loss: 40.85139083862305 Epoch 48, Loss: 40.701045989990234 Epoch 49, Loss: 40.551143646240234 Epoch 50, Loss: 40.401710510253906 Epoch 51, Loss: 40.252742767333984 Epoch 52, Loss: 40.104248046875 Epoch 53, Loss: 39.95624542236328 Epoch 54, Loss: 39.80873107910156 Epoch 55, Loss: 39.66170883178711 Epoch 56, Loss: 39.515193939208984 Epoch 57, Loss: 39.36917495727539 Epoch 58, Loss: 39.22365951538086 Epoch 59, Loss: 39.07866287231445 Epoch 60, Loss: 38.93416976928711 Epoch 61, Loss: 38.790199279785156 Epoch 62, Loss: 38.646732330322266 Epoch 63, Loss: 38.503780364990234 Epoch 64, Loss: 38.36134719848633 Epoch 65, Loss: 38.219425201416016 Epoch 66, Loss: 38.07802200317383 Epoch 67, Loss: 37.9371337890625 Epoch 68, Loss: 37.79676055908203 Epoch 69, Loss: 37.65690231323242 Epoch 70, Loss: 37.5175666809082 Epoch 71, Loss: 37.37874221801758 Epoch 72, Loss: 37.24042510986328 Epoch 73, Loss: 37.10261917114258 Epoch 74, Loss: 36.965328216552734 Epoch 75, Loss: 36.828548431396484 Epoch 76, Loss: 36.69227600097656 Epoch 77, Loss: 36.55651092529297 Epoch 78, Loss: 36.4212532043457 Epoch 79, Loss: 36.2864990234375 Epoch 80, Loss: 36.15225601196289 Epoch 81, Loss: 36.01850509643555 Epoch 82, Loss: 35.88526153564453 Epoch 83, Loss: 35.75251007080078 Epoch 84, Loss: 35.620262145996094 Epoch 85, Loss: 35.48851013183594 Epoch 86, Loss: 35.357242584228516 Epoch 87, Loss: 35.226478576660156 Epoch 88, Loss: 35.0962028503418 Epoch 89, Loss: 34.96641540527344 Epoch 90, Loss: 34.83710861206055 Epoch 91, Loss: 34.70829391479492 Epoch 92, Loss: 34.5799674987793 Epoch 93, Loss: 34.45211410522461 Epoch 94, Loss: 34.32474899291992 Epoch 95, Loss: 34.19786071777344 Epoch 96, Loss: 34.071449279785156 Epoch 97, Loss: 33.94551086425781 Epoch 98, Loss: 33.82004928588867 Epoch 99, Loss: 33.69506072998047 Epoch 100, Loss: 33.5705451965332 Epoch 101, Loss: 33.44648361206055 Epoch 102, Loss: 33.32290267944336 Epoch 103, Loss: 33.199790954589844 Epoch 104, Loss: 33.07713317871094 Epoch 105, Loss: 32.95493698120117 Epoch 106, Loss: 32.83320236206055 Epoch 107, Loss: 32.71192932128906 Epoch 108, Loss: 32.59111022949219 Epoch 109, Loss: 32.47074890136719 Epoch 110, Loss: 32.350833892822266 Epoch 111, Loss: 32.23137664794922 Epoch 112, Loss: 32.112369537353516 Epoch 113, Loss: 31.993806838989258 Epoch 114, Loss: 31.875696182250977 Epoch 115, Loss: 31.758024215698242 Epoch 116, Loss: 31.64080047607422 Epoch 117, Loss: 31.524015426635742 Epoch 118, Loss: 31.407676696777344 Epoch 119, Loss: 31.291770935058594 Epoch 120, Loss: 31.17629623413086 Epoch 121, Loss: 31.061262130737305 Epoch 122, Loss: 30.9466609954834 Epoch 123, Loss: 30.832494735717773 Epoch 124, Loss: 30.718753814697266 Epoch 125, Loss: 30.605443954467773 Epoch 126, Loss: 30.4925594329834 Epoch 127, Loss: 30.380102157592773 Epoch 128, Loss: 30.268068313598633 Epoch 129, Loss: 30.156457901000977 Epoch 130, Loss: 30.045259475708008 Epoch 131, Loss: 29.93448829650879 Epoch 132, Loss: 29.82413101196289 Epoch 133, Loss: 29.71419334411621 Epoch 134, Loss: 29.60466766357422 Epoch 135, Loss: 29.495553970336914 Epoch 136, Loss: 29.38684844970703 Epoch 137, Loss: 29.278554916381836 Epoch 138, Loss: 29.17067527770996 Epoch 139, Loss: 29.06319808959961 Epoch 140, Loss: 28.956119537353516 Epoch 141, Loss: 28.849451065063477 Epoch 142, Loss: 28.743181228637695 Epoch 143, Loss: 28.637317657470703 Epoch 144, Loss: 28.53184700012207 Epoch 145, Loss: 28.426776885986328 Epoch 146, Loss: 28.322099685668945 Epoch 147, Loss: 28.217817306518555 Epoch 148, Loss: 28.11393165588379 Epoch 149, Loss: 28.01043128967285 Epoch 150, Loss: 27.907325744628906 Epoch 151, Loss: 27.804603576660156 Epoch 152, Loss: 27.702274322509766 Epoch 153, Loss: 27.60032844543457 Epoch 154, Loss: 27.498767852783203 Epoch 155, Loss: 27.397586822509766 Epoch 156, Loss: 27.296785354614258 Epoch 157, Loss: 27.196372985839844 Epoch 158, Loss: 27.096330642700195 Epoch 159, Loss: 26.996667861938477 Epoch 160, Loss: 26.89737892150879 Epoch 161, Loss: 26.798465728759766 Epoch 162, Loss: 26.699922561645508 Epoch 163, Loss: 26.60175323486328 Epoch 164, Loss: 26.503950119018555 Epoch 165, Loss: 26.406524658203125 Epoch 166, Loss: 26.30945587158203 Epoch 167, Loss: 26.212759017944336 Epoch 168, Loss: 26.116424560546875 Epoch 169, Loss: 26.02045249938965 Epoch 170, Loss: 25.92484474182129 Epoch 171, Loss: 25.8295955657959 Epoch 172, Loss: 25.734704971313477 Epoch 173, Loss: 25.640172958374023 Epoch 174, Loss: 25.545991897583008 Epoch 175, Loss: 25.45216941833496 Epoch 176, Loss: 25.35870361328125 Epoch 177, Loss: 25.265588760375977 Epoch 178, Loss: 25.172819137573242 Epoch 179, Loss: 25.080406188964844 Epoch 180, Loss: 24.98833656311035 Epoch 181, Loss: 24.8966121673584 Epoch 182, Loss: 24.80524253845215 Epoch 183, Loss: 24.71420669555664 Epoch 184, Loss: 24.62352180480957 Epoch 185, Loss: 24.533170700073242 Epoch 186, Loss: 24.44316864013672 Epoch 187, Loss: 24.35349464416504 Epoch 188, Loss: 24.264169692993164 Epoch 189, Loss: 24.17517852783203 Epoch 190, Loss: 24.086517333984375 Epoch 191, Loss: 23.99819564819336 Epoch 192, Loss: 23.910205841064453 Epoch 193, Loss: 23.822547912597656 Epoch 194, Loss: 23.73521614074707 Epoch 195, Loss: 23.648218154907227 Epoch 196, Loss: 23.56155014038086 Epoch 197, Loss: 23.47519874572754 Epoch 198, Loss: 23.389179229736328 Epoch 199, Loss: 23.303483963012695 Epoch 200, Loss: 23.218111038208008 Epoch 201, Loss: 23.133058547973633 Epoch 202, Loss: 23.04832649230957 Epoch 203, Loss: 22.963916778564453 Epoch 204, Loss: 22.879819869995117 Epoch 205, Loss: 22.79604148864746 Epoch 206, Loss: 22.712581634521484 Epoch 207, Loss: 22.62943458557129 Epoch 208, Loss: 22.546600341796875 Epoch 209, Loss: 22.464082717895508 Epoch 210, Loss: 22.381868362426758 Epoch 211, Loss: 22.299968719482422 Epoch 212, Loss: 22.218379974365234 Epoch 213, Loss: 22.137088775634766 Epoch 214, Loss: 22.056114196777344 Epoch 215, Loss: 21.975439071655273 Epoch 216, Loss: 21.895069122314453 Epoch 217, Loss: 21.815000534057617 Epoch 218, Loss: 21.7352352142334 Epoch 219, Loss: 21.655773162841797 Epoch 220, Loss: 21.57660484313965 Epoch 221, Loss: 21.49774169921875 Epoch 222, Loss: 21.419166564941406 Epoch 223, Loss: 21.34089469909668 Epoch 224, Loss: 21.262920379638672 Epoch 225, Loss: 21.185232162475586 Epoch 226, Loss: 21.107839584350586 Epoch 227, Loss: 21.030736923217773 Epoch 228, Loss: 20.953927993774414 Epoch 229, Loss: 20.87740707397461 Epoch 230, Loss: 20.80117416381836 Epoch 231, Loss: 20.72522735595703 Epoch 232, Loss: 20.649568557739258 Epoch 233, Loss: 20.57419204711914 Epoch 234, Loss: 20.499103546142578 Epoch 235, Loss: 20.424297332763672 Epoch 236, Loss: 20.349769592285156 Epoch 237, Loss: 20.27552604675293 Epoch 238, Loss: 20.20155906677246 Epoch 239, Loss: 20.127872467041016 Epoch 240, Loss: 20.05446434020996 Epoch 241, Loss: 19.981334686279297 Epoch 242, Loss: 19.908477783203125 Epoch 243, Loss: 19.835895538330078 Epoch 244, Loss: 19.76358413696289 Epoch 245, Loss: 19.69154930114746 Epoch 246, Loss: 19.619783401489258 Epoch 247, Loss: 19.548288345336914 Epoch 248, Loss: 19.47706413269043 Epoch 249, Loss: 19.406105041503906 Epoch 250, Loss: 19.335418701171875 Epoch 251, Loss: 19.26499366760254 Epoch 252, Loss: 19.194833755493164 Epoch 253, Loss: 19.124942779541016 Epoch 254, Loss: 19.055309295654297 Epoch 255, Loss: 18.985942840576172 Epoch 256, Loss: 18.91683578491211 Epoch 257, Loss: 18.847990036010742 Epoch 258, Loss: 18.77940559387207 Epoch 259, Loss: 18.711078643798828 Epoch 260, Loss: 18.64300537109375 Epoch 261, Loss: 18.5751895904541 Epoch 262, Loss: 18.50762939453125 Epoch 263, Loss: 18.440326690673828 Epoch 264, Loss: 18.373273849487305 Epoch 265, Loss: 18.30647850036621 Epoch 266, Loss: 18.23992919921875 Epoch 267, Loss: 18.173635482788086 Epoch 268, Loss: 18.107585906982422 Epoch 269, Loss: 18.041791915893555 Epoch 270, Loss: 17.97624397277832 Epoch 271, Loss: 17.910938262939453 Epoch 272, Loss: 17.84587860107422 Epoch 273, Loss: 17.781070709228516 Epoch 274, Loss: 17.716503143310547 Epoch 275, Loss: 17.652179718017578 Epoch 276, Loss: 17.58810043334961 Epoch 277, Loss: 17.524261474609375 Epoch 278, Loss: 17.460657119750977 Epoch 279, Loss: 17.39729881286621 Epoch 280, Loss: 17.33417510986328 Epoch 281, Loss: 17.27129554748535 Epoch 282, Loss: 17.208646774291992 Epoch 283, Loss: 17.146238327026367 Epoch 284, Loss: 17.08405876159668 Epoch 285, Loss: 17.02212142944336 Epoch 286, Loss: 16.960412979125977 Epoch 287, Loss: 16.898937225341797 Epoch 288, Loss: 16.837696075439453 Epoch 289, Loss: 16.776681900024414 Epoch 290, Loss: 16.715898513793945 Epoch 291, Loss: 16.655344009399414 Epoch 292, Loss: 16.595020294189453 Epoch 293, Loss: 16.534921646118164 Epoch 294, Loss: 16.475046157836914 Epoch 295, Loss: 16.4153995513916 Epoch 296, Loss: 16.35597801208496 Epoch 297, Loss: 16.296781539916992 Epoch 298, Loss: 16.237804412841797 Epoch 299, Loss: 16.179054260253906 Epoch 300, Loss: 16.12052345275879 Epoch 301, Loss: 16.062211990356445 Epoch 302, Loss: 16.004117965698242 Epoch 303, Loss: 15.946245193481445 Epoch 304, Loss: 15.888594627380371 Epoch 305, Loss: 15.831155776977539 Epoch 306, Loss: 15.77393627166748 Epoch 307, Loss: 15.71692943572998 Epoch 308, Loss: 15.660139083862305 Epoch 309, Loss: 15.603562355041504 Epoch 310, Loss: 15.547200202941895 Epoch 311, Loss: 15.491046905517578 Epoch 312, Loss: 15.43510913848877 Epoch 313, Loss: 15.379379272460938 Epoch 314, Loss: 15.32386302947998 Epoch 315, Loss: 15.26855182647705 Epoch 316, Loss: 15.213452339172363 Epoch 317, Loss: 15.15855884552002 Epoch 318, Loss: 15.10387134552002 Epoch 319, Loss: 15.049391746520996 Epoch 320, Loss: 14.995116233825684 Epoch 321, Loss: 14.941041946411133 Epoch 322, Loss: 14.887176513671875 Epoch 323, Loss: 14.833512306213379 Epoch 324, Loss: 14.780049324035645 Epoch 325, Loss: 14.726792335510254 Epoch 326, Loss: 14.67372989654541 Epoch 327, Loss: 14.620871543884277 Epoch 328, Loss: 14.568208694458008 Epoch 329, Loss: 14.515746116638184 Epoch 330, Loss: 14.463484764099121 Epoch 331, Loss: 14.41141414642334 Epoch 332, Loss: 14.35954475402832 Epoch 333, Loss: 14.30786418914795 Epoch 334, Loss: 14.256383895874023 Epoch 335, Loss: 14.205097198486328 Epoch 336, Loss: 14.154003143310547 Epoch 337, Loss: 14.103100776672363 Epoch 338, Loss: 14.052389144897461 Epoch 339, Loss: 14.001873016357422 Epoch 340, Loss: 13.951542854309082 Epoch 341, Loss: 13.901403427124023 Epoch 342, Loss: 13.851452827453613 Epoch 343, Loss: 13.801692008972168 Epoch 344, Loss: 13.752116203308105 Epoch 345, Loss: 13.702729225158691 Epoch 346, Loss: 13.65352725982666 Epoch 347, Loss: 13.604512214660645 Epoch 348, Loss: 13.555682182312012 Epoch 349, Loss: 13.50702953338623 Epoch 350, Loss: 13.45856761932373 Epoch 351, Loss: 13.410286903381348 Epoch 352, Loss: 13.362186431884766 Epoch 353, Loss: 13.314266204833984 Epoch 354, Loss: 13.26652717590332 Epoch 355, Loss: 13.218972206115723 Epoch 356, Loss: 13.171588897705078 Epoch 357, Loss: 13.124390602111816 Epoch 358, Loss: 13.077366828918457 Epoch 359, Loss: 13.030521392822266 Epoch 360, Loss: 12.983851432800293 Epoch 361, Loss: 12.937359809875488 Epoch 362, Loss: 12.891043663024902 Epoch 363, Loss: 12.844895362854004 Epoch 364, Loss: 12.798928260803223 Epoch 365, Loss: 12.753133773803711 Epoch 366, Loss: 12.707507133483887 Epoch 367, Loss: 12.662055015563965 Epoch 368, Loss: 12.616774559020996 Epoch 369, Loss: 12.571664810180664 Epoch 370, Loss: 12.526723861694336 Epoch 371, Loss: 12.481949806213379 Epoch 372, Loss: 12.437348365783691 Epoch 373, Loss: 12.392916679382324 Epoch 374, Loss: 12.348648071289062 Epoch 375, Loss: 12.304547309875488 Epoch 376, Loss: 12.260616302490234 Epoch 377, Loss: 12.216845512390137 Epoch 378, Loss: 12.173242568969727 Epoch 379, Loss: 12.129802703857422 Epoch 380, Loss: 12.086525917053223 Epoch 381, Loss: 12.043412208557129 Epoch 382, Loss: 12.00046443939209 Epoch 383, Loss: 11.95767593383789 Epoch 384, Loss: 11.91504955291748 Epoch 385, Loss: 11.872584342956543 Epoch 386, Loss: 11.830276489257812 Epoch 387, Loss: 11.788128852844238 Epoch 388, Loss: 11.74614143371582 Epoch 389, Loss: 11.704310417175293 Epoch 390, Loss: 11.662637710571289 Epoch 391, Loss: 11.62112045288086 Epoch 392, Loss: 11.57976245880127 Epoch 393, Loss: 11.53856086730957 Epoch 394, Loss: 11.497511863708496 Epoch 395, Loss: 11.456618309020996 Epoch 396, Loss: 11.415877342224121 Epoch 397, Loss: 11.375290870666504 Epoch 398, Loss: 11.334859848022461 Epoch 399, Loss: 11.29457950592041 Epoch 400, Loss: 11.254451751708984 Epoch 401, Loss: 11.214471817016602 Epoch 402, Loss: 11.174643516540527 Epoch 403, Loss: 11.134966850280762 Epoch 404, Loss: 11.095440864562988 Epoch 405, Loss: 11.056061744689941 Epoch 406, Loss: 11.016830444335938 Epoch 407, Loss: 10.977749824523926 Epoch 408, Loss: 10.938813209533691 Epoch 409, Loss: 10.9000244140625 Epoch 410, Loss: 10.861380577087402 Epoch 411, Loss: 10.822884559631348 Epoch 412, Loss: 10.78453254699707 Epoch 413, Loss: 10.74632740020752 Epoch 414, Loss: 10.70826244354248 Epoch 415, Loss: 10.670343399047852 Epoch 416, Loss: 10.63256549835205 Epoch 417, Loss: 10.59493350982666 Epoch 418, Loss: 10.557437896728516 Epoch 419, Loss: 10.520085334777832 Epoch 420, Loss: 10.482876777648926 Epoch 421, Loss: 10.445805549621582 Epoch 422, Loss: 10.408872604370117 Epoch 423, Loss: 10.372082710266113 Epoch 424, Loss: 10.335428237915039 Epoch 425, Loss: 10.29891300201416 Epoch 426, Loss: 10.262533187866211 Epoch 427, Loss: 10.226293563842773 Epoch 428, Loss: 10.190189361572266 Epoch 429, Loss: 10.154221534729004 Epoch 430, Loss: 10.118389129638672 Epoch 431, Loss: 10.08269214630127 Epoch 432, Loss: 10.04712963104248 Epoch 433, Loss: 10.011701583862305 Epoch 434, Loss: 9.976404190063477 Epoch 435, Loss: 9.941242218017578 Epoch 436, Loss: 9.906213760375977 Epoch 437, Loss: 9.871312141418457 Epoch 438, Loss: 9.8365478515625 Epoch 439, Loss: 9.801911354064941 Epoch 440, Loss: 9.767406463623047 Epoch 441, Loss: 9.7330322265625 Epoch 442, Loss: 9.698782920837402 Epoch 443, Loss: 9.66466999053955 Epoch 444, Loss: 9.630680084228516 Epoch 445, Loss: 9.596821784973145 Epoch 446, Loss: 9.563085556030273 Epoch 447, Loss: 9.529481887817383 Epoch 448, Loss: 9.496004104614258 Epoch 449, Loss: 9.462651252746582 Epoch 450, Loss: 9.429425239562988 Epoch 451, Loss: 9.396322250366211 Epoch 452, Loss: 9.363343238830566 Epoch 453, Loss: 9.330492973327637 Epoch 454, Loss: 9.297761917114258 Epoch 455, Loss: 9.265155792236328 Epoch 456, Loss: 9.232675552368164 Epoch 457, Loss: 9.200313568115234 Epoch 458, Loss: 9.168072700500488 Epoch 459, Loss: 9.135957717895508 Epoch 460, Loss: 9.103960990905762 Epoch 461, Loss: 9.0720853805542 Epoch 462, Loss: 9.040329933166504 Epoch 463, Loss: 9.008692741394043 Epoch 464, Loss: 8.977176666259766 Epoch 465, Loss: 8.945779800415039 Epoch 466, Loss: 8.914501190185547 Epoch 467, Loss: 8.883337020874023 Epoch 468, Loss: 8.852293014526367 Epoch 469, Loss: 8.821365356445312 Epoch 470, Loss: 8.79055404663086 Epoch 471, Loss: 8.759859085083008 Epoch 472, Loss: 8.729279518127441 Epoch 473, Loss: 8.698817253112793 Epoch 474, Loss: 8.668466567993164 Epoch 475, Loss: 8.63823413848877 Epoch 476, Loss: 8.608112335205078 Epoch 477, Loss: 8.578104019165039 Epoch 478, Loss: 8.548209190368652 Epoch 479, Loss: 8.518428802490234 Epoch 480, Loss: 8.488758087158203 Epoch 481, Loss: 8.459199905395508 Epoch 482, Loss: 8.429753303527832 Epoch 483, Loss: 8.400418281555176 Epoch 484, Loss: 8.371193885803223 Epoch 485, Loss: 8.342079162597656 Epoch 486, Loss: 8.31307315826416 Epoch 487, Loss: 8.284175872802734 Epoch 488, Loss: 8.255391120910645 Epoch 489, Loss: 8.22671127319336 Epoch 490, Loss: 8.198140144348145 Epoch 491, Loss: 8.169678688049316 Epoch 492, Loss: 8.141321182250977 Epoch 493, Loss: 8.11307430267334 Epoch 494, Loss: 8.084931373596191 Epoch 495, Loss: 8.056894302368164 Epoch 496, Loss: 8.028964042663574 Epoch 497, Loss: 8.001137733459473 Epoch 498, Loss: 7.973416805267334 Epoch 499, Loss: 7.94580078125
In [8]:
!pip install visdom
Collecting visdom
Downloading visdom-0.2.4.tar.gz (1.4 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 MB 3.8 MB/s eta 0:00:00a 0:00:01
Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy>=1.8 in /opt/conda/lib/python3.10/site-packages (from visdom) (1.26.3)
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Downloading scipy-1.15.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.7 MB)
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Building wheels for collected packages: visdom
DEPRECATION: Building 'visdom' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'visdom'. Discussion can be found at https://github.com/pypa/pip/issues/6334
Building wheel for visdom (setup.py) ... done
Created wheel for visdom: filename=visdom-0.2.4-py3-none-any.whl size=1408194 sha256=ac4a41963386402b0b7026649cfe03640129a6763c260f6d33e0591afc354b68
Stored in directory: /root/.cache/pip/wheels/42/29/49/5bed207bac4578e4d2c0c5fc0226bfd33a7e2953ea56356855
Successfully built visdom
Installing collected packages: scipy, visdom
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Successfully installed scipy-1.15.3 visdom-0.2.4
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
In [21]:
import subprocess # 启动 visdom 服务 subprocess.Popen(["visdom", "--host", "0.0.0.0"])
Out[21]:
<Popen: returncode: None args: ['visdom', '--host', '0.0.0.0']>
In [22]:
from visdom import Visdom import numpy as np import time # 将窗口类实例化 viz = Visdom() # 创建窗口并实例化 viz.line([0.], [0], win='train_loss', opts=dict(title='Loss', xlabel='Epoch', ylabel='Loss')) for epoch in range(100): # 模拟损失值 loss = 0.2 * np.random.random() + 1 # 更新窗口 viz.line([loss], [epoch], win='train_loss', update='append') # 打印损失 # print(f'Epoch {epoch}, Loss: {loss}') # 模拟训练时间 time.sleep(0.1) img = np.zeros((3, 100, 100)) img[0] = np.arange(0, 10000).reshape(100, 100) / 10000 img[1] = np.arange(0, 10000).reshape(100, 100) / 10000 # 可视化图像 viz.image(img, win='my_image', opts=dict(title='My Image'))
Setting up a new session... 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Out[22]:
'my_image'
In [23]:
# Visdom from visdom import Visdom import numpy as np # 训练 model = LinearModel() optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, weight_decay=1e-2, momentum=0.9) y_train = torch.tensor(y_train, dtype=torch.float32) # 实例化一个窗口 viz = Visdom() # 创建窗口并实例化 viz.line([0.], [0], win='train_loss', opts=dict(title='Loss', xlabel='Epoch', ylabel='Loss')) for epoch in range(500): input = torch.from_numpy(x_train) out = model(input) loss = nn.MSELoss()(out, y_train) model.zero_grad() loss.backward() optimizer.step() # 打印损失 # print(f'Epoch {epoch}, Loss: {loss.item()}') # 记录损失 viz.line([loss.item()], [epoch], win='train_loss', update='append')
/tmp/ipykernel_15/2491798258.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). y_train = torch.tensor(y_train, dtype=torch.float32) Setting up a new session... 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INFO:tornado.access:200 POST /env/main (172.18.0.1) 1.07ms INFO:tornado.access:200 POST /update (::1) 2.80ms INFO:tornado.access:200 POST /win_exists (::1) 0.46ms INFO:tornado.access:200 POST /env/main (172.18.0.1) 0.75ms INFO:tornado.access:200 POST /update (::1) 3.15ms INFO:tornado.access:200 POST /win_exists (::1) 0.43ms INFO:tornado.access:200 POST /env/main (172.18.0.1) 1.77ms INFO:tornado.access:200 POST /update (::1) 2.85ms INFO:tornado.access:200 POST /win_exists (::1) 0.45ms INFO:tornado.access:200 POST /env/main (172.18.0.1) 0.82ms INFO:tornado.access:200 POST /update (::1) 2.09ms INFO:tornado.access:200 POST /env/main (172.18.0.1) 1.25ms