Created
October 24, 2017 00:11
-
-
Save ProGamerGov/c3d850318d6d80eee63890beb74ff928 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| -image_size 512 & -tv_weight 0 | |
| [libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message. If the message turns out to be larger than 1073741824 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. | |
| [libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538683157 | |
| Successfully loaded models/VGG16_SOD_finetune.caffemodel | |
| conv1_1: 64 3 3 3 | |
| conv1_2: 64 64 3 3 | |
| conv2_1: 128 64 3 3 | |
| conv2_2: 128 128 3 3 | |
| conv3_1: 256 128 3 3 | |
| conv3_2: 256 256 3 3 | |
| conv3_3: 256 256 3 3 | |
| conv4_1: 512 256 3 3 | |
| conv4_2: 512 512 3 3 | |
| conv4_3: 512 512 3 3 | |
| conv5_1: 512 512 3 3 | |
| conv5_2: 512 512 3 3 | |
| conv5_3: 512 512 3 3 | |
| fc6: 1 1 25088 4096 | |
| fc7: 1 1 4096 4096 | |
| fc8-SOD100: 1 1 4096 100 | |
| Setting up style layer 2 : relu1_1 | |
| Setting up style layer 7 : relu2_1 | |
| Setting up style layer 12 : relu3_1 | |
| Setting up style layer 19 : relu4_1 | |
| Setting up content layer 21 : relu4_2 | |
| Setting up style layer 26 : relu5_1 | |
| Capturing content targets | |
| nn.Sequential { | |
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> output] | |
| (1): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 1,1) | |
| (2): cudnn.ReLU | |
| (3): nn.StyleLoss | |
| (4): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1) | |
| (5): cudnn.ReLU | |
| (6): cudnn.SpatialMaxPooling(2x2, 2,2) | |
| (7): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1) | |
| (8): cudnn.ReLU | |
| (9): nn.StyleLoss | |
| (10): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1) | |
| (11): cudnn.ReLU | |
| (12): cudnn.SpatialMaxPooling(2x2, 2,2) | |
| (13): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1) | |
| (14): cudnn.ReLU | |
| (15): nn.StyleLoss | |
| (16): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) | |
| (17): cudnn.ReLU | |
| (18): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1) | |
| (19): cudnn.ReLU | |
| (20): cudnn.SpatialMaxPooling(2x2, 2,2) | |
| (21): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1) | |
| (22): cudnn.ReLU | |
| (23): nn.StyleLoss | |
| (24): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1) | |
| (25): cudnn.ReLU | |
| (26): nn.ContentLoss | |
| (27): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1) | |
| (28): cudnn.ReLU | |
| (29): cudnn.SpatialMaxPooling(2x2, 2,2) | |
| (30): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1) | |
| (31): cudnn.ReLU | |
| (32): nn.StyleLoss | |
| } | |
| Capturing style target 1 | |
| 6.8129343986511 | |
| 246.64805603027 | |
| 216.18815612793 | |
| 47.680400848389 | |
| 14.186424255371 | |
| y value: 82.659896850586 | |
| dy value: 0 | |
| Running optimization with ADAM | |
| --- | |
| x1 value: -7.1907348632812 | |
| feval(x) grad value: -0.0015942046884447 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105558808 | |
| StyleLoss:updateOutput self.G 2: 6.7818932533264 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6394277874431e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037283526035026 | |
| dG 1: -1.5157122106757e-05 | |
| dG 2: -9.7380827360216e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925916416 | |
| StyleLoss:updateOutput self.G 2: 246.95088195801 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9300686321676e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -1.4163540981826e-05 | |
| dG 1: 3.69675726688e-05 | |
| dG 2: 4.7401644241718e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 877470336 | |
| StyleLoss:updateOutput self.G 2: 225.02751159668 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.5081109451294e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00031362011213787 | |
| dG 1: 0.00026975476066582 | |
| dG 2: 6.917868361489e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93552264 | |
| StyleLoss:updateOutput self.G 2: 47.58313369751 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.9566167231442e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00022891449043527 | |
| dG 1: -7.4203325084454e-07 | |
| dG 2: -3.7741747119062e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6736320 | |
| StyleLoss:updateOutput self.G 2: 13.705079078674 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0460073696095e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00062760431319475 | |
| dG 1: -3.6723836274177e-06 | |
| dG 2: -7.4714843686929e-12 | |
| --- | |
| Iteration 1 / 2500 | |
| Content 1 loss: 4598131.640625 | |
| Style 1 loss: 21730.979919 | |
| Style 2 loss: 18174884.765625 | |
| Style 3 loss: 61770708.984375 | |
| Style 4 loss: 6159049.804688 | |
| Style 5 loss: 571164.230347 | |
| Total loss: 91295670.405579 | |
| --- | |
| x1 value: -7.1881165504456 | |
| feval(x) grad value: 0.0014791755238548 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105899360 | |
| StyleLoss:updateOutput self.G 2: 6.8037710189819 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.42964673433e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00011784443631768 | |
| dG 1: -4.4740518205799e-06 | |
| dG 2: -2.8744671573544e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1933717376 | |
| StyleLoss:updateOutput self.G 2: 247.95115661621 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.9939397323869e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -5.1633942348417e-05 | |
| dG 1: 0.00015907059423625 | |
| dG 2: 2.0396843936066e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 863342976 | |
| StyleLoss:updateOutput self.G 2: 221.40446472168 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.4475540644507e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00019469208200462 | |
| dG 1: 0.00015918962890282 | |
| dG 2: 4.0824222474756e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 91500704 | |
| StyleLoss:updateOutput self.G 2: 46.539672851562 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.5122358111912e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0003701293899212 | |
| dG 1: -8.703071216587e-06 | |
| dG 2: -4.4266105538038e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6734719.5 | |
| StyleLoss:updateOutput self.G 2: 13.701823234558 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.3685409423233e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0026211247313768 | |
| dG 1: -3.6972235193389e-06 | |
| dG 2: -7.5220194656334e-12 | |
| --- | |
| Iteration 2 / 2500 | |
| Content 1 loss: 4506305.468750 | |
| Style 1 loss: 16246.167183 | |
| Style 2 loss: 12339704.589844 | |
| Style 3 loss: 34971550.781250 | |
| Style 4 loss: 3264162.231445 | |
| Style 5 loss: 268919.448853 | |
| Total loss: 55366888.687325 | |
| --- | |
| x1 value: -7.1877503395081 | |
| feval(x) grad value: -0.0028631370514631 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 107066744 | |
| StyleLoss:updateOutput self.G 2: 6.87877368927 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.5806560201054e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00096543319523335 | |
| dG 1: 3.2147887395695e-05 | |
| dG 2: 2.0654216184579e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1959184384 | |
| StyleLoss:updateOutput self.G 2: 251.2166595459 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.5303941103848e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00019884797802661 | |
| dG 1: 0.00055769033497199 | |
| dG 2: 7.150991604421e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 874999744 | |
| StyleLoss:updateOutput self.G 2: 224.39385986328 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.2025592601221e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037269134190865 | |
| dG 1: 0.00025041869957931 | |
| dG 2: 6.4219934925447e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94123808 | |
| StyleLoss:updateOutput self.G 2: 47.873840332031 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3781257024448e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0001786870561773 | |
| dG 1: 1.4757815733901e-06 | |
| dG 2: 7.5062146168842e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7271408 | |
| StyleLoss:updateOutput self.G 2: 14.793717384338 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.2912835119278e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.003774770302698 | |
| dG 1: 4.6332770580193e-06 | |
| dG 2: 9.4264257857746e-12 | |
| --- | |
| Iteration 3 / 2500 | |
| Content 1 loss: 4697250.000000 | |
| Style 1 loss: 11644.589424 | |
| Style 2 loss: 8501630.126953 | |
| Style 3 loss: 20663041.992188 | |
| Style 4 loss: 2154238.952637 | |
| Style 5 loss: 168343.666077 | |
| Total loss: 36196149.327278 | |
| --- | |
| x1 value: -7.1881852149963 | |
| feval(x) grad value: -0.003585517173633 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 108040200 | |
| StyleLoss:updateOutput self.G 2: 6.9413161277771 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6149792614328e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014789969427511 | |
| dG 1: 6.2686514866073e-05 | |
| dG 2: 4.0274537177476e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1980076928 | |
| StyleLoss:updateOutput self.G 2: 253.89559936523 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.5350612032944e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00028728536562994 | |
| dG 1: 0.0008847089484334 | |
| dG 2: 1.1344189476681e-10 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 876910720 | |
| StyleLoss:updateOutput self.G 2: 224.88394165039 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0704947328577e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00051756796892732 | |
| dG 1: 0.00026537544908933 | |
| dG 2: 6.8055595880967e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94028624 | |
| StyleLoss:updateOutput self.G 2: 47.825435638428 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.6955967175345e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020961258269381 | |
| dG 1: 1.1065022818002e-06 | |
| dG 2: 5.6279612047072e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6783932 | |
| StyleLoss:updateOutput self.G 2: 13.801947593689 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4512145791487e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0026707297656685 | |
| dG 1: -2.9333314159885e-06 | |
| dG 2: -5.9678785351014e-12 | |
| --- | |
| Iteration 4 / 2500 | |
| Content 1 loss: 4677426.562500 | |
| Style 1 loss: 8071.896315 | |
| Style 2 loss: 6197091.796875 | |
| Style 3 loss: 13751610.351562 | |
| Style 4 loss: 1775310.058594 | |
| Style 5 loss: 130430.065155 | |
| Total loss: 26539940.731001 | |
| --- | |
| x1 value: -7.1873183250427 | |
| feval(x) grad value: -0.0049881190061569 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 108524200 | |
| StyleLoss:updateOutput self.G 2: 6.9724111557007 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3925858627649e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017388677224517 | |
| dG 1: 7.7869641245343e-05 | |
| dG 2: 5.0029312290145e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1990673152 | |
| StyleLoss:updateOutput self.G 2: 255.25430297852 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.6622094386257e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0004278679843992 | |
| dG 1: 0.0010505681857467 | |
| dG 2: 1.3470921600423e-10 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 874926208 | |
| StyleLoss:updateOutput self.G 2: 224.37496948242 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2807581128982e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065403216285631 | |
| dG 1: 0.00024984270567074 | |
| dG 2: 6.407221975202e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93573216 | |
| StyleLoss:updateOutput self.G 2: 47.593799591064 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.6835178462979e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012806650192942 | |
| dG 1: -6.6067798343283e-07 | |
| dG 2: -3.3603859712211e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6712324.5 | |
| StyleLoss:updateOutput self.G 2: 13.656257629395 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.3052937093744e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0037831759545952 | |
| dG 1: -4.0448371692037e-06 | |
| dG 2: -8.2292428080222e-12 | |
| --- | |
| Iteration 5 / 2500 | |
| Content 1 loss: 4699614.843750 | |
| Style 1 loss: 5605.602622 | |
| Style 2 loss: 4812788.818359 | |
| Style 3 loss: 10447941.650391 | |
| Style 4 loss: 1539798.339844 | |
| Style 5 loss: 109393.226624 | |
| Total loss: 21615142.481589 | |
| --- | |
| x1 value: -7.1845512390137 | |
| feval(x) grad value: -0.0057208822108805 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 108365616 | |
| StyleLoss:updateOutput self.G 2: 6.9622230529785 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5913845291825e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001711759949103 | |
| dG 1: 7.2894676122814e-05 | |
| dG 2: 4.6833023570336e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1988171904 | |
| StyleLoss:updateOutput self.G 2: 254.93353271484 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4243382875447e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00055326335132122 | |
| dG 1: 0.0010114145698026 | |
| dG 2: 1.2968877360908e-10 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 867545152 | |
| StyleLoss:updateOutput self.G 2: 222.48213195801 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1805435207179e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065349752549082 | |
| dG 1: 0.00019207841251045 | |
| dG 2: 4.9258552098363e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 92816168 | |
| StyleLoss:updateOutput self.G 2: 47.20874786377 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5405874354002e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00011451218597358 | |
| dG 1: -3.5983944144391e-06 | |
| dG 2: -1.8302382179258e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6899383 | |
| StyleLoss:updateOutput self.G 2: 14.036831855774 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8540672996892e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00011124413867947 | |
| dG 1: -1.1413035281294e-06 | |
| dG 2: -2.3219876542357e-12 | |
| --- | |
| Iteration 6 / 2500 | |
| Content 1 loss: 4732125.390625 | |
| Style 1 loss: 3964.573860 | |
| Style 2 loss: 3772302.978516 | |
| Style 3 loss: 8373788.818359 | |
| Style 4 loss: 1325101.043701 | |
| Style 5 loss: 91583.536148 | |
| Total loss: 18298866.341209 | |
| --- | |
| x1 value: -7.1798892021179 | |
| feval(x) grad value: -0.0021758831571788 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 107578520 | |
| StyleLoss:updateOutput self.G 2: 6.9116539955139 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8740170304454e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011868244037032 | |
| dG 1: 4.8202669859165e-05 | |
| dG 2: 3.0969020750615e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1973527040 | |
| StyleLoss:updateOutput self.G 2: 253.05577087402 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.7805055670742e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00048593894462101 | |
| dG 1: 0.00078219105489552 | |
| dG 2: 1.0029653496613e-10 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 853540224 | |
| StyleLoss:updateOutput self.G 2: 218.890625 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.7807235382134e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00041542228427716 | |
| dG 1: 8.2472724898253e-05 | |
| dG 2: 2.1150149340232e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 91309464 | |
| StyleLoss:updateOutput self.G 2: 46.442401885986 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1310638114992e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00079981505405158 | |
| dG 1: -9.44526072999e-06 | |
| dG 2: -4.8041089335815e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6859600.5 | |
| StyleLoss:updateOutput self.G 2: 13.955892562866 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1237870839741e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0012742723338306 | |
| dG 1: -1.7588178025107e-06 | |
| dG 2: -3.5783244856785e-12 | |
| --- | |
| Iteration 7 / 2500 | |
| Content 1 loss: 4695154.687500 | |
| Style 1 loss: 2846.404195 | |
| Style 2 loss: 2831080.993652 | |
| Style 3 loss: 6777178.710938 | |
| Style 4 loss: 1162587.524414 | |
| Style 5 loss: 79330.032349 | |
| Total loss: 15548178.353047 | |
| --- | |
| x1 value: -7.1753439903259 | |
| feval(x) grad value: 0.00051878805970773 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 106563184 | |
| StyleLoss:updateOutput self.G 2: 6.8464212417603 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.8150448073159e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00063727697124705 | |
| dG 1: 1.6350961232092e-05 | |
| dG 2: 1.0505092889329e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1955338240 | |
| StyleLoss:updateOutput self.G 2: 250.7234954834 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.0449587497023e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00038487018900923 | |
| dG 1: 0.00049749040044844 | |
| dG 2: 6.379076433749e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843800960 | |
| StyleLoss:updateOutput self.G 2: 216.39297485352 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5553544230329e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00013736262917519 | |
| dG 1: 6.2500789681508e-06 | |
| dG 2: 1.6028363532261e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 91153192 | |
| StyleLoss:updateOutput self.G 2: 46.362922668457 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5219237070596e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00099830841645598 | |
| dG 1: -1.0051635399577e-05 | |
| dG 2: -5.1125262877372e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6952085 | |
| StyleLoss:updateOutput self.G 2: 14.144053459167 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.8714046714576e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00059228425379843 | |
| dG 1: -3.2326852306142e-07 | |
| dG 2: -6.5769167455904e-13 | |
| --- | |
| Iteration 8 / 2500 | |
| Content 1 loss: 4719631.250000 | |
| Style 1 loss: 2118.458033 | |
| Style 2 loss: 2085956.359863 | |
| Style 3 loss: 5590564.819336 | |
| Style 4 loss: 1027157.684326 | |
| Style 5 loss: 72662.744522 | |
| Total loss: 13498091.316080 | |
| --- | |
| x1 value: -7.1707201004028 | |
| feval(x) grad value: 0.0018092476530001 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105627696 | |
| StyleLoss:updateOutput self.G 2: 6.7863187789917 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0928170368629e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00033395510399714 | |
| dG 1: -1.2996322766412e-05 | |
| dG 2: -8.3498198589702e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1939622400 | |
| StyleLoss:updateOutput self.G 2: 248.70832824707 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.5466041598274e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00024414280778728 | |
| dG 1: 0.00025149714201689 | |
| dG 2: 3.2248256148781e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 839988544 | |
| StyleLoss:updateOutput self.G 2: 215.4153137207 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2367282309356e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -3.9535527321277e-05 | |
| dG 1: -2.3585558665218e-05 | |
| dG 2: -6.0485245284569e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 92118808 | |
| StyleLoss:updateOutput self.G 2: 46.85404586792 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.2903597703753e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058737496146932 | |
| dG 1: -6.3046259128896e-06 | |
| dG 2: -3.2066998795904e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7044308 | |
| StyleLoss:updateOutput self.G 2: 14.3316822052 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.2728316884677e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0025636991485953 | |
| dG 1: 1.1082245237048e-06 | |
| dG 2: 2.2546886233044e-12 | |
| --- | |
| Iteration 9 / 2500 | |
| Content 1 loss: 4774742.187500 | |
| Style 1 loss: 1670.131266 | |
| Style 2 loss: 1582075.378418 | |
| Style 3 loss: 4698680.786133 | |
| Style 4 loss: 921795.501709 | |
| Style 5 loss: 68238.338470 | |
| Total loss: 12047202.323496 | |
| --- | |
| x1 value: -7.1658511161804 | |
| feval(x) grad value: 0.0039558419957757 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104951264 | |
| StyleLoss:updateOutput self.G 2: 6.7428593635559 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3952669181426e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020073475025129 | |
| dG 1: -3.421615838306e-05 | |
| dG 2: -2.1983040388029e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1929227520 | |
| StyleLoss:updateOutput self.G 2: 247.37548828125 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2049743364505e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.3231724703219e-05 | |
| dG 1: 8.8794964540284e-05 | |
| dG 2: 1.1385744777548e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 838702336 | |
| StyleLoss:updateOutput self.G 2: 215.08543395996 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3512114921687e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00013979313371237 | |
| dG 1: -3.3652890124358e-05 | |
| dG 2: -8.6302900589863e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 92854552 | |
| StyleLoss:updateOutput self.G 2: 47.228267669678 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7790496065782e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002096163952956 | |
| dG 1: -3.4495412819524e-06 | |
| dG 2: -1.7545276212796e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6943444.5 | |
| StyleLoss:updateOutput self.G 2: 14.126477241516 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.2450349598039e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00055470212828368 | |
| dG 1: -4.5738218545921e-07 | |
| dG 2: -9.3054654693586e-13 | |
| --- | |
| Iteration 10 / 2500 | |
| Content 1 loss: 4786624.609375 | |
| Style 1 loss: 1393.162608 | |
| Style 2 loss: 1262669.403076 | |
| Style 3 loss: 4038174.682617 | |
| Style 4 loss: 846101.806641 | |
| Style 5 loss: 61882.226944 | |
| Total loss: 10996845.891261 | |
| --- | |
| x1 value: -7.1622538566589 | |
| feval(x) grad value: 0.0025583715178072 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104689720 | |
| StyleLoss:updateOutput self.G 2: 6.726056098938 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0756666546949e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044931884622201 | |
| dG 1: -4.242127033649e-05 | |
| dG 2: -2.7254617833428e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926613376 | |
| StyleLoss:updateOutput self.G 2: 247.04028320312 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7208604319308e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 7.8227327321656e-05 | |
| dG 1: 4.7874520532787e-05 | |
| dG 2: 6.1387145366965e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842263808 | |
| StyleLoss:updateOutput self.G 2: 215.99876403809 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.4123762421577e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.3404679672676e-06 | |
| dG 1: -5.7803013078228e-06 | |
| dG 2: -1.4823602415004e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93772904 | |
| StyleLoss:updateOutput self.G 2: 47.695365905762 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.3633786361852e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00030533218523487 | |
| dG 1: 1.1417404266467e-07 | |
| dG 2: 5.807188768487e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6996679 | |
| StyleLoss:updateOutput self.G 2: 14.234780311584 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.388371089206e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00083302275743335 | |
| dG 1: 3.6891643162562e-07 | |
| dG 2: 7.505622642498e-13 | |
| --- | |
| Iteration 11 / 2500 | |
| Content 1 loss: 4837418.359375 | |
| Style 1 loss: 1172.196329 | |
| Style 2 loss: 1046080.718994 | |
| Style 3 loss: 3495596.923828 | |
| Style 4 loss: 777797.332764 | |
| Style 5 loss: 57025.251389 | |
| Total loss: 10215090.782678 | |
| --- | |
| x1 value: -7.1608424186707 | |
| feval(x) grad value: 0.0028852140530944 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104638464 | |
| StyleLoss:updateOutput self.G 2: 6.7227630615234 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4032716200727e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00046681016101502 | |
| dG 1: -4.4029256969225e-05 | |
| dG 2: -2.8287708547098e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927172224 | |
| StyleLoss:updateOutput self.G 2: 247.1118927002 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2438831237537e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00010824237688212 | |
| dG 1: 5.6622626289027e-05 | |
| dG 2: 7.260443114776e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844044544 | |
| StyleLoss:updateOutput self.G 2: 216.45541381836 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9839767162466e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.9775752460118e-05 | |
| dG 1: 8.1559855971136e-06 | |
| dG 2: 2.0916042335617e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93799680 | |
| StyleLoss:updateOutput self.G 2: 47.708988189697 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0486834390331e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00019040747429244 | |
| dG 1: 2.1806596350871e-07 | |
| dG 2: 1.1091390935032e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6936952.5 | |
| StyleLoss:updateOutput self.G 2: 14.113266944885 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.932917399472e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011597503907979 | |
| dG 1: -5.5815445421104e-07 | |
| dG 2: -1.1355682019709e-12 | |
| --- | |
| Iteration 12 / 2500 | |
| Content 1 loss: 4846000.000000 | |
| Style 1 loss: 999.811739 | |
| Style 2 loss: 886595.764160 | |
| Style 3 loss: 3051467.834473 | |
| Style 4 loss: 719406.967163 | |
| Style 5 loss: 53446.580887 | |
| Total loss: 9557916.958421 | |
| --- | |
| x1 value: -7.1624870300293 | |
| feval(x) grad value: 0.0020237613935024 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104687016 | |
| StyleLoss:updateOutput self.G 2: 6.7258815765381 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5208319815183e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00058365077711642 | |
| dG 1: -4.2506228055572e-05 | |
| dG 2: -2.7309198739195e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928860032 | |
| StyleLoss:updateOutput self.G 2: 247.32830810547 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1705006620086e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00014955567894503 | |
| dG 1: 8.3038852608297e-05 | |
| dG 2: 1.0647667643415e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845840448 | |
| StyleLoss:updateOutput self.G 2: 216.91603088379 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.7961839893796e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.000206595621421 | |
| dG 1: 2.2212410840439e-05 | |
| dG 2: 5.696377831238e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93944672 | |
| StyleLoss:updateOutput self.G 2: 47.782741546631 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.9717413358176e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00018322312098462 | |
| dG 1: 7.8072901033011e-07 | |
| dG 2: 3.9709945401367e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6939418 | |
| StyleLoss:updateOutput self.G 2: 14.118284225464 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5262584440734e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00091575511032715 | |
| dG 1: -5.1988286031701e-07 | |
| dG 2: -1.05770427139e-12 | |
| --- | |
| Iteration 13 / 2500 | |
| Content 1 loss: 4861574.218750 | |
| Style 1 loss: 872.416914 | |
| Style 2 loss: 763870.559692 | |
| Style 3 loss: 2681381.286621 | |
| Style 4 loss: 666884.490967 | |
| Style 5 loss: 50126.329422 | |
| Total loss: 9024709.302366 | |
| --- | |
| x1 value: -7.1667900085449 | |
| feval(x) grad value: 0.0012912690872326 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104682680 | |
| StyleLoss:updateOutput self.G 2: 6.725604057312 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6913974099662e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061524019110948 | |
| dG 1: -4.2641957406886e-05 | |
| dG 2: -2.7396409793545e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928913664 | |
| StyleLoss:updateOutput self.G 2: 247.33512878418 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.412913684997e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00015593056741636 | |
| dG 1: 8.3871156675741e-05 | |
| dG 2: 1.0754389566381e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846969344 | |
| StyleLoss:updateOutput self.G 2: 217.20552062988 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.036875805331e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00027977683930658 | |
| dG 1: 3.1047413358465e-05 | |
| dG 2: 7.9621170767652e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94084712 | |
| StyleLoss:updateOutput self.G 2: 47.853958129883 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.7824628640901e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00026390570565127 | |
| dG 1: 1.3241501619632e-06 | |
| dG 2: 6.7349766172051e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6976260.5 | |
| StyleLoss:updateOutput self.G 2: 14.19324016571 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1457147053306e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00068742863368243 | |
| dG 1: 5.1990426186421e-08 | |
| dG 2: 1.0577468263252e-13 | |
| --- | |
| Iteration 14 / 2500 | |
| Content 1 loss: 4880112.890625 | |
| Style 1 loss: 795.616508 | |
| Style 2 loss: 657986.801147 | |
| Style 3 loss: 2375652.099609 | |
| Style 4 loss: 619150.543213 | |
| Style 5 loss: 47465.180397 | |
| Total loss: 8581163.131499 | |
| --- | |
| x1 value: -7.1731176376343 | |
| feval(x) grad value: 0.0039362208917737 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104546264 | |
| StyleLoss:updateOutput self.G 2: 6.7168397903442 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0679523749532e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00023342906206381 | |
| dG 1: -4.6921501052566e-05 | |
| dG 2: -3.0145903134882e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926128768 | |
| StyleLoss:updateOutput self.G 2: 246.97813415527 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3314102648442e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.7845664084889e-05 | |
| dG 1: 4.0289756725542e-05 | |
| dG 2: 5.1661596008057e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845516096 | |
| StyleLoss:updateOutput self.G 2: 216.83280944824 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1697229729843e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00017907595611177 | |
| dG 1: 1.967398202396e-05 | |
| dG 2: 5.0453976933873e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93674840 | |
| StyleLoss:updateOutput self.G 2: 47.645492553711 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.1330152895399e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.5330852521583e-05 | |
| dG 1: -2.6632889671419e-07 | |
| dG 2: -1.3546188639118e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6921816.5 | |
| StyleLoss:updateOutput self.G 2: 14.082471847534 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2641758334885e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001358505571261 | |
| dG 1: -7.9309830880447e-07 | |
| dG 2: -1.6135625821589e-12 | |
| --- | |
| Iteration 15 / 2500 | |
| Content 1 loss: 4872648.828125 | |
| Style 1 loss: 763.547212 | |
| Style 2 loss: 561353.164673 | |
| Style 3 loss: 2117379.638672 | |
| Style 4 loss: 578060.211182 | |
| Style 5 loss: 45398.420334 | |
| Total loss: 8175603.810197 | |
| --- | |
| x1 value: -7.1821188926697 | |
| feval(x) grad value: 0.0015014637028798 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104486544 | |
| StyleLoss:updateOutput self.G 2: 6.713002204895 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2866567184774e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044053082820028 | |
| dG 1: -4.8795172915561e-05 | |
| dG 2: -3.1349690638588e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924977792 | |
| StyleLoss:updateOutput self.G 2: 246.83052062988 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9269105422381e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00015469496429432 | |
| dG 1: 2.227486402262e-05 | |
| dG 2: 2.8561974833863e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847002560 | |
| StyleLoss:updateOutput self.G 2: 217.2140045166 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.198256403105e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002934787189588 | |
| dG 1: 3.1306528399e-05 | |
| dG 2: 8.028567394236e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94083584 | |
| StyleLoss:updateOutput self.G 2: 47.853382110596 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.180550198882e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00041533054900356 | |
| dG 1: 1.3197559383116e-06 | |
| dG 2: 6.7126263315204e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7044565.5 | |
| StyleLoss:updateOutput self.G 2: 14.332203865051 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3831297464058e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0032298783771694 | |
| dG 1: 1.1122069736302e-06 | |
| dG 2: 2.2627904324585e-12 | |
| --- | |
| Iteration 16 / 2500 | |
| Content 1 loss: 4916187.890625 | |
| Style 1 loss: 727.587864 | |
| Style 2 loss: 484840.530396 | |
| Style 3 loss: 1896608.093262 | |
| Style 4 loss: 540207.366943 | |
| Style 5 loss: 44497.246742 | |
| Total loss: 7883068.715832 | |
| --- | |
| x1 value: -7.1925830841064 | |
| feval(x) grad value: 0.0069736619479954 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104365336 | |
| StyleLoss:updateOutput self.G 2: 6.7052145004272 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5479630728096e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -7.7453536505345e-05 | |
| dG 1: -5.2597453759518e-05 | |
| dG 2: -3.3792562931928e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922301824 | |
| StyleLoss:updateOutput self.G 2: 246.48738098145 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.2203477924072e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -8.389198774239e-05 | |
| dG 1: -1.961341513379e-05 | |
| dG 2: -2.5149311886491e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843062784 | |
| StyleLoss:updateOutput self.G 2: 216.20362854004 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.4462707687588e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.8149909212516e-06 | |
| dG 1: 4.7220157739503e-07 | |
| dG 2: 1.2109586879257e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93136656 | |
| StyleLoss:updateOutput self.G 2: 47.371753692627 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.4695662988233e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00047800102038309 | |
| dG 1: -2.3548307126475e-06 | |
| dG 2: -1.1977290903867e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6812098.5 | |
| StyleLoss:updateOutput self.G 2: 13.859251976013 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.1975670102329e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0055185398086905 | |
| dG 1: -2.4961370854726e-06 | |
| dG 2: -5.0784047106456e-12 | |
| --- | |
| Iteration 17 / 2500 | |
| Content 1 loss: 4859485.546875 | |
| Style 1 loss: 716.693491 | |
| Style 2 loss: 425892.974854 | |
| Style 3 loss: 1713025.634766 | |
| Style 4 loss: 513235.427856 | |
| Style 5 loss: 44005.248070 | |
| Total loss: 7556361.525911 | |
| --- | |
| x1 value: -7.2060642242432 | |
| feval(x) grad value: -0.0038297595456243 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104545712 | |
| StyleLoss:updateOutput self.G 2: 6.7168035507202 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.3428702412029e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0008308460819535 | |
| dG 1: -4.6938966988819e-05 | |
| dG 2: -3.0157126795771e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925889280 | |
| StyleLoss:updateOutput self.G 2: 246.94738769531 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.8645535721239e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037704169517383 | |
| dG 1: 3.6539699067362e-05 | |
| dG 2: 4.6853076973719e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 850343936 | |
| StyleLoss:updateOutput self.G 2: 218.07092285156 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0556384921756e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061951478710398 | |
| dG 1: 5.7457851653453e-05 | |
| dG 2: 1.4735081210748e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94816112 | |
| StyleLoss:updateOutput self.G 2: 48.225963592529 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9437679554812e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011683168122545 | |
| dG 1: 4.1623511606304e-06 | |
| dG 2: 2.1170816835725e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7165262 | |
| StyleLoss:updateOutput self.G 2: 14.57776260376 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1033250757464e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0066199516877532 | |
| dG 1: 2.9856762466807e-06 | |
| dG 2: 6.074375377696e-12 | |
| --- | |
| Iteration 18 / 2500 | |
| Content 1 loss: 4983195.703125 | |
| Style 1 loss: 656.264931 | |
| Style 2 loss: 381461.082458 | |
| Style 3 loss: 1581685.913086 | |
| Style 4 loss: 494416.717529 | |
| Style 5 loss: 44903.926849 | |
| Total loss: 7486319.607979 | |
| --- | |
| x1 value: -7.2180409431458 | |
| feval(x) grad value: 0.0082177715376019 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104329304 | |
| StyleLoss:updateOutput self.G 2: 6.7028999328613 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6739696557552e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00033779549994506 | |
| dG 1: -5.3727719205199e-05 | |
| dG 2: -3.4518733357802e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920858240 | |
| StyleLoss:updateOutput self.G 2: 246.3023223877 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.9463908262907e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00021970232774038 | |
| dG 1: -4.220614573569e-05 | |
| dG 2: -5.4118879516674e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841711744 | |
| StyleLoss:updateOutput self.G 2: 215.85717773438 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0075685114307e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0001255829265574 | |
| dG 1: -1.0100608051289e-05 | |
| dG 2: -2.5903034057972e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 92837728 | |
| StyleLoss:updateOutput self.G 2: 47.219711303711 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4340821508085e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00089922192273661 | |
| dG 1: -3.5148516417394e-06 | |
| dG 2: -1.7877459495413e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6760250 | |
| StyleLoss:updateOutput self.G 2: 13.753766059875 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2076682196493e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0072460090741515 | |
| dG 1: -3.300928256067e-06 | |
| dG 2: -6.7157564231923e-12 | |
| --- | |
| Iteration 19 / 2500 | |
| Content 1 loss: 4854566.015625 | |
| Style 1 loss: 691.687346 | |
| Style 2 loss: 339918.617249 | |
| Style 3 loss: 1439863.311768 | |
| Style 4 loss: 469208.541870 | |
| Style 5 loss: 42752.626419 | |
| Total loss: 7147000.800276 | |
| --- | |
| x1 value: -7.2327008247375 | |
| feval(x) grad value: -0.00023620287538506 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104450896 | |
| StyleLoss:updateOutput self.G 2: 6.7107124328613 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5463821152225e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00058106484357268 | |
| dG 1: -4.99133093399e-05 | |
| dG 2: -3.2068072156055e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923044096 | |
| StyleLoss:updateOutput self.G 2: 246.58258056641 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2568731833085e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00013372401008382 | |
| dG 1: -7.9932015069062e-06 | |
| dG 2: -1.0249284060349e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847565952 | |
| StyleLoss:updateOutput self.G 2: 217.35852050781 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.2844366033896e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00047843711217865 | |
| dG 1: 3.5716540878639e-05 | |
| dG 2: 9.1595168949521e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94192640 | |
| StyleLoss:updateOutput self.G 2: 47.90885925293 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.6347079647785e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00058937323046848 | |
| dG 1: 1.7429189256291e-06 | |
| dG 2: 8.8649470793636e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7040570.5 | |
| StyleLoss:updateOutput self.G 2: 14.324077606201 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.1020998625972e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0030612598638982 | |
| dG 1: 1.0502196801099e-06 | |
| dG 2: 2.1366779873189e-12 | |
| --- | |
| Iteration 20 / 2500 | |
| Content 1 loss: 4945599.218750 | |
| Style 1 loss: 649.208218 | |
| Style 2 loss: 303482.666016 | |
| Style 3 loss: 1305713.012695 | |
| Style 4 loss: 436827.484131 | |
| Style 5 loss: 38954.066277 | |
| Total loss: 7031225.656086 | |
| --- | |
| x1 value: -7.2473487854004 | |
| feval(x) grad value: 0.00065677892416716 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104418016 | |
| StyleLoss:updateOutput self.G 2: 6.7086005210876 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6575475920463e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00046997491153888 | |
| dG 1: -5.0944538088515e-05 | |
| dG 2: -3.2730604251213e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921838464 | |
| StyleLoss:updateOutput self.G 2: 246.42793273926 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.3631942503453e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 7.7364988101181e-05 | |
| dG 1: -2.6866502594203e-05 | |
| dG 2: -3.4449613300902e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846768320 | |
| StyleLoss:updateOutput self.G 2: 217.15394592285 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.7390005515099e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00045776620390825 | |
| dG 1: 2.9473252652679e-05 | |
| dG 2: 7.5584226377767e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94137096 | |
| StyleLoss:updateOutput self.G 2: 47.88060760498 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8718905999485e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00053172436309978 | |
| dG 1: 1.5274523548214e-06 | |
| dG 2: 7.7690241392925e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7020261.5 | |
| StyleLoss:updateOutput self.G 2: 14.282762527466 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.8310389527396e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0022986233234406 | |
| dG 1: 7.3498779329384e-07 | |
| dG 2: 1.4953365152018e-12 | |
| --- | |
| Iteration 21 / 2500 | |
| Content 1 loss: 4943478.906250 | |
| Style 1 loss: 629.280120 | |
| Style 2 loss: 272932.891846 | |
| Style 3 loss: 1198850.921631 | |
| Style 4 loss: 417740.936279 | |
| Style 5 loss: 37702.800751 | |
| Total loss: 6871335.736877 | |
| --- | |
| x1 value: -7.2619409561157 | |
| feval(x) grad value: 0.0086157331243157 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104313928 | |
| StyleLoss:updateOutput self.G 2: 6.7019128799438 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8542172947919e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00032011934672482 | |
| dG 1: -5.4209998779697e-05 | |
| dG 2: -3.4828589665081e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1918927360 | |
| StyleLoss:updateOutput self.G 2: 246.05470275879 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8133887280669e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00031084020156413 | |
| dG 1: -7.2428389103152e-05 | |
| dG 2: -9.287139633718e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841522048 | |
| StyleLoss:updateOutput self.G 2: 215.80857849121 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0920817078718e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.000118501840916 | |
| dG 1: -1.1583686500671e-05 | |
| dG 2: -2.9706389258199e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93057384 | |
| StyleLoss:updateOutput self.G 2: 47.331436157227 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2450996678126e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00080187822459266 | |
| dG 1: -2.6624068141246e-06 | |
| dG 2: -1.3541703565781e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6801982 | |
| StyleLoss:updateOutput self.G 2: 13.838667869568 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0939155572487e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0065634935162961 | |
| dG 1: -2.6531690764386e-06 | |
| dG 2: -5.3978874321736e-12 | |
| --- | |
| Iteration 22 / 2500 | |
| Content 1 loss: 4874487.890625 | |
| Style 1 loss: 626.455531 | |
| Style 2 loss: 252058.868408 | |
| Style 3 loss: 1122312.469482 | |
| Style 4 loss: 407755.325317 | |
| Style 5 loss: 38337.275505 | |
| Total loss: 6695578.284869 | |
| --- | |
| x1 value: -7.2782635688782 | |
| feval(x) grad value: -0.0039788912981749 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104563768 | |
| StyleLoss:updateOutput self.G 2: 6.717963218689 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.571767675543e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00094291073037311 | |
| dG 1: -4.637266829377e-05 | |
| dG 2: -2.9793296735942e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923496576 | |
| StyleLoss:updateOutput self.G 2: 246.64057922363 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7868872603799e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0003156803722959 | |
| dG 1: -9.125976703217e-07 | |
| dG 2: -1.1701789981878e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848945920 | |
| StyleLoss:updateOutput self.G 2: 217.71238708496 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2405200777721e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00073035346576944 | |
| dG 1: 4.6515888243448e-05 | |
| dG 2: 1.1929010730605e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94743272 | |
| StyleLoss:updateOutput self.G 2: 48.18892288208 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0818384882659e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001236331416294 | |
| dG 1: 3.8796697481303e-06 | |
| dG 2: 1.9733017303514e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7168276.5 | |
| StyleLoss:updateOutput self.G 2: 14.583897590637 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2045622952428e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0072273733094335 | |
| dG 1: 3.0324727049447e-06 | |
| dG 2: 6.1695804715045e-12 | |
| --- | |
| Iteration 23 / 2500 | |
| Content 1 loss: 4995851.953125 | |
| Style 1 loss: 554.318726 | |
| Style 2 loss: 231802.986145 | |
| Style 3 loss: 1061976.837158 | |
| Style 4 loss: 394045.440674 | |
| Style 5 loss: 39131.804466 | |
| Total loss: 6723363.340294 | |
| --- | |
| x1 value: -7.2940158843994 | |
| feval(x) grad value: 0.0077380677685142 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104441512 | |
| StyleLoss:updateOutput self.G 2: 6.7101092338562 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.800544317618e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00033774838084355 | |
| dG 1: -5.0207792810397e-05 | |
| dG 2: -3.2257265435154e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920116736 | |
| StyleLoss:updateOutput self.G 2: 246.20721435547 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.073298783183e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002214084379375 | |
| dG 1: -5.3814816055819e-05 | |
| dG 2: -6.9004094972736e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841813312 | |
| StyleLoss:updateOutput self.G 2: 215.88320922852 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1425236695904e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00010301792644896 | |
| dG 1: -9.3063636086299e-06 | |
| dG 2: -2.386619114142e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93211616 | |
| StyleLoss:updateOutput self.G 2: 47.409870147705 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1433505875402e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00073219917248935 | |
| dG 1: -2.0639704416681e-06 | |
| dG 2: -1.049789703951e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6875133.5 | |
| StyleLoss:updateOutput self.G 2: 13.987496376038 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0898335025049e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0042539001442492 | |
| dG 1: -1.5177005252553e-06 | |
| dG 2: -3.0877698401627e-12 | |
| --- | |
| Iteration 24 / 2500 | |
| Content 1 loss: 4902148.046875 | |
| Style 1 loss: 562.630221 | |
| Style 2 loss: 218041.763306 | |
| Style 3 loss: 970401.214600 | |
| Style 4 loss: 373006.759644 | |
| Style 5 loss: 35241.253853 | |
| Total loss: 6499401.668498 | |
| --- | |
| x1 value: -7.3120579719543 | |
| feval(x) grad value: 0.0022557263728231 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104596312 | |
| StyleLoss:updateOutput self.G 2: 6.720055103302 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6251827257038e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044817503658123 | |
| dG 1: -4.5351502194535e-05 | |
| dG 2: -2.9137222148923e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922617472 | |
| StyleLoss:updateOutput self.G 2: 246.52787780762 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0713416820352e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 1.9945291569456e-05 | |
| dG 1: -1.4669855772809e-05 | |
| dG 2: -1.8810445822498e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844676544 | |
| StyleLoss:updateOutput self.G 2: 216.61749267578 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.2322373989e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00032397653558291 | |
| dG 1: 1.3102183402225e-05 | |
| dG 2: 3.3600581084842e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93932176 | |
| StyleLoss:updateOutput self.G 2: 47.776378631592 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.673364983887e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002614397672005 | |
| dG 1: 7.3220417107223e-07 | |
| dG 2: 3.7241835049856e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6979056.5 | |
| StyleLoss:updateOutput self.G 2: 14.198925971985 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8102133588563e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00052861275617033 | |
| dG 1: 9.5373465569537e-08 | |
| dG 2: 1.9403781966604e-13 | |
| --- | |
| Iteration 25 / 2500 | |
| Content 1 loss: 4944239.453125 | |
| Style 1 loss: 516.437307 | |
| Style 2 loss: 204369.209290 | |
| Style 3 loss: 902065.795898 | |
| Style 4 loss: 355374.137878 | |
| Style 5 loss: 33731.675148 | |
| Total loss: 6440296.708646 | |
| --- | |
| x1 value: -7.3305654525757 | |
| feval(x) grad value: -0.00082882412243634 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104678352 | |
| StyleLoss:updateOutput self.G 2: 6.7253260612488 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5522963176836e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00056289066560566 | |
| dG 1: -4.2777894122992e-05 | |
| dG 2: -2.7483735773326e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923674496 | |
| StyleLoss:updateOutput self.G 2: 246.66339111328 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9782685711789e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00022323454322759 | |
| dG 1: 1.8744416365735e-06 | |
| dG 2: 2.4035070808615e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846579904 | |
| StyleLoss:updateOutput self.G 2: 217.1056060791 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.3794128019908e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00057841732632369 | |
| dG 1: 2.7998221412417e-05 | |
| dG 2: 7.1801523060522e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94305648 | |
| StyleLoss:updateOutput self.G 2: 47.966339111328 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2292302642436e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0007246655295603 | |
| dG 1: 2.1815294530825e-06 | |
| dG 2: 1.1095832369232e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7063522 | |
| StyleLoss:updateOutput self.G 2: 14.370775222778 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.093360068211e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0042560161091387 | |
| dG 1: 1.4064702327232e-06 | |
| dG 2: 2.8614714764341e-12 | |
| --- | |
| Iteration 26 / 2500 | |
| Content 1 loss: 4984906.250000 | |
| Style 1 loss: 485.272899 | |
| Style 2 loss: 191165.599823 | |
| Style 3 loss: 855723.358154 | |
| Style 4 loss: 345751.350403 | |
| Style 5 loss: 33726.725578 | |
| Total loss: 6411758.556858 | |
| --- | |
| x1 value: -7.348484992981 | |
| feval(x) grad value: 0.0093943299725652 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104544984 | |
| StyleLoss:updateOutput self.G 2: 6.7167572975159 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7969042071827e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00022473928402178 | |
| dG 1: -4.696180621977e-05 | |
| dG 2: -3.0171800387974e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920152704 | |
| StyleLoss:updateOutput self.G 2: 246.21183776855 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3052006586634e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00034126947866753 | |
| dG 1: -5.3248309995979e-05 | |
| dG 2: -6.8277718548448e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841156992 | |
| StyleLoss:updateOutput self.G 2: 215.71493530273 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.958503569379e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00023751173284836 | |
| dG 1: -1.4441930943576e-05 | |
| dG 2: -3.7036376569766e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93132320 | |
| StyleLoss:updateOutput self.G 2: 47.369560241699 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4008068660587e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00089356652460992 | |
| dG 1: -2.3715854240436e-06 | |
| dG 2: -1.2062509194624e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6829033.5 | |
| StyleLoss:updateOutput self.G 2: 13.893701553345 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0504481906537e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0063026887364686 | |
| dG 1: -2.2332847038342e-06 | |
| dG 2: -4.5436306626845e-12 | |
| --- | |
| Iteration 27 / 2500 | |
| Content 1 loss: 4904564.062500 | |
| Style 1 loss: 498.090416 | |
| Style 2 loss: 182105.758667 | |
| Style 3 loss: 818657.684326 | |
| Style 4 loss: 343617.393494 | |
| Style 5 loss: 34087.277412 | |
| Total loss: 6283530.266815 | |
| --- | |
| x1 value: -7.3674650192261 | |
| feval(x) grad value: -0.0041865394450724 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104723872 | |
| StyleLoss:updateOutput self.G 2: 6.7282495498657 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5981924046146e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00086687383009121 | |
| dG 1: -4.134995106142e-05 | |
| dG 2: -2.6566327263056e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923495552 | |
| StyleLoss:updateOutput self.G 2: 246.64044189453 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.873306310074e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00033330294536427 | |
| dG 1: -9.299751582148e-07 | |
| dG 2: -1.1924589462566e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848359808 | |
| StyleLoss:updateOutput self.G 2: 217.56204223633 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3286765465637e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00079096353147179 | |
| dG 1: 4.1927662095986e-05 | |
| dG 2: 1.0752361674637e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94729152 | |
| StyleLoss:updateOutput self.G 2: 48.181755065918 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1890973300742e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012993285199627 | |
| dG 1: 3.8249440876825e-06 | |
| dG 2: 1.945467441658e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7148309 | |
| StyleLoss:updateOutput self.G 2: 14.543272972107 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1995973636658e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0071975840255618 | |
| dG 1: 2.7225198664382e-06 | |
| dG 2: 5.5389811660922e-12 | |
| --- | |
| Iteration 28 / 2500 | |
| Content 1 loss: 5024055.468750 | |
| Style 1 loss: 443.643272 | |
| Style 2 loss: 167832.824707 | |
| Style 3 loss: 781191.558838 | |
| Style 4 loss: 330477.561951 | |
| Style 5 loss: 34230.031013 | |
| Total loss: 6338231.088531 | |
| --- | |
| x1 value: -7.3855237960815 | |
| feval(x) grad value: 0.0079806316643953 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104563248 | |
| StyleLoss:updateOutput self.G 2: 6.717930316925 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9084711406522e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00049564114306122 | |
| dG 1: -4.6388857299462e-05 | |
| dG 2: -2.9803692066371e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919516160 | |
| StyleLoss:updateOutput self.G 2: 246.13021850586 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2283055400862e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00028395050321706 | |
| dG 1: -6.321475666482e-05 | |
| dG 2: -8.1057200881918e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841945472 | |
| StyleLoss:updateOutput self.G 2: 215.9171295166 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4768028456256e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00019733564113267 | |
| dG 1: -8.2718761404976e-06 | |
| dG 2: -2.1213252508756e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93438320 | |
| StyleLoss:updateOutput self.G 2: 47.525188446045 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.9745022080479e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00060263753402978 | |
| dG 1: -1.1841626701425e-06 | |
| dG 2: -6.022963161198e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6889832 | |
| StyleLoss:updateOutput self.G 2: 14.017397880554 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0474021640621e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0042284419760108 | |
| dG 1: -1.2895669669888e-06 | |
| dG 2: -2.6236309132177e-12 | |
| --- | |
| Iteration 29 / 2500 | |
| Content 1 loss: 4944023.828125 | |
| Style 1 loss: 455.862120 | |
| Style 2 loss: 158792.633057 | |
| Style 3 loss: 716941.543579 | |
| Style 4 loss: 314120.910645 | |
| Style 5 loss: 31515.535355 | |
| Total loss: 6165850.312880 | |
| --- | |
| x1 value: -7.4047245979309 | |
| feval(x) grad value: -0.00014424696564674 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104730136 | |
| StyleLoss:updateOutput self.G 2: 6.7286524772644 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6743945603112e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010327212512493 | |
| dG 1: -4.1153532947646e-05 | |
| dG 2: -2.6440128298583e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922502016 | |
| StyleLoss:updateOutput self.G 2: 246.51304626465 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.4330045169168e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 7.8108241723385e-05 | |
| dG 1: -1.647945100558e-05 | |
| dG 2: -2.1130801101943e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845703808 | |
| StyleLoss:updateOutput self.G 2: 216.88095092773 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.3042515914021e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00053816457511857 | |
| dG 1: 2.1142554032849e-05 | |
| dG 2: 5.4220126652349e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94166320 | |
| StyleLoss:updateOutput self.G 2: 47.89546585083 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.171536041511e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00067709502764046 | |
| dG 1: 1.6408579313065e-06 | |
| dG 2: 8.3458343317841e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7007427 | |
| StyleLoss:updateOutput self.G 2: 14.256644248962 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.4882532418123e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0020929514430463 | |
| dG 1: 5.357350119084e-07 | |
| dG 2: 1.0899554913138e-12 | |
| --- | |
| Iteration 30 / 2500 | |
| Content 1 loss: 4982285.937500 | |
| Style 1 loss: 414.917707 | |
| Style 2 loss: 149816.219330 | |
| Style 3 loss: 686913.482666 | |
| Style 4 loss: 305772.628784 | |
| Style 5 loss: 30479.905128 | |
| Total loss: 6155683.091116 | |
| --- | |
| x1 value: -7.4232187271118 | |
| feval(x) grad value: 0.0043642930686474 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104700320 | |
| StyleLoss:updateOutput self.G 2: 6.7267360687256 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8293347987283e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00079151114914566 | |
| dG 1: -4.2088991904166e-05 | |
| dG 2: -2.7041138425665e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921686144 | |
| StyleLoss:updateOutput self.G 2: 246.40843200684 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2069961413962e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.6330575565225e-05 | |
| dG 1: -2.9250661100377e-05 | |
| dG 2: -3.7506699135381e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843674624 | |
| StyleLoss:updateOutput self.G 2: 216.36054992676 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3656958876047e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 8.7704807810951e-05 | |
| dG 1: 5.2617351684603e-06 | |
| dG 2: 1.3493730872255e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93667624 | |
| StyleLoss:updateOutput self.G 2: 47.641819000244 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4445123137393e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00032152773928829 | |
| dG 1: -2.9432737846946e-07 | |
| dG 2: -1.4970261087623e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6979333.5 | |
| StyleLoss:updateOutput self.G 2: 14.199494361877 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.2098911023913e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00025259362882935 | |
| dG 1: 9.9705637524039e-08 | |
| dG 2: 2.0285166504441e-13 | |
| --- | |
| Iteration 31 / 2500 | |
| Content 1 loss: 4990193.750000 | |
| Style 1 loss: 396.047920 | |
| Style 2 loss: 139300.266266 | |
| Style 3 loss: 669748.077393 | |
| Style 4 loss: 307731.193542 | |
| Style 5 loss: 30668.226242 | |
| Total loss: 6138037.561363 | |
| --- | |
| x1 value: -7.4411554336548 | |
| feval(x) grad value: 0.00070443010190502 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104822720 | |
| StyleLoss:updateOutput self.G 2: 6.734601020813 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.597676195317e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012565876822919 | |
| dG 1: -3.8248930650298e-05 | |
| dG 2: -2.4573990845683e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923718912 | |
| StyleLoss:updateOutput self.G 2: 246.66911315918 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0323458471694e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -1.5092614376044e-05 | |
| dG 1: 2.5703859591886e-06 | |
| dG 2: 3.2958816340196e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844650368 | |
| StyleLoss:updateOutput self.G 2: 216.61077880859 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.1573296622773e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037094246363267 | |
| dG 1: 1.289800820814e-05 | |
| dG 2: 3.3076974319257e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94040384 | |
| StyleLoss:updateOutput self.G 2: 47.831420898438 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0770227839366e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00047152044135146 | |
| dG 1: 1.1521296983119e-06 | |
| dG 2: 5.8600341376266e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6970844.5 | |
| StyleLoss:updateOutput self.G 2: 14.182217597961 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.2861621807333e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00025716976961121 | |
| dG 1: -3.2087424273186e-08 | |
| dG 2: -6.5282021867279e-14 | |
| --- | |
| Iteration 32 / 2500 | |
| Content 1 loss: 4968328.906250 | |
| Style 1 loss: 372.646585 | |
| Style 2 loss: 136889.350891 | |
| Style 3 loss: 651129.409790 | |
| Style 4 loss: 303308.876038 | |
| Style 5 loss: 29757.496834 | |
| Total loss: 6089786.686388 | |
| --- | |
| x1 value: -7.4587087631226 | |
| feval(x) grad value: 0.0021250597201288 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104762144 | |
| StyleLoss:updateOutput self.G 2: 6.7307090759277 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8140663011509e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00017331558046862 | |
| dG 1: -4.0149090636987e-05 | |
| dG 2: -2.5794798155093e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922326784 | |
| StyleLoss:updateOutput self.G 2: 246.49053955078 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.7239638039921e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.2353955551516e-05 | |
| dG 1: -1.9224829884479e-05 | |
| dG 2: -2.4651071114934e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844119552 | |
| StyleLoss:updateOutput self.G 2: 216.47468566895 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.3404571553319e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00023031384625938 | |
| dG 1: 8.7447233454441e-06 | |
| dG 2: 2.2425863249742e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93942912 | |
| StyleLoss:updateOutput self.G 2: 47.781841278076 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.4025280370997e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00017919093079399 | |
| dG 1: 7.7392570574375e-07 | |
| dG 2: 3.9363900593975e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7005247.5 | |
| StyleLoss:updateOutput self.G 2: 14.252216339111 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0405570328185e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018243341473863 | |
| dG 1: 5.0192613798572e-07 | |
| dG 2: 1.021171537087e-12 | |
| --- | |
| Iteration 33 / 2500 | |
| Content 1 loss: 5002023.046875 | |
| Style 1 loss: 360.714555 | |
| Style 2 loss: 124416.023254 | |
| Style 3 loss: 591710.815430 | |
| Style 4 loss: 284179.504395 | |
| Style 5 loss: 28917.540550 | |
| Total loss: 6031607.645059 | |
| --- | |
| x1 value: -7.4765295982361 | |
| feval(x) grad value: 0.0052990168333054 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104719560 | |
| StyleLoss:updateOutput self.G 2: 6.727972984314 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.929993124847e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00032157637178898 | |
| dG 1: -4.1485000110697e-05 | |
| dG 2: -2.6653091626111e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921089536 | |
| StyleLoss:updateOutput self.G 2: 246.33197021484 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.262779836253e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00011584487219807 | |
| dG 1: -3.8582875276916e-05 | |
| dG 2: -4.9472934429695e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843060416 | |
| StyleLoss:updateOutput self.G 2: 216.20306396484 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4272240056457e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.7720292564481e-05 | |
| dG 1: 4.5495463041334e-07 | |
| dG 2: 1.1667324550067e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93715256 | |
| StyleLoss:updateOutput self.G 2: 47.666053771973 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6278842213401e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00020353596482892 | |
| dG 1: -1.0950186180025e-07 | |
| dG 2: -5.5695523198821e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6929340.5 | |
| StyleLoss:updateOutput self.G 2: 14.097779273987 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2959652546415e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0025775795802474 | |
| dG 1: -6.7629895283972e-07 | |
| dG 2: -1.3759335467864e-12 | |
| --- | |
| Iteration 34 / 2500 | |
| Content 1 loss: 4983069.140625 | |
| Style 1 loss: 354.992531 | |
| Style 2 loss: 117969.188690 | |
| Style 3 loss: 564277.038574 | |
| Style 4 loss: 275637.107849 | |
| Style 5 loss: 28269.616127 | |
| Total loss: 5969577.084397 | |
| --- | |
| x1 value: -7.494658946991 | |
| feval(x) grad value: -0.0034482714254409 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104860480 | |
| StyleLoss:updateOutput self.G 2: 6.7370266914368 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6976659007214e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016253122594208 | |
| dG 1: -3.7064462958369e-05 | |
| dG 2: -2.3813000182837e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923696256 | |
| StyleLoss:updateOutput self.G 2: 246.66619873047 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9612443225014e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00017912109615281 | |
| dG 1: 2.2140054625197e-06 | |
| dG 2: 2.8389069524001e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846408128 | |
| StyleLoss:updateOutput self.G 2: 217.0615234375 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2913761793243e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00071561924414709 | |
| dG 1: 2.6654062821763e-05 | |
| dG 2: 6.8354401999704e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94552720 | |
| StyleLoss:updateOutput self.G 2: 48.09200668335 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1395082683284e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001291916007176 | |
| dG 1: 3.1402323656948e-06 | |
| dG 2: 1.5972047903018e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7068676 | |
| StyleLoss:updateOutput self.G 2: 14.381259918213 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8759389882398e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0053255632519722 | |
| dG 1: 1.4864662034597e-06 | |
| dG 2: 3.0242232329503e-12 | |
| --- | |
| Iteration 35 / 2500 | |
| Content 1 loss: 5016544.140625 | |
| Style 1 loss: 325.477034 | |
| Style 2 loss: 114866.741180 | |
| Style 3 loss: 566627.334595 | |
| Style 4 loss: 278069.526672 | |
| Style 5 loss: 28321.257591 | |
| Total loss: 6004754.477698 | |
| --- | |
| x1 value: -7.5119791030884 | |
| feval(x) grad value: 0.012597248889506 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104658104 | |
| StyleLoss:updateOutput self.G 2: 6.724024772644 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.1571433345998e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0018762013642117 | |
| dG 1: -4.3413325329311e-05 | |
| dG 2: -2.7891985596162e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919255808 | |
| StyleLoss:updateOutput self.G 2: 246.09680175781 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0861877437237e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00044015407911502 | |
| dG 1: -6.7291046434548e-05 | |
| dG 2: -8.6284035472062e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 839712832 | |
| StyleLoss:updateOutput self.G 2: 215.34457397461 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2654254533118e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0006958874873817 | |
| dG 1: -2.5744951926754e-05 | |
| dG 2: -6.6022994363313e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93015608 | |
| StyleLoss:updateOutput self.G 2: 47.31018447876 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1873509581383e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013775496045128 | |
| dG 1: -2.8245115117898e-06 | |
| dG 2: -1.4366210044497e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6852090.5 | |
| StyleLoss:updateOutput self.G 2: 13.940613746643 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0560838745732e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0063365017995238 | |
| dG 1: -1.8753851236397e-06 | |
| dG 2: -3.8154817846858e-12 | |
| --- | |
| Iteration 36 / 2500 | |
| Content 1 loss: 4963532.421875 | |
| Style 1 loss: 336.544789 | |
| Style 2 loss: 109100.234985 | |
| Style 3 loss: 554424.041748 | |
| Style 4 loss: 276605.209351 | |
| Style 5 loss: 28791.111946 | |
| Total loss: 5932789.564694 | |
| --- | |
| x1 value: -7.5300879478455 | |
| feval(x) grad value: -0.0080368630588055 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105027352 | |
| StyleLoss:updateOutput self.G 2: 6.747748374939 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.441848489023e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001733886427246 | |
| dG 1: -3.1829324143473e-05 | |
| dG 2: -2.0449553508883e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926605184 | |
| StyleLoss:updateOutput self.G 2: 247.03923034668 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.2924995941339e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00055671675363556 | |
| dG 1: 4.7750250814715e-05 | |
| dG 2: 6.1227811015696e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 849555264 | |
| StyleLoss:updateOutput self.G 2: 217.86866760254 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.939070273238e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011329686967656 | |
| dG 1: 5.1284932851559e-05 | |
| dG 2: 1.3152033281894e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94922128 | |
| StyleLoss:updateOutput self.G 2: 48.279895782471 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9049672889414e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017482230905443 | |
| dG 1: 4.5737820073555e-06 | |
| dG 2: 2.3263463638096e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7145628.5 | |
| StyleLoss:updateOutput self.G 2: 14.537818908691 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3374020682022e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0080244122073054 | |
| dG 1: 2.6809150313056e-06 | |
| dG 2: 5.4543362014436e-12 | |
| --- | |
| Iteration 37 / 2500 | |
| Content 1 loss: 5060733.984375 | |
| Style 1 loss: 278.056696 | |
| Style 2 loss: 106833.984375 | |
| Style 3 loss: 542111.572266 | |
| Style 4 loss: 266364.624023 | |
| Style 5 loss: 28874.287605 | |
| Total loss: 6005196.509340 | |
| --- | |
| x1 value: -7.5461349487305 | |
| feval(x) grad value: 0.0087035279721022 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104805688 | |
| StyleLoss:updateOutput self.G 2: 6.7335071563721 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.947770193553e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -2.856427045117e-06 | |
| dG 1: -3.87831023545e-05 | |
| dG 2: -2.4917186369766e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921518208 | |
| StyleLoss:updateOutput self.G 2: 246.38693237305 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2079085287553e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00034367345506325 | |
| dG 1: -3.1877381843515e-05 | |
| dG 2: -4.0874812215486e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841421952 | |
| StyleLoss:updateOutput self.G 2: 215.78288269043 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0337482687391e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002397593925707 | |
| dG 1: -1.2367573617666e-05 | |
| dG 2: -3.1716671389931e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93231752 | |
| StyleLoss:updateOutput self.G 2: 47.420127868652 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3119272068707e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0008323768270202 | |
| dG 1: -1.9858084669977e-06 | |
| dG 2: -1.0100342871106e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6834229 | |
| StyleLoss:updateOutput self.G 2: 13.904274940491 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1453010984042e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0068718087859452 | |
| dG 1: -2.152622982976e-06 | |
| dG 2: -4.379522786091e-12 | |
| --- | |
| Iteration 38 / 2500 | |
| Content 1 loss: 4951239.062500 | |
| Style 1 loss: 300.216816 | |
| Style 2 loss: 100425.853729 | |
| Style 3 loss: 516309.310913 | |
| Style 4 loss: 271154.571533 | |
| Style 5 loss: 28359.383583 | |
| Total loss: 5867788.399075 | |
| --- | |
| x1 value: -7.5629057884216 | |
| feval(x) grad value: 0.00042490439955145 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104840768 | |
| StyleLoss:updateOutput self.G 2: 6.7357606887817 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9942169511041e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00052126741502434 | |
| dG 1: -3.7682726542698e-05 | |
| dG 2: -2.4210221501175e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922403200 | |
| StyleLoss:updateOutput self.G 2: 246.50035095215 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.229150419808e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00018680161156226 | |
| dG 1: -1.8027754776995e-05 | |
| dG 2: -2.3116098878811e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844850304 | |
| StyleLoss:updateOutput self.G 2: 216.66206359863 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.3944965000264e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00031441915780306 | |
| dG 1: 1.4462844774243e-05 | |
| dG 2: 3.7090001209217e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94090592 | |
| StyleLoss:updateOutput self.G 2: 47.856941223145 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.8342295378443e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00040023867040873 | |
| dG 1: 1.3469251598508e-06 | |
| dG 2: 6.8508176562232e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7080508 | |
| StyleLoss:updateOutput self.G 2: 14.405331611633 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.2026590436944e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0055215964093804 | |
| dG 1: 1.6701183085388e-06 | |
| dG 2: 3.39786488876e-12 | |
| --- | |
| Iteration 39 / 2500 | |
| Content 1 loss: 5044917.187500 | |
| Style 1 loss: 276.602268 | |
| Style 2 loss: 93300.785065 | |
| Style 3 loss: 507079.925537 | |
| Style 4 loss: 263495.155334 | |
| Style 5 loss: 27613.265991 | |
| Total loss: 5936682.921696 | |
| --- | |
| x1 value: -7.5790338516235 | |
| feval(x) grad value: 0.0028323333244771 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104873464 | |
| StyleLoss:updateOutput self.G 2: 6.7378611564636 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8979153177697e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00043178113992326 | |
| dG 1: -3.6657009331975e-05 | |
| dG 2: -2.3551221736695e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922565888 | |
| StyleLoss:updateOutput self.G 2: 246.52127075195 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.8316536589185e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00010670888150344 | |
| dG 1: -1.5478435670957e-05 | |
| dG 2: -1.9847246676002e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844096384 | |
| StyleLoss:updateOutput self.G 2: 216.46875 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.2531156313762e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00027618117746897 | |
| dG 1: 8.5623642007704e-06 | |
| dG 2: 2.1958201316258e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93889448 | |
| StyleLoss:updateOutput self.G 2: 47.754642486572 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.5255450114846e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00016582234820817 | |
| dG 1: 5.6640482171133e-07 | |
| dG 2: 2.8808845500133e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6962066.5 | |
| StyleLoss:updateOutput self.G 2: 14.164361953735 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0325393873245e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061952380929142 | |
| dG 1: -1.6833470795063e-07 | |
| dG 2: -3.4247775513967e-13 | |
| --- | |
| Iteration 40 / 2500 | |
| Content 1 loss: 5006095.312500 | |
| Style 1 loss: 271.898925 | |
| Style 2 loss: 90415.409088 | |
| Style 3 loss: 460170.135498 | |
| Style 4 loss: 247178.352356 | |
| Style 5 loss: 25777.127266 | |
| Total loss: 5829908.235633 | |
| --- | |
| x1 value: -7.5950794219971 | |
| feval(x) grad value: 0.0022976663894951 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104949880 | |
| StyleLoss:updateOutput self.G 2: 6.7427711486816 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7948461201486e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00063338945619762 | |
| dG 1: -3.4259537642356e-05 | |
| dG 2: -2.2010902215458e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923409152 | |
| StyleLoss:updateOutput self.G 2: 246.62936401367 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3859231096092e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -4.9099580792245e-06 | |
| dG 1: -2.2796320990892e-06 | |
| dG 2: -2.9230586592703e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843964032 | |
| StyleLoss:updateOutput self.G 2: 216.43481445312 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.7707584488753e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00035374853177927 | |
| dG 1: 7.527071829827e-06 | |
| dG 2: 1.9303200531062e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93810944 | |
| StyleLoss:updateOutput self.G 2: 47.714702606201 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5915762929808e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 7.3860945121851e-05 | |
| dG 1: 2.61728928308e-07 | |
| dG 2: 1.3312221876054e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6950962.5 | |
| StyleLoss:updateOutput self.G 2: 14.14176940918 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2937067001294e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013762240996584 | |
| dG 1: -3.4067866749865e-07 | |
| dG 2: -6.9311283058468e-13 | |
| --- | |
| Iteration 41 / 2500 | |
| Content 1 loss: 5002139.453125 | |
| Style 1 loss: 250.081860 | |
| Style 2 loss: 88043.054581 | |
| Style 3 loss: 453883.209229 | |
| Style 4 loss: 246555.427551 | |
| Style 5 loss: 25584.880829 | |
| Total loss: 5816456.107174 | |
| --- | |
| x1 value: -7.6117324829102 | |
| feval(x) grad value: -0.00024894968373701 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104922432 | |
| StyleLoss:updateOutput self.G 2: 6.7410078048706 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9337561591756e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037200635415502 | |
| dG 1: -3.5120770917274e-05 | |
| dG 2: -2.2564229130995e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923026432 | |
| StyleLoss:updateOutput self.G 2: 246.58030700684 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.8158282634224e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00022779990104027 | |
| dG 1: -8.269856152765e-06 | |
| dG 2: -1.0604039347994e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844725760 | |
| StyleLoss:updateOutput self.G 2: 216.63009643555 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.2462364291396e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00029335732688196 | |
| dG 1: 1.3487106116372e-05 | |
| dG 2: 3.458771246842e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94162488 | |
| StyleLoss:updateOutput self.G 2: 47.893531799316 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.4067750094619e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00047990499297157 | |
| dG 1: 1.6260260053969e-06 | |
| dG 2: 8.2703977130269e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7072139.5 | |
| StyleLoss:updateOutput self.G 2: 14.388308525085 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.6289890077751e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0051773944869637 | |
| dG 1: 1.5402373492179e-06 | |
| dG 2: 3.1336205331967e-12 | |
| --- | |
| Iteration 42 / 2500 | |
| Content 1 loss: 5054686.718750 | |
| Style 1 loss: 242.404282 | |
| Style 2 loss: 83207.427979 | |
| Style 3 loss: 461746.994019 | |
| Style 4 loss: 250754.905701 | |
| Style 5 loss: 26494.368553 | |
| Total loss: 5877132.819283 | |
| --- | |
| x1 value: -7.6270704269409 | |
| feval(x) grad value: 0.0080332038924098 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104860576 | |
| StyleLoss:updateOutput self.G 2: 6.737033367157 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.0327354728097e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00030797996441834 | |
| dG 1: -3.706125789904e-05 | |
| dG 2: -2.381094019871e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921182592 | |
| StyleLoss:updateOutput self.G 2: 246.3438873291 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.42792133065e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00040859519504011 | |
| dG 1: -3.7129819247639e-05 | |
| dG 2: -4.7609767690748e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841717312 | |
| StyleLoss:updateOutput self.G 2: 215.85862731934 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.2321425414448e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00033570305095054 | |
| dG 1: -1.0056627615995e-05 | |
| dG 2: -2.5790244505969e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93487160 | |
| StyleLoss:updateOutput self.G 2: 47.550033569336 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.3282878310674e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061961222672835 | |
| dG 1: -9.9467968084355e-07 | |
| dG 2: -5.0592033817506e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6883690 | |
| StyleLoss:updateOutput self.G 2: 14.004902839661 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.8770860884324e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0053262510336936 | |
| dG 1: -1.3848983826392e-06 | |
| dG 2: -2.8175834061728e-12 | |
| --- | |
| Iteration 43 / 2500 | |
| Content 1 loss: 4987405.859375 | |
| Style 1 loss: 250.627287 | |
| Style 2 loss: 81567.478180 | |
| Style 3 loss: 431004.180908 | |
| Style 4 loss: 240995.933533 | |
| Style 5 loss: 25734.289169 | |
| Total loss: 5766958.368452 | |
| --- | |
| x1 value: -7.6421527862549 | |
| feval(x) grad value: -0.0084492713212967 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105125312 | |
| StyleLoss:updateOutput self.G 2: 6.7540416717529 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4691923051232e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018252836307511 | |
| dG 1: -2.8756261599483e-05 | |
| dG 2: -1.8475186658318e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926180608 | |
| StyleLoss:updateOutput self.G 2: 246.98472595215 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3620443196678e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00054443342378363 | |
| dG 1: 4.109832298127e-05 | |
| dG 2: 5.2698383851135e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848763328 | |
| StyleLoss:updateOutput self.G 2: 217.66561889648 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9388527050523e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011336465831846 | |
| dG 1: 4.508750862442e-05 | |
| dG 2: 1.1562700449885e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94771096 | |
| StyleLoss:updateOutput self.G 2: 48.203071594238 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.7929291945838e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016786608612165 | |
| dG 1: 3.9876163100416e-06 | |
| dG 2: 2.0282063788873e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7119432.5 | |
| StyleLoss:updateOutput self.G 2: 14.484524726868 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2670813021032e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0076024890877306 | |
| dG 1: 2.2743138288206e-06 | |
| dG 2: 4.627103821625e-12 | |
| --- | |
| Iteration 44 / 2500 | |
| Content 1 loss: 5071228.515625 | |
| Style 1 loss: 206.608929 | |
| Style 2 loss: 81130.897522 | |
| Style 3 loss: 454019.943237 | |
| Style 4 loss: 240658.241272 | |
| Style 5 loss: 26147.775650 | |
| Total loss: 5873391.982235 | |
| --- | |
| x1 value: -7.6558136940002 | |
| feval(x) grad value: 0.012901566922665 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104826624 | |
| StyleLoss:updateOutput self.G 2: 6.7348499298096 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.1956888569293e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020477862562984 | |
| dG 1: -3.8127105653984e-05 | |
| dG 2: -2.4495722290852e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919738496 | |
| StyleLoss:updateOutput self.G 2: 246.15872192383 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.0419011188296e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00045837290235795 | |
| dG 1: -5.9735088143498e-05 | |
| dG 2: -7.6595388018297e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 839952064 | |
| StyleLoss:updateOutput self.G 2: 215.40592956543 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4312254847937e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00077564967796206 | |
| dG 1: -2.3872127712821e-05 | |
| dG 2: -6.1220121853889e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93008240 | |
| StyleLoss:updateOutput self.G 2: 47.306442260742 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3866192577771e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0015125680947676 | |
| dG 1: -2.8530812414829e-06 | |
| dG 2: -1.4511526745878e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6854406 | |
| StyleLoss:updateOutput self.G 2: 13.94532585144 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1317717962811e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.006790631916374 | |
| dG 1: -1.8394343896944e-06 | |
| dG 2: -3.7423393377256e-12 | |
| --- | |
| Iteration 45 / 2500 | |
| Content 1 loss: 4992783.203125 | |
| Style 1 loss: 235.910609 | |
| Style 2 loss: 76707.681656 | |
| Style 3 loss: 425810.165405 | |
| Style 4 loss: 238984.794617 | |
| Style 5 loss: 25959.385872 | |
| Total loss: 5760481.141284 | |
| --- | |
| x1 value: -7.6701307296753 | |
| feval(x) grad value: -0.0061299353837967 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105123840 | |
| StyleLoss:updateOutput self.G 2: 6.7539467811584 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4709867808024e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016575622139499 | |
| dG 1: -2.8802562155761e-05 | |
| dG 2: -1.8504934997526e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925416448 | |
| StyleLoss:updateOutput self.G 2: 246.88677978516 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.158869515436e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00033207569504157 | |
| dG 1: 2.9140868718969e-05 | |
| dG 2: 3.7365921988497e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846674432 | |
| StyleLoss:updateOutput self.G 2: 217.12989807129 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5781463957865e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00087994622299448 | |
| dG 1: 2.8738997571054e-05 | |
| dG 2: 7.3701236086299e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94490064 | |
| StyleLoss:updateOutput self.G 2: 48.060131072998 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2509239272495e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013410046231002 | |
| dG 1: 2.897064860008e-06 | |
| dG 2: 1.4735237769542e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7062920.5 | |
| StyleLoss:updateOutput self.G 2: 14.369547843933 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.2488335212693e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0055493009276688 | |
| dG 1: 1.3971208545627e-06 | |
| dG 2: 2.84245023352e-12 | |
| --- | |
| Iteration 46 / 2500 | |
| Content 1 loss: 5055642.578125 | |
| Style 1 loss: 199.827231 | |
| Style 2 loss: 75438.583374 | |
| Style 3 loss: 405129.592896 | |
| Style 4 loss: 227983.200073 | |
| Style 5 loss: 24562.826157 | |
| Total loss: 5788956.607856 | |
| --- | |
| x1 value: -7.6825637817383 | |
| feval(x) grad value: 0.0029209291096777 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105063976 | |
| StyleLoss:updateOutput self.G 2: 6.7501010894775 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7108226652881e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -9.4034236099105e-05 | |
| dG 1: -3.06805522996e-05 | |
| dG 2: -1.9711498058794e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923287808 | |
| StyleLoss:updateOutput self.G 2: 246.61381530762 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0938014050055e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -4.5043281716062e-05 | |
| dG 1: -4.1793537093326e-06 | |
| dG 2: -5.3589820298364e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843303488 | |
| StyleLoss:updateOutput self.G 2: 216.26538085938 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.4251129104869e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00018255600298289 | |
| dG 1: 2.357856146773e-06 | |
| dG 2: 6.0467267044145e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93695176 | |
| StyleLoss:updateOutput self.G 2: 47.655830383301 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.372282719738e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00020367282559164 | |
| dG 1: -1.8746328578345e-07 | |
| dG 2: -9.5348716715096e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6953892 | |
| StyleLoss:updateOutput self.G 2: 14.147729873657 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1320461485175e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0012792279012501 | |
| dG 1: -2.9522570343943e-07 | |
| dG 2: -6.0063829994753e-13 | |
| --- | |
| Iteration 47 / 2500 | |
| Content 1 loss: 5021597.265625 | |
| Style 1 loss: 196.234524 | |
| Style 2 loss: 72160.297394 | |
| Style 3 loss: 392935.867310 | |
| Style 4 loss: 225502.761841 | |
| Style 5 loss: 23949.497223 | |
| Total loss: 5736341.923916 | |
| --- | |
| x1 value: -7.6957516670227 | |
| feval(x) grad value: 0.0042492439970374 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104980976 | |
| StyleLoss:updateOutput self.G 2: 6.7447681427002 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8928214368971e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00052122079068795 | |
| dG 1: -3.3284282835666e-05 | |
| dG 2: -2.1384330937957e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921950208 | |
| StyleLoss:updateOutput self.G 2: 246.44230651855 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7651155204135e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -1.9208942831028e-05 | |
| dG 1: -2.5118082703557e-05 | |
| dG 2: -3.2207684869806e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843388864 | |
| StyleLoss:updateOutput self.G 2: 216.28726196289 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.3451222054312e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 3.515938487908e-06 | |
| dG 1: 3.0250002964749e-06 | |
| dG 2: 7.7576194166401e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93836088 | |
| StyleLoss:updateOutput self.G 2: 47.727512359619 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1326787830466e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -5.9575468185358e-05 | |
| dG 1: 3.5937537745667e-07 | |
| dG 2: 1.8278781266368e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6983243 | |
| StyleLoss:updateOutput self.G 2: 14.207443237305 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0036975339744e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00060221867170185 | |
| dG 1: 1.603679748996e-07 | |
| dG 2: 3.262695289722e-13 | |
| --- | |
| Iteration 48 / 2500 | |
| Content 1 loss: 5046206.250000 | |
| Style 1 loss: 208.675571 | |
| Style 2 loss: 69135.457993 | |
| Style 3 loss: 392068.130493 | |
| Style 4 loss: 224312.461853 | |
| Style 5 loss: 24063.474655 | |
| Total loss: 5755994.450565 | |
| --- | |
| x1 value: -7.7082948684692 | |
| feval(x) grad value: -0.003086781129241 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105136288 | |
| StyleLoss:updateOutput self.G 2: 6.7547473907471 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4965485557213e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013173661427572 | |
| dG 1: -2.8411830498953e-05 | |
| dG 2: -1.8253900994913e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924490112 | |
| StyleLoss:updateOutput self.G 2: 246.7679901123 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1490660745703e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012625438102987 | |
| dG 1: 1.4637847925769e-05 | |
| dG 2: 1.8769400096652e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844726016 | |
| StyleLoss:updateOutput self.G 2: 216.6301574707 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0364926339435e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0005597168346867 | |
| dG 1: 1.3489408956957e-05 | |
| dG 2: 3.459362137026e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94127768 | |
| StyleLoss:updateOutput self.G 2: 47.875858306885 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3053833924914e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00076963729225099 | |
| dG 1: 1.4912174037818e-06 | |
| dG 2: 7.5847244066993e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6991417.5 | |
| StyleLoss:updateOutput self.G 2: 14.224080085754 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.4234799411715e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014540880220011 | |
| dG 1: 2.8727706080645e-07 | |
| dG 2: 5.8446666560338e-13 | |
| --- | |
| Iteration 49 / 2500 | |
| Content 1 loss: 5037507.421875 | |
| Style 1 loss: 183.807168 | |
| Style 2 loss: 68266.994476 | |
| Style 3 loss: 376666.442871 | |
| Style 4 loss: 221300.926208 | |
| Style 5 loss: 23509.497643 | |
| Total loss: 5727435.090242 | |
| --- | |
| x1 value: -7.7192621231079 | |
| feval(x) grad value: 0.0057037817314267 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105061280 | |
| StyleLoss:updateOutput self.G 2: 6.749927520752 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7768779154467e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011064102873206 | |
| dG 1: -3.0765175324632e-05 | |
| dG 2: -1.9765864292531e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922399744 | |
| StyleLoss:updateOutput self.G 2: 246.49995422363 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.4606569810953e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -5.6853838032112e-05 | |
| dG 1: -1.8077073036693e-05 | |
| dG 2: -2.3179346896746e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842240832 | |
| StyleLoss:updateOutput self.G 2: 215.99282836914 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9478586561236e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00021465279860422 | |
| dG 1: -5.9602066357911e-06 | |
| dG 2: -1.5284966218068e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93528504 | |
| StyleLoss:updateOutput self.G 2: 47.571063995361 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.063779393462e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00064475205726922 | |
| dG 1: -8.3421235785863e-07 | |
| dG 2: -4.2430235757188e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6964683 | |
| StyleLoss:updateOutput self.G 2: 14.169687271118 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3132510900959e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00078795052831993 | |
| dG 1: -1.2769930890499e-07 | |
| dG 2: -2.5980490003276e-13 | |
| --- | |
| Iteration 50 / 2500 | |
| Content 1 loss: 5034939.062500 | |
| Style 1 loss: 184.282400 | |
| Style 2 loss: 65766.174316 | |
| Style 3 loss: 364825.355530 | |
| Style 4 loss: 217075.057983 | |
| Style 5 loss: 23277.833462 | |
| Total loss: 5706067.766191 | |
| --- | |
| x1 value: -7.7312960624695 | |
| feval(x) grad value: -0.0042577632702887 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105158864 | |
| StyleLoss:updateOutput self.G 2: 6.7561964988708 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4051660097466e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013786444906145 | |
| dG 1: -2.7704076273949e-05 | |
| dG 2: -1.7799184435369e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924517376 | |
| StyleLoss:updateOutput self.G 2: 246.77146911621 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5152477434176e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00026342130149715 | |
| dG 1: 1.506699663878e-05 | |
| dG 2: 1.9319676067275e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846284544 | |
| StyleLoss:updateOutput self.G 2: 217.02990722656 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3310892654772e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075457303319126 | |
| dG 1: 2.5687713787192e-05 | |
| dG 2: 6.5876197725967e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94423808 | |
| StyleLoss:updateOutput self.G 2: 48.026439666748 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0647695464504e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011867018183693 | |
| dG 1: 2.6400198294141e-06 | |
| dG 2: 1.3427834148413e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7035626.5 | |
| StyleLoss:updateOutput self.G 2: 14.314019203186 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.8803120711891e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0041281883604825 | |
| dG 1: 9.734675359141e-07 | |
| dG 2: 1.9805246852245e-12 | |
| --- | |
| Iteration 51 / 2500 | |
| Content 1 loss: 5069026.953125 | |
| Style 1 loss: 181.402937 | |
| Style 2 loss: 66841.730118 | |
| Style 3 loss: 376711.761475 | |
| Style 4 loss: 215540.336609 | |
| Style 5 loss: 23356.461525 | |
| Total loss: 5751658.645788 | |
| --- | |
| x1 value: -7.7416090965271 | |
| feval(x) grad value: 0.0084469504654408 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105065072 | |
| StyleLoss:updateOutput self.G 2: 6.7501707077026 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.788399365907e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010662251152098 | |
| dG 1: -3.0646289815195e-05 | |
| dG 2: -1.9689484417884e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921804160 | |
| StyleLoss:updateOutput self.G 2: 246.42359924316 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8293995651675e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00039488211041316 | |
| dG 1: -2.7399526516092e-05 | |
| dG 2: -3.5133081340011e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840893312 | |
| StyleLoss:updateOutput self.G 2: 215.64726257324 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.6489635836861e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00055569759570062 | |
| dG 1: -1.6506437532371e-05 | |
| dG 2: -4.2330804660151e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93410736 | |
| StyleLoss:updateOutput self.G 2: 47.511154174805 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3256452291444e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00082293647574261 | |
| dG 1: -1.2912413467348e-06 | |
| dG 2: -6.5675942332102e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6878336 | |
| StyleLoss:updateOutput self.G 2: 13.994011878967 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0208809726464e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0061252866871655 | |
| dG 1: -1.4680082358609e-06 | |
| dG 2: -2.9866701559828e-12 | |
| --- | |
| Iteration 52 / 2500 | |
| Content 1 loss: 5012293.750000 | |
| Style 1 loss: 176.796153 | |
| Style 2 loss: 64034.751892 | |
| Style 3 loss: 359878.829956 | |
| Style 4 loss: 214546.554565 | |
| Style 5 loss: 23546.151638 | |
| Total loss: 5674476.834205 | |
| --- | |
| x1 value: -7.7518954277039 | |
| feval(x) grad value: -0.0087329251691699 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105277568 | |
| StyleLoss:updateOutput self.G 2: 6.7638239860535 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0970864034762e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014098616084084 | |
| dG 1: -2.3979831894394e-05 | |
| dG 2: -1.5406449987293e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926217344 | |
| StyleLoss:updateOutput self.G 2: 246.98945617676 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.8193897700676e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00071077636675909 | |
| dG 1: 4.1675459215185e-05 | |
| dG 2: 5.3438412549178e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848015488 | |
| StyleLoss:updateOutput self.G 2: 217.4737701416 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8211935071122e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011112032225356 | |
| dG 1: 3.9233855204657e-05 | |
| dG 2: 1.0061534071182e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94701304 | |
| StyleLoss:updateOutput self.G 2: 48.167587280273 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.6174757294939e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015818082029 | |
| dG 1: 3.716907940543e-06 | |
| dG 2: 1.8905172566308e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7159161 | |
| StyleLoss:updateOutput self.G 2: 14.56535243988 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4838335573586e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.008903001435101 | |
| dG 1: 2.8909876164107e-06 | |
| dG 2: 5.8817295651992e-12 | |
| --- | |
| Iteration 53 / 2500 | |
| Content 1 loss: 5108985.156250 | |
| Style 1 loss: 156.010408 | |
| Style 2 loss: 62952.249527 | |
| Style 3 loss: 372318.122864 | |
| Style 4 loss: 215961.662292 | |
| Style 5 loss: 25213.348389 | |
| Total loss: 5785586.549730 | |
| --- | |
| x1 value: -7.761378288269 | |
| feval(x) grad value: 0.011928983032703 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 104999776 | |
| StyleLoss:updateOutput self.G 2: 6.7459754943848 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8872689006885e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010396561119705 | |
| dG 1: -3.2694755645934e-05 | |
| dG 2: -2.1005571414212e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920258176 | |
| StyleLoss:updateOutput self.G 2: 246.22534179688 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9759851350473e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061158905737102 | |
| dG 1: -5.1598010031739e-05 | |
| dG 2: -6.6161607442661e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840126720 | |
| StyleLoss:updateOutput self.G 2: 215.45068359375 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2504889923548e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00074883346678689 | |
| dG 1: -2.2505364540848e-05 | |
| dG 2: -5.771503934493e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93096072 | |
| StyleLoss:updateOutput self.G 2: 47.351119995117 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0997913452447e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013513864250854 | |
| dG 1: -2.5122681108769e-06 | |
| dG 2: -1.277805552341e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6839230.5 | |
| StyleLoss:updateOutput self.G 2: 13.914450645447 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.244089503416e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0074645378626883 | |
| dG 1: -2.0750053408847e-06 | |
| dG 2: -4.2216096070302e-12 | |
| --- | |
| Iteration 54 / 2500 | |
| Content 1 loss: 5000022.265625 | |
| Style 1 loss: 190.064214 | |
| Style 2 loss: 62951.305389 | |
| Style 3 loss: 367229.553223 | |
| Style 4 loss: 217000.190735 | |
| Style 5 loss: 24307.837486 | |
| Total loss: 5671701.216672 | |
| --- | |
| x1 value: -7.7713937759399 | |
| feval(x) grad value: -0.0083844857290387 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105290840 | |
| StyleLoss:updateOutput self.G 2: 6.7646760940552 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0949583279826e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011198696447536 | |
| dG 1: -2.3563561626361e-05 | |
| dG 2: -1.5139006584799e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925782528 | |
| StyleLoss:updateOutput self.G 2: 246.93371582031 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.1608676443493e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00041630183113739 | |
| dG 1: 3.4868320653914e-05 | |
| dG 2: 4.4709943212984e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846686016 | |
| StyleLoss:updateOutput self.G 2: 217.13290405273 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6389985546539e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00096167594892904 | |
| dG 1: 2.8830594601459e-05 | |
| dG 2: 7.3936126318563e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94404632 | |
| StyleLoss:updateOutput self.G 2: 48.016677856445 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1705471908717e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012974684359506 | |
| dG 1: 2.5656172510935e-06 | |
| dG 2: 1.3049402053725e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7061400.5 | |
| StyleLoss:updateOutput self.G 2: 14.366456985474 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4808785888745e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0056885280646384 | |
| dG 1: 1.3735404991166e-06 | |
| dG 2: 2.7944749379089e-12 | |
| --- | |
| Iteration 55 / 2500 | |
| Content 1 loss: 5077795.703125 | |
| Style 1 loss: 144.119382 | |
| Style 2 loss: 62204.320908 | |
| Style 3 loss: 352011.314392 | |
| Style 4 loss: 205825.950623 | |
| Style 5 loss: 22784.477234 | |
| Total loss: 5720765.885663 | |
| --- | |
| x1 value: -7.7792797088623 | |
| feval(x) grad value: 0.0067160059697926 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105145888 | |
| StyleLoss:updateOutput self.G 2: 6.7553634643555 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5920678815037e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00073927827179432 | |
| dG 1: -2.8110791390645e-05 | |
| dG 2: -1.8060492337768e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922534784 | |
| StyleLoss:updateOutput self.G 2: 246.51727294922 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.8845314155938e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -4.0400950638286e-06 | |
| dG 1: -1.5966295904946e-05 | |
| dG 2: -2.0472800971866e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842155584 | |
| StyleLoss:updateOutput self.G 2: 215.97099304199 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.7306969836191e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002742268552538 | |
| dG 1: -6.626720278291e-06 | |
| dG 2: -1.6994238048046e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93571904 | |
| StyleLoss:updateOutput self.G 2: 47.593139648438 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.159155922589e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058298208750784 | |
| dG 1: -6.6578581936483e-07 | |
| dG 2: -3.3863612868695e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6967156.5 | |
| StyleLoss:updateOutput self.G 2: 14.174715995789 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.7899338413754e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058739597443491 | |
| dG 1: -8.9313616058462e-08 | |
| dG 2: -1.8170904505458e-13 | |
| --- | |
| Iteration 56 / 2500 | |
| Content 1 loss: 5046495.312500 | |
| Style 1 loss: 159.890302 | |
| Style 2 loss: 56625.629425 | |
| Style 3 loss: 326224.548340 | |
| Style 4 loss: 202631.217957 | |
| Style 5 loss: 22197.711468 | |
| Total loss: 5654334.309991 | |
| --- | |
| x1 value: -7.7887034416199 | |
| feval(x) grad value: -0.0012329837772995 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105201184 | |
| StyleLoss:updateOutput self.G 2: 6.7589159011841 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.3512102365639e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00092896248679608 | |
| dG 1: -2.6376103051007e-05 | |
| dG 2: -1.6945995388179e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923859840 | |
| StyleLoss:updateOutput self.G 2: 246.68716430664 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6796214197257e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -5.0909042329295e-05 | |
| dG 1: 4.7753469516465e-06 | |
| dG 2: 6.1231949415388e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844820288 | |
| StyleLoss:updateOutput self.G 2: 216.65440368652 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.2871303536413e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00048760970821604 | |
| dG 1: 1.4227774045139e-05 | |
| dG 2: 3.6487163117271e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94080544 | |
| StyleLoss:updateOutput self.G 2: 47.851825714111 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.7926708519935e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00051485857693478 | |
| dG 1: 1.3079212521916e-06 | |
| dG 2: 6.6524325111061e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6958057 | |
| StyleLoss:updateOutput self.G 2: 14.156204223633 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.9758671498948e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011855200864375 | |
| dG 1: -2.3056279019329e-07 | |
| dG 2: -4.6908120833813e-13 | |
| --- | |
| Iteration 57 / 2500 | |
| Content 1 loss: 5053510.156250 | |
| Style 1 loss: 157.224275 | |
| Style 2 loss: 56116.773605 | |
| Style 3 loss: 329061.012268 | |
| Style 4 loss: 201892.730713 | |
| Style 5 loss: 22297.297955 | |
| Total loss: 5663035.195066 | |
| --- | |
| x1 value: -7.7958345413208 | |
| feval(x) grad value: -0.0026350917760283 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105213512 | |
| StyleLoss:updateOutput self.G 2: 6.759708404541 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4070045390754e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -2.6249006623402e-05 | |
| dG 1: -2.5989493224188e-05 | |
| dG 2: -1.6697607923069e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923571456 | |
| StyleLoss:updateOutput self.G 2: 246.65014648438 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3955461675152e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00023817953479011 | |
| dG 1: 2.5831673156063e-07 | |
| dG 2: 3.3122701630084e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844559424 | |
| StyleLoss:updateOutput self.G 2: 216.58743286133 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.7311895552157e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00053125724662095 | |
| dG 1: 1.2185207197035e-05 | |
| dG 2: 3.1248998614425e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94085488 | |
| StyleLoss:updateOutput self.G 2: 47.854351043701 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.9590778290803e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061794806970283 | |
| dG 1: 1.3271587704367e-06 | |
| dG 2: 6.7502785045664e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7052971 | |
| StyleLoss:updateOutput self.G 2: 14.349305152893 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.500837225256e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0051005026325583 | |
| dG 1: 1.242684220415e-06 | |
| dG 2: 2.5282473597316e-12 | |
| --- | |
| Iteration 58 / 2500 | |
| Content 1 loss: 5079101.953125 | |
| Style 1 loss: 146.682870 | |
| Style 2 loss: 55978.734970 | |
| Style 3 loss: 326506.713867 | |
| Style 4 loss: 200132.423401 | |
| Style 5 loss: 22405.893803 | |
| Total loss: 5684272.402035 | |
| --- | |
| x1 value: -7.8030381202698 | |
| feval(x) grad value: 0.011332589201629 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105082992 | |
| StyleLoss:updateOutput self.G 2: 6.7513213157654 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7353182708321e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00098652904853225 | |
| dG 1: -3.0084500394878e-05 | |
| dG 2: -1.9328547009451e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920705920 | |
| StyleLoss:updateOutput self.G 2: 246.28271484375 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7255884538799e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00047007808461785 | |
| dG 1: -4.4594082282856e-05 | |
| dG 2: -5.7180813903268e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840328576 | |
| StyleLoss:updateOutput self.G 2: 215.50250244141 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3075565163945e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00075428828131407 | |
| dG 1: -2.0924288037349e-05 | |
| dG 2: -5.366040077559e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93309520 | |
| StyleLoss:updateOutput self.G 2: 47.459674835205 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6942817637755e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010827303631231 | |
| dG 1: -1.6839916270328e-06 | |
| dG 2: -8.565225351892e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6884465 | |
| StyleLoss:updateOutput self.G 2: 14.006481170654 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.9427040822775e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0059656230732799 | |
| dG 1: -1.3728655403611e-06 | |
| dG 2: -2.7931021211181e-12 | |
| --- | |
| Iteration 59 / 2500 | |
| Content 1 loss: 5023868.750000 | |
| Style 1 loss: 167.713746 | |
| Style 2 loss: 55977.910995 | |
| Style 3 loss: 327750.274658 | |
| Style 4 loss: 200414.039612 | |
| Style 5 loss: 22409.919262 | |
| Total loss: 5630588.608274 | |
| --- | |
| x1 value: -7.8115172386169 | |
| feval(x) grad value: -0.012461097911 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105427680 | |
| StyleLoss:updateOutput self.G 2: 6.7734689712524 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4763427692042e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00190176256001 | |
| dG 1: -1.9270912162028e-05 | |
| dG 2: -1.2381084655774e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927846400 | |
| StyleLoss:updateOutput self.G 2: 247.19834899902 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.1162994547412e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00070171465631574 | |
| dG 1: 6.7173670686316e-05 | |
| dG 2: 8.6133530863286e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 849292416 | |
| StyleLoss:updateOutput self.G 2: 217.80123901367 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1068572664262e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011852927273139 | |
| dG 1: 4.9227837735089e-05 | |
| dG 2: 1.2624489127699e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94988184 | |
| StyleLoss:updateOutput self.G 2: 48.313495635986 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.3129771281892e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019594682380557 | |
| dG 1: 4.8300944399671e-06 | |
| dG 2: 2.4567123509123e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7113729.5 | |
| StyleLoss:updateOutput self.G 2: 14.47292137146 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3122687505529e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0078736124560237 | |
| dG 1: 2.185784296671e-06 | |
| dG 2: 4.4469900851996e-12 | |
| --- | |
| Iteration 60 / 2500 | |
| Content 1 loss: 5117212.109375 | |
| Style 1 loss: 126.851052 | |
| Style 2 loss: 57066.999435 | |
| Style 3 loss: 349976.783752 | |
| Style 4 loss: 203572.517395 | |
| Style 5 loss: 22941.295624 | |
| Total loss: 5750896.556634 | |
| --- | |
| x1 value: -7.816915512085 | |
| feval(x) grad value: 0.0076878927648067 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105199976 | |
| StyleLoss:updateOutput self.G 2: 6.7588386535645 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4775344321124e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011234908597544 | |
| dG 1: -2.6414076273795e-05 | |
| dG 2: -1.697038993706e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922452352 | |
| StyleLoss:updateOutput self.G 2: 246.5066986084 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1250488540782e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00029295365675353 | |
| dG 1: -1.7255588318221e-05 | |
| dG 2: -2.2126001118089e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841755008 | |
| StyleLoss:updateOutput self.G 2: 215.86827087402 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0584299294296e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00036418318632059 | |
| dG 1: -9.7619504231261e-06 | |
| dG 2: -2.5034551254938e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93391472 | |
| StyleLoss:updateOutput self.G 2: 47.501361846924 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5609947467965e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00097613409161568 | |
| dG 1: -1.3659770274899e-06 | |
| dG 2: -6.9477193095913e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6885974 | |
| StyleLoss:updateOutput self.G 2: 14.009551048279 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0089241868627e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0060535450465977 | |
| dG 1: -1.3494304766937e-06 | |
| dG 2: -2.7454234632213e-12 | |
| --- | |
| Iteration 61 / 2500 | |
| Content 1 loss: 5030903.515625 | |
| Style 1 loss: 142.055120 | |
| Style 2 loss: 52518.985748 | |
| Style 3 loss: 308104.522705 | |
| Style 4 loss: 194823.120117 | |
| Style 5 loss: 22071.268559 | |
| Total loss: 5608563.467874 | |
| --- | |
| x1 value: -7.8225679397583 | |
| feval(x) grad value: 1.9106752006337e-05 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105243080 | |
| StyleLoss:updateOutput self.G 2: 6.7616086006165 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2106641728878e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0008955862140283 | |
| dG 1: -2.5061721316888e-05 | |
| dG 2: -1.6101538505287e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923254016 | |
| StyleLoss:updateOutput self.G 2: 246.60948181152 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5391094782279e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00018916996486951 | |
| dG 1: -4.7097901187954e-06 | |
| dG 2: -6.0391351208028e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844166656 | |
| StyleLoss:updateOutput self.G 2: 216.48675537109 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3680100364772e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00034085527295247 | |
| dG 1: 9.1120919023524e-06 | |
| dG 2: 2.3367980727523e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94081960 | |
| StyleLoss:updateOutput self.G 2: 47.852558135986 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0316143317368e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061531231040135 | |
| dG 1: 1.313388906965e-06 | |
| dG 2: 6.6802417547293e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7060983.5 | |
| StyleLoss:updateOutput self.G 2: 14.365612030029 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.7559734513197e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0058535835705698 | |
| dG 1: 1.3670826319867e-06 | |
| dG 2: 2.7813374433444e-12 | |
| --- | |
| Iteration 62 / 2500 | |
| Content 1 loss: 5078428.515625 | |
| Style 1 loss: 149.714928 | |
| Style 2 loss: 55229.106903 | |
| Style 3 loss: 328821.853638 | |
| Style 4 loss: 195021.400452 | |
| Style 5 loss: 21911.145687 | |
| Total loss: 5679561.737233 | |
| --- | |
| x1 value: -7.8297114372253 | |
| feval(x) grad value: 0.00017797316832002 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105258832 | |
| StyleLoss:updateOutput self.G 2: 6.762619972229 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2633036656525e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00063981651328504 | |
| dG 1: -2.4567840227974e-05 | |
| dG 2: -1.5784231560678e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923401600 | |
| StyleLoss:updateOutput self.G 2: 246.62840270996 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8580645041247e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -7.698103581788e-05 | |
| dG 1: -2.3972893359314e-06 | |
| dG 2: -3.0739278310266e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843803712 | |
| StyleLoss:updateOutput self.G 2: 216.39364624023 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.226568440733e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012402844731696 | |
| dG 1: 6.2719054767513e-06 | |
| dG 2: 1.6084318122181e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93913944 | |
| StyleLoss:updateOutput self.G 2: 47.767101287842 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.021064382518e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.5900738768978e-05 | |
| dG 1: 6.6147100596936e-07 | |
| dG 2: 3.3644154085952e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6950754.5 | |
| StyleLoss:updateOutput self.G 2: 14.141345977783 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.2810561378938e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0019686336163431 | |
| dG 1: -3.4392192560517e-07 | |
| dG 2: -6.9971085132556e-13 | |
| --- | |
| Iteration 63 / 2500 | |
| Content 1 loss: 5068457.812500 | |
| Style 1 loss: 134.257257 | |
| Style 2 loss: 51699.886322 | |
| Style 3 loss: 318850.250244 | |
| Style 4 loss: 194890.686035 | |
| Style 5 loss: 21714.311600 | |
| Total loss: 5655747.203958 | |
| --- | |
| x1 value: -7.8343601226807 | |
| feval(x) grad value: -0.0016920273192227 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105326520 | |
| StyleLoss:updateOutput self.G 2: 6.7669687271118 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9489780334634e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011528208851814 | |
| dG 1: -2.2444342903327e-05 | |
| dG 2: -1.4419935514751e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924280704 | |
| StyleLoss:updateOutput self.G 2: 246.74110412598 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.8818634589811e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00011872933828272 | |
| dG 1: 1.1360834832885e-05 | |
| dG 2: 1.4567442304519e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844520064 | |
| StyleLoss:updateOutput self.G 2: 216.57737731934 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0343959644388e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00062636262737215 | |
| dG 1: 1.1878052646352e-05 | |
| dG 2: 3.0461295378453e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94006960 | |
| StyleLoss:updateOutput self.G 2: 47.81441116333 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.3073275309052e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00054280966287479 | |
| dG 1: 1.0224440529782e-06 | |
| dG 2: 5.2004196305147e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6969444.5 | |
| StyleLoss:updateOutput self.G 2: 14.179374694824 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9040433108539e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00023424255778082 | |
| dG 1: -5.3797467813865e-08 | |
| dG 2: -1.0945127318579e-13 | |
| --- | |
| Iteration 64 / 2500 | |
| Content 1 loss: 5060182.031250 | |
| Style 1 loss: 131.112523 | |
| Style 2 loss: 49438.035965 | |
| Style 3 loss: 298848.655701 | |
| Style 4 loss: 190521.915436 | |
| Style 5 loss: 21117.540836 | |
| Total loss: 5620239.291711 | |
| --- | |
| x1 value: -7.8393454551697 | |
| feval(x) grad value: 0.0060231694951653 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105196936 | |
| StyleLoss:updateOutput self.G 2: 6.7586431503296 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4670215970655e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00091570848599076 | |
| dG 1: -2.6509438612266e-05 | |
| dG 2: -1.7031659286029e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921797120 | |
| StyleLoss:updateOutput self.G 2: 246.42266845703 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.1166518255077e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 1.4664357877336e-05 | |
| dG 1: -2.7512745873537e-05 | |
| dG 2: -3.5278251674098e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842196288 | |
| StyleLoss:updateOutput self.G 2: 215.98147583008 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.3381783377281e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00032265554182231 | |
| dG 1: -6.3084103203437e-06 | |
| dG 2: -1.6177933558764e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93731544 | |
| StyleLoss:updateOutput self.G 2: 47.674320220947 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4415052968816e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00029204160091467 | |
| dG 1: -4.6386119834096e-08 | |
| dG 2: -2.3593160845131e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7031715.5 | |
| StyleLoss:updateOutput self.G 2: 14.306060791016 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.6183957869725e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0039710374549031 | |
| dG 1: 9.1274506530681e-07 | |
| dG 2: 1.8569848349997e-12 | |
| --- | |
| Iteration 65 / 2500 | |
| Content 1 loss: 5079912.890625 | |
| Style 1 loss: 145.148601 | |
| Style 2 loss: 50174.165726 | |
| Style 3 loss: 309852.493286 | |
| Style 4 loss: 191375.495911 | |
| Style 5 loss: 21335.525036 | |
| Total loss: 5652795.719185 | |
| --- | |
| x1 value: -7.8457183837891 | |
| feval(x) grad value: -0.0031830847728997 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105360112 | |
| StyleLoss:updateOutput self.G 2: 6.7691259384155 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8310222549853e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00072028412250802 | |
| dG 1: -2.1390585970948e-05 | |
| dG 2: -1.3742921731175e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924674176 | |
| StyleLoss:updateOutput self.G 2: 246.7915802002 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.6574562045644e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -3.0794864869677e-05 | |
| dG 1: 1.7520373148727e-05 | |
| dG 2: 2.2465521196807e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844490880 | |
| StyleLoss:updateOutput self.G 2: 216.56988525391 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.0152923348796e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00046464000479318 | |
| dG 1: 1.1650929081952e-05 | |
| dG 2: 2.9878838118946e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93999504 | |
| StyleLoss:updateOutput self.G 2: 47.81063079834 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.1627298698095e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00043587604886852 | |
| dG 1: 9.9347016657703e-07 | |
| dG 2: 5.0530510765229e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6932788.5 | |
| StyleLoss:updateOutput self.G 2: 14.104791641235 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.2429271590881e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0031457557342947 | |
| dG 1: -6.2278633095048e-07 | |
| dG 2: -1.2670619761734e-12 | |
| --- | |
| Iteration 66 / 2500 | |
| Content 1 loss: 5056417.578125 | |
| Style 1 loss: 122.132242 | |
| Style 2 loss: 50094.165802 | |
| Style 3 loss: 303318.031311 | |
| Style 4 loss: 189017.131805 | |
| Style 5 loss: 21068.053722 | |
| Total loss: 5620037.093008 | |
| --- | |
| x1 value: -7.8495860099792 | |
| feval(x) grad value: -0.0036168459337205 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105343040 | |
| StyleLoss:updateOutput self.G 2: 6.768030166626 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9139241181374e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00076925021130592 | |
| dG 1: -2.1926107365289e-05 | |
| dG 2: -1.4086980280187e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924181760 | |
| StyleLoss:updateOutput self.G 2: 246.72848510742 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5509073964258e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037026248173788 | |
| dG 1: 9.8138852990814e-06 | |
| dG 2: 1.2583868409105e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844853952 | |
| StyleLoss:updateOutput self.G 2: 216.66297912598 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1244169684232e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00073599559254944 | |
| dG 1: 1.4491112779069e-05 | |
| dG 2: 3.7162482292852e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94151232 | |
| StyleLoss:updateOutput self.G 2: 47.887802124023 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.426780897873e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00083709100726992 | |
| dG 1: 1.5822710111024e-06 | |
| dG 2: 8.0478478698895e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7044685.5 | |
| StyleLoss:updateOutput self.G 2: 14.332448959351 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.3326096955716e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0049995658919215 | |
| dG 1: 1.1140747346872e-06 | |
| dG 2: 2.2665912115943e-12 | |
| --- | |
| Iteration 67 / 2500 | |
| Content 1 loss: 5091585.546875 | |
| Style 1 loss: 125.168961 | |
| Style 2 loss: 46598.044395 | |
| Style 3 loss: 282034.538269 | |
| Style 4 loss: 183150.146484 | |
| Style 5 loss: 20928.787708 | |
| Total loss: 5624422.232693 | |
| --- | |
| x1 value: -7.85391664505 | |
| feval(x) grad value: 0.013801443390548 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105132768 | |
| StyleLoss:updateOutput self.G 2: 6.7545199394226 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6808807496745e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0019835717976093 | |
| dG 1: -2.8522852517199e-05 | |
| dG 2: -1.8325228487437e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919845888 | |
| StyleLoss:updateOutput self.G 2: 246.17250061035 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.8575729983422e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061718781944364 | |
| dG 1: -5.8055396948475e-05 | |
| dG 2: -7.4441607392228e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 838941056 | |
| StyleLoss:updateOutput self.G 2: 215.14659118652 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8569157589354e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010715750977397 | |
| dG 1: -3.1785908504389e-05 | |
| dG 2: -8.1515046448932e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93154088 | |
| StyleLoss:updateOutput self.G 2: 47.380619049072 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3837237961288e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0014857539208606 | |
| dG 1: -2.2871518012835e-06 | |
| dG 2: -1.1633056714103e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6900179 | |
| StyleLoss:updateOutput self.G 2: 14.038451194763 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.0495086624287e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0054297046735883 | |
| dG 1: -1.1289416761429e-06 | |
| dG 2: -2.2968385006428e-12 | |
| --- | |
| Iteration 68 / 2500 | |
| Content 1 loss: 5038444.921875 | |
| Style 1 loss: 147.460084 | |
| Style 2 loss: 49485.517502 | |
| Style 3 loss: 306472.229004 | |
| Style 4 loss: 187886.432648 | |
| Style 5 loss: 21036.829948 | |
| Total loss: 5603473.391061 | |
| --- | |
| x1 value: -7.8595180511475 | |
| feval(x) grad value: -0.013592090457678 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105553256 | |
| StyleLoss:updateOutput self.G 2: 6.7815361022949 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7772140143156e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00226243538782 | |
| dG 1: -1.5331568647525e-05 | |
| dG 2: -9.8501545461871e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928338304 | |
| StyleLoss:updateOutput self.G 2: 247.26139831543 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.9195886232574e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00070802232949063 | |
| dG 1: 7.4870600656141e-05 | |
| dG 2: 9.600292782963e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 849218816 | |
| StyleLoss:updateOutput self.G 2: 217.7823638916 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1841695740932e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001224763225764 | |
| dG 1: 4.8652244004188e-05 | |
| dG 2: 1.2476879772405e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 95036640 | |
| StyleLoss:updateOutput self.G 2: 48.338138580322 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.5820272614728e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021589703392237 | |
| dG 1: 5.018107003707e-06 | |
| dG 2: 2.5523415846107e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7135751.5 | |
| StyleLoss:updateOutput self.G 2: 14.517726898193 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4902105931469e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.008941262960434 | |
| dG 1: 2.5276187898271e-06 | |
| dG 2: 5.1424533034639e-12 | |
| --- | |
| Iteration 69 / 2500 | |
| Content 1 loss: 5126208.203125 | |
| Style 1 loss: 106.013160 | |
| Style 2 loss: 51800.909042 | |
| Style 3 loss: 314940.032959 | |
| Style 4 loss: 189830.646515 | |
| Style 5 loss: 22058.491230 | |
| Total loss: 5704944.296031 | |
| --- | |
| x1 value: -7.8627142906189 | |
| feval(x) grad value: 0.004345481749624 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105335952 | |
| StyleLoss:updateOutput self.G 2: 6.7675747871399 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9960855957588e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002684680512175 | |
| dG 1: -2.2148400603328e-05 | |
| dG 2: -1.4229800063964e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923445760 | |
| StyleLoss:updateOutput self.G 2: 246.63407897949 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.9042978263242e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00014218199066818 | |
| dG 1: -1.7076822587114e-06 | |
| dG 2: -2.189675307393e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843056640 | |
| StyleLoss:updateOutput self.G 2: 216.20205688477 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.230787294304e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00013345158367883 | |
| dG 1: 4.2360119323348e-07 | |
| dG 2: 1.0863265988799e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93544264 | |
| StyleLoss:updateOutput self.G 2: 47.579074859619 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.7867115300687e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00065387942595407 | |
| dG 1: -7.730931201877e-07 | |
| dG 2: -3.9321548946612e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6874577.5 | |
| StyleLoss:updateOutput self.G 2: 13.986365318298 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1295015838186e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0067770094610751 | |
| dG 1: -1.5263420891642e-06 | |
| dG 2: -3.1053514794321e-12 | |
| --- | |
| Iteration 70 / 2500 | |
| Content 1 loss: 5047907.421875 | |
| Style 1 loss: 119.023215 | |
| Style 2 loss: 45671.939850 | |
| Style 3 loss: 276616.012573 | |
| Style 4 loss: 180727.294922 | |
| Style 5 loss: 21155.179024 | |
| Total loss: 5572196.871459 | |
| --- | |
| x1 value: -7.8658490180969 | |
| feval(x) grad value: 0.0063083809800446 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105219504 | |
| StyleLoss:updateOutput self.G 2: 6.7600932121277 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.3915996172927e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00089193094754592 | |
| dG 1: -2.5801591618801e-05 | |
| dG 2: -1.6576885263769e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921260928 | |
| StyleLoss:updateOutput self.G 2: 246.35391235352 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.1731404110124e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.3020561356097e-05 | |
| dG 1: -3.590378764784e-05 | |
| dG 2: -4.6037670203836e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841679488 | |
| StyleLoss:updateOutput self.G 2: 215.84892272949 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.4037800718306e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0004508369602263 | |
| dG 1: -1.0352483513998e-05 | |
| dG 2: -2.6548971354678e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93746176 | |
| StyleLoss:updateOutput self.G 2: 47.68176651001 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.636970902221e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00025726036983542 | |
| dG 1: 1.0386337123691e-08 | |
| dG 2: 5.282768309172e-15 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7020534 | |
| StyleLoss:updateOutput self.G 2: 14.283310890198 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.5004034038575e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0033002414274961 | |
| dG 1: 7.3918971565945e-07 | |
| dG 2: 1.5038853409116e-12 | |
| --- | |
| Iteration 71 / 2500 | |
| Content 1 loss: 5085505.078125 | |
| Style 1 loss: 139.227279 | |
| Style 2 loss: 49792.945862 | |
| Style 3 loss: 305770.065308 | |
| Style 4 loss: 183928.493500 | |
| Style 5 loss: 20528.218746 | |
| Total loss: 5645664.028819 | |
| --- | |
| x1 value: -7.8703198432922 | |
| feval(x) grad value: -0.0080876844003797 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105446040 | |
| StyleLoss:updateOutput self.G 2: 6.7746477127075 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4343376142942e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014093116624281 | |
| dG 1: -1.8694780010264e-05 | |
| dG 2: -1.2010936949886e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925383680 | |
| StyleLoss:updateOutput self.G 2: 246.8825378418 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.2067225198016e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00027081702137366 | |
| dG 1: 2.8625396225834e-05 | |
| dG 2: 3.6704944639254e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845670464 | |
| StyleLoss:updateOutput self.G 2: 216.87242126465 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.52921373342e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00083860062295571 | |
| dG 1: 2.0881521777483e-05 | |
| dG 2: 5.355072722063e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94358496 | |
| StyleLoss:updateOutput self.G 2: 47.993209838867 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1204104427852e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012926212511957 | |
| dG 1: 2.3864974991739e-06 | |
| dG 2: 1.21383545576e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7032031 | |
| StyleLoss:updateOutput self.G 2: 14.306704521179 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.207656835817e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0043245940469205 | |
| dG 1: 9.1765514298459e-07 | |
| dG 2: 1.8669744569766e-12 | |
| --- | |
| Iteration 72 / 2500 | |
| Content 1 loss: 5091525.000000 | |
| Style 1 loss: 110.222083 | |
| Style 2 loss: 45508.558273 | |
| Style 3 loss: 278015.785217 | |
| Style 4 loss: 179381.160736 | |
| Style 5 loss: 20293.886662 | |
| Total loss: 5614834.612971 | |
| --- | |
| x1 value: -7.873565196991 | |
| feval(x) grad value: 0.0081779733300209 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105296920 | |
| StyleLoss:updateOutput self.G 2: 6.7650661468506 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2079257411842e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011860592057928 | |
| dG 1: -2.3373018848361e-05 | |
| dG 2: -1.50165871491e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922212608 | |
| StyleLoss:updateOutput self.G 2: 246.47598266602 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0375431120101e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00038940404192545 | |
| dG 1: -2.1005915186834e-05 | |
| dG 2: -2.6934858423505e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842025792 | |
| StyleLoss:updateOutput self.G 2: 215.93772888184 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.0033748638944e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00029072296456434 | |
| dG 1: -7.6429796536104e-06 | |
| dG 2: -1.9600441061862e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93383432 | |
| StyleLoss:updateOutput self.G 2: 47.49728012085 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6541481784316e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010757432319224 | |
| dG 1: -1.3971382486488e-06 | |
| dG 2: -7.1062134041761e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6858230.5 | |
| StyleLoss:updateOutput self.G 2: 13.953106880188 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2341643014224e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0074049858376384 | |
| dG 1: -1.7800747400543e-06 | |
| dG 2: -3.6215713587751e-12 | |
| --- | |
| Iteration 73 / 2500 | |
| Content 1 loss: 5048021.484375 | |
| Style 1 loss: 118.693460 | |
| Style 2 loss: 44702.570915 | |
| Style 3 loss: 279234.786987 | |
| Style 4 loss: 179840.457916 | |
| Style 5 loss: 21392.951488 | |
| Total loss: 5573310.945142 | |
| --- | |
| x1 value: -7.8768339157104 | |
| feval(x) grad value: -0.0070538502186537 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105459056 | |
| StyleLoss:updateOutput self.G 2: 6.775484085083 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.3521033060424e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016690689371899 | |
| dG 1: -1.8286509657628e-05 | |
| dG 2: -1.1748630497285e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925752576 | |
| StyleLoss:updateOutput self.G 2: 246.92987060547 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.5016154359701e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065229798201472 | |
| dG 1: 3.4402008168399e-05 | |
| dG 2: 4.4112023062093e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846825024 | |
| StyleLoss:updateOutput self.G 2: 217.1685333252 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.42272583048e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00085510569624603 | |
| dG 1: 2.9917915526312e-05 | |
| dG 2: 7.6724572875553e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94623136 | |
| StyleLoss:updateOutput self.G 2: 48.127819061279 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.7294589699522e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016044777585194 | |
| dG 1: 3.4134754969273e-06 | |
| dG 2: 1.7361832457818e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7121003.5 | |
| StyleLoss:updateOutput self.G 2: 14.487718582153 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.42440626405e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0085464380681515 | |
| dG 1: 2.2987021566223e-06 | |
| dG 2: 4.6767221172084e-12 | |
| --- | |
| Iteration 74 / 2500 | |
| Content 1 loss: 5127277.734375 | |
| Style 1 loss: 115.464818 | |
| Style 2 loss: 49475.503922 | |
| Style 3 loss: 318550.323486 | |
| Style 4 loss: 182698.448181 | |
| Style 5 loss: 21506.381035 | |
| Total loss: 5699623.855817 | |
| --- | |
| x1 value: -7.8797435760498 | |
| feval(x) grad value: 0.0098699927330017 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105287096 | |
| StyleLoss:updateOutput self.G 2: 6.7644357681274 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.231905492702e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013758650748059 | |
| dG 1: -2.3681182938162e-05 | |
| dG 2: -1.5214576560424e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921762304 | |
| StyleLoss:updateOutput self.G 2: 246.4182434082 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6290554561447e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00041553500341251 | |
| dG 1: -2.8055235816282e-05 | |
| dG 2: -3.5973862777539e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840458880 | |
| StyleLoss:updateOutput self.G 2: 215.53587341309 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5036934541968e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00091252522543073 | |
| dG 1: -1.9906148736482e-05 | |
| dG 2: -5.1049364388489e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93397392 | |
| StyleLoss:updateOutput self.G 2: 47.504379272461 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6579615191858e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010282805887982 | |
| dG 1: -1.3429536238618e-06 | |
| dG 2: -6.8306151750422e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6924068 | |
| StyleLoss:updateOutput self.G 2: 14.087055206299 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.6365777229294e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0039819460362196 | |
| dG 1: -7.5814108413397e-07 | |
| dG 2: -1.54244195541e-12 | |
| --- | |
| Iteration 75 / 2500 | |
| Content 1 loss: 5053519.140625 | |
| Style 1 loss: 121.784091 | |
| Style 2 loss: 42711.236000 | |
| Style 3 loss: 266981.277466 | |
| Style 4 loss: 176185.832977 | |
| Style 5 loss: 20198.869228 | |
| Total loss: 5559718.140388 | |
| --- | |
| x1 value: -7.8837766647339 | |
| feval(x) grad value: -0.0081818653270602 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105506960 | |
| StyleLoss:updateOutput self.G 2: 6.7785615921021 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.1849324955147e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00085306708933786 | |
| dG 1: -1.6783600585768e-05 | |
| dG 2: -1.0783050884491e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926194560 | |
| StyleLoss:updateOutput self.G 2: 246.98654174805 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.8162488159041e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0003680984955281 | |
| dG 1: 4.1318886360386e-05 | |
| dG 2: 5.2981195819424e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847464640 | |
| StyleLoss:updateOutput self.G 2: 217.33255004883 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6669061153607e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010103686945513 | |
| dG 1: 3.4922664781334e-05 | |
| dG 2: 8.9559262783645e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94369976 | |
| StyleLoss:updateOutput self.G 2: 47.999050140381 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.869994576964e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010964502580464 | |
| dG 1: 2.4311043489433e-06 | |
| dG 2: 1.2365235788417e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7012004 | |
| StyleLoss:updateOutput self.G 2: 14.265957832336 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.1776209513955e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0025065725203604 | |
| dG 1: 6.067919571251e-07 | |
| dG 2: 1.2345213826898e-12 | |
| --- | |
| Iteration 76 / 2500 | |
| Content 1 loss: 5106897.265625 | |
| Style 1 loss: 96.710868 | |
| Style 2 loss: 50306.121826 | |
| Style 3 loss: 311515.045166 | |
| Style 4 loss: 179727.813721 | |
| Style 5 loss: 20291.893959 | |
| Total loss: 5668834.851165 | |
| --- | |
| x1 value: -7.8858985900879 | |
| feval(x) grad value: 0.00089942628983408 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105387264 | |
| StyleLoss:updateOutput self.G 2: 6.7708711624146 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7665063956638e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010124392574653 | |
| dG 1: -2.0538880562526e-05 | |
| dG 2: -1.3195723810519e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923566592 | |
| StyleLoss:updateOutput self.G 2: 246.64956665039 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.3690219199184e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -3.4757391404128e-05 | |
| dG 1: 1.8410162283544e-07 | |
| dG 2: 2.360679468745e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844306688 | |
| StyleLoss:updateOutput self.G 2: 216.52264404297 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.5542623651236e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0005283253849484 | |
| dG 1: 1.0206935257884e-05 | |
| dG 2: 2.6175708735948e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93850152 | |
| StyleLoss:updateOutput self.G 2: 47.7346534729 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8987721617236e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 8.743076614337e-05 | |
| dG 1: 4.1387210103494e-07 | |
| dG 2: 2.105062543549e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6968516.5 | |
| StyleLoss:updateOutput self.G 2: 14.17748260498 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.4405129169008e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00044643075671047 | |
| dG 1: -6.8212820281133e-08 | |
| dG 2: -1.3877938240866e-13 | |
| --- | |
| Iteration 77 / 2500 | |
| Content 1 loss: 5074100.000000 | |
| Style 1 loss: 114.137024 | |
| Style 2 loss: 41516.601562 | |
| Style 3 loss: 263639.076233 | |
| Style 4 loss: 173368.240356 | |
| Style 5 loss: 19758.968353 | |
| Total loss: 5572497.023529 | |
| --- | |
| x1 value: -7.888726234436 | |
| feval(x) grad value: 0.0070796674117446 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105253016 | |
| StyleLoss:updateOutput self.G 2: 6.7622451782227 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4136847066056e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0017835309263319 | |
| dG 1: -2.4750323063927e-05 | |
| dG 2: -1.5901470678398e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920951552 | |
| StyleLoss:updateOutput self.G 2: 246.31423950195 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.526840851644e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00018477688718121 | |
| dG 1: -4.0748211176833e-05 | |
| dG 2: -5.2249441752361e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840584320 | |
| StyleLoss:updateOutput self.G 2: 215.56805419922 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3208193649916e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00075215398101136 | |
| dG 1: -1.8924345567939e-05 | |
| dG 2: -4.8531521683326e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93643952 | |
| StyleLoss:updateOutput self.G 2: 47.62975692749 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.8299165434382e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0004401246260386 | |
| dG 1: -3.8633771737295e-07 | |
| dG 2: -1.9650152002521e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7028540 | |
| StyleLoss:updateOutput self.G 2: 14.299606323242 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.4929969312288e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0038957977667451 | |
| dG 1: 8.6348842387451e-07 | |
| dG 2: 1.7567718113565e-12 | |
| --- | |
| Iteration 78 / 2500 | |
| Content 1 loss: 5091548.437500 | |
| Style 1 loss: 123.135421 | |
| Style 2 loss: 42631.359100 | |
| Style 3 loss: 284329.238892 | |
| Style 4 loss: 178475.109100 | |
| Style 5 loss: 20150.366306 | |
| Total loss: 5617257.646320 | |
| --- | |
| x1 value: -7.8919811248779 | |
| feval(x) grad value: -0.0045144269242883 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105483816 | |
| StyleLoss:updateOutput self.G 2: 6.7770738601685 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.3090806539349e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013273943914101 | |
| dG 1: -1.7509868484922e-05 | |
| dG 2: -1.1249658973464e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925216640 | |
| StyleLoss:updateOutput self.G 2: 246.86111450195 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.1112479582116e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020103693532292 | |
| dG 1: 2.6010642613983e-05 | |
| dG 2: 3.3352179525103e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845102720 | |
| StyleLoss:updateOutput self.G 2: 216.72679138184 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.39473641525e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00080264743883163 | |
| dG 1: 1.6437888916698e-05 | |
| dG 2: 4.2155003446287e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94131712 | |
| StyleLoss:updateOutput self.G 2: 47.877864837646 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.545267025449e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00084832985885441 | |
| dG 1: 1.5064930494191e-06 | |
| dG 2: 7.6624199606828e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6938756.5 | |
| StyleLoss:updateOutput self.G 2: 14.11693572998 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6361972749764e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0027817180380225 | |
| dG 1: -5.3016043466414e-07 | |
| dG 2: -1.0786141944885e-12 | |
| --- | |
| Iteration 79 / 2500 | |
| Content 1 loss: 5080469.140625 | |
| Style 1 loss: 100.084081 | |
| Style 2 loss: 41762.875557 | |
| Style 3 loss: 260425.277710 | |
| Style 4 loss: 171691.978455 | |
| Style 5 loss: 19697.075844 | |
| Total loss: 5574146.432272 | |
| --- | |
| x1 value: -7.8943338394165 | |
| feval(x) grad value: -0.00014948796888348 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105435184 | |
| StyleLoss:updateOutput self.G 2: 6.7739505767822 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5918702678828e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044876258471049 | |
| dG 1: -1.9035172954318e-05 | |
| dG 2: -1.2229630285895e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924080384 | |
| StyleLoss:updateOutput self.G 2: 246.71546936035 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3469459264902e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012065404007444 | |
| dG 1: 8.2284022937529e-06 | |
| dG 2: 1.0550877662871e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844507008 | |
| StyleLoss:updateOutput self.G 2: 216.57403564453 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.4734047334223e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00054151512449607 | |
| dG 1: 1.1776698556787e-05 | |
| dG 2: 3.020137308643e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93902328 | |
| StyleLoss:updateOutput self.G 2: 47.761207580566 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7284349951296e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 8.6862441094127e-05 | |
| dG 1: 6.164410706333e-07 | |
| dG 2: 3.1353817654158e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6985371.5 | |
| StyleLoss:updateOutput self.G 2: 14.211771965027 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0296250962938e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061777501832694 | |
| dG 1: 1.9339701395893e-07 | |
| dG 2: 3.9346721410552e-13 | |
| --- | |
| Iteration 80 / 2500 | |
| Content 1 loss: 5092165.234375 | |
| Style 1 loss: 104.050454 | |
| Style 2 loss: 40348.340034 | |
| Style 3 loss: 257422.737122 | |
| Style 4 loss: 169188.137054 | |
| Style 5 loss: 19517.196178 | |
| Total loss: 5578745.695218 | |
| --- | |
| x1 value: -7.8969097137451 | |
| feval(x) grad value: 0.0036390288732946 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105344352 | |
| StyleLoss:updateOutput self.G 2: 6.7681150436401 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0226769471019e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00075858336640522 | |
| dG 1: -2.1884377929382e-05 | |
| dG 2: -1.4060173381472e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922202240 | |
| StyleLoss:updateOutput self.G 2: 246.474609375 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7109916089785e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.8044108047616e-05 | |
| dG 1: -2.1172350898269e-05 | |
| dG 2: -2.7148277115946e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841497920 | |
| StyleLoss:updateOutput self.G 2: 215.80235290527 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.2589445443991e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00056234427029267 | |
| dG 1: -1.177467584057e-05 | |
| dG 2: -3.0196186263237e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93838832 | |
| StyleLoss:updateOutput self.G 2: 47.728897094727 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1447998993219e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.4169205112848e-05 | |
| dG 1: 3.7001723285357e-07 | |
| dG 2: 1.8820054293201e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7051969 | |
| StyleLoss:updateOutput self.G 2: 14.347270011902 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.3564722192241e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0056138839572668 | |
| dG 1: 1.2271290188437e-06 | |
| dG 2: 2.4966003656784e-12 | |
| --- | |
| Iteration 81 / 2500 | |
| Content 1 loss: 5093504.296875 | |
| Style 1 loss: 115.028679 | |
| Style 2 loss: 40889.539719 | |
| Style 3 loss: 262098.243713 | |
| Style 4 loss: 169984.165192 | |
| Style 5 loss: 19747.880459 | |
| Total loss: 5586339.154637 | |
| --- | |
| x1 value: -7.8998322486877 | |
| feval(x) grad value: -0.00074242858681828 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105442648 | |
| StyleLoss:updateOutput self.G 2: 6.7744293212891 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5881029702978e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00043706595897675 | |
| dG 1: -1.88012891158e-05 | |
| dG 2: -1.2079364201598e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924082048 | |
| StyleLoss:updateOutput self.G 2: 246.71565246582 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.453035108374e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.5781347580487e-05 | |
| dG 1: 8.2511833170429e-06 | |
| dG 2: 1.0580090406206e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843997760 | |
| StyleLoss:updateOutput self.G 2: 216.44345092773 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0889542175555e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00050533458124846 | |
| dG 1: 7.7910235631862e-06 | |
| dG 2: 1.9980093968197e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93899376 | |
| StyleLoss:updateOutput self.G 2: 47.759696960449 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.5258447534688e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00029516327776946 | |
| dG 1: 6.0494249964904e-07 | |
| dG 2: 3.0768969186759e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6921097 | |
| StyleLoss:updateOutput self.G 2: 14.081007003784 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.1042916260922e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0042625754140317 | |
| dG 1: -8.042640615713e-07 | |
| dG 2: -1.6362793281779e-12 | |
| --- | |
| Iteration 82 / 2500 | |
| Content 1 loss: 5079558.984375 | |
| Style 1 loss: 101.705175 | |
| Style 2 loss: 39064.713478 | |
| Style 3 loss: 247865.982056 | |
| Style 4 loss: 166740.840912 | |
| Style 5 loss: 19582.483292 | |
| Total loss: 5552914.709287 | |
| --- | |
| x1 value: -7.9018311500549 | |
| feval(x) grad value: -0.0022672237828374 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105455920 | |
| StyleLoss:updateOutput self.G 2: 6.7752828598022 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4999857234652e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0009056823910214 | |
| dG 1: -1.8384636859992e-05 | |
| dG 2: -1.1811673601009e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924143744 | |
| StyleLoss:updateOutput self.G 2: 246.72357177734 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.419039546453e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00021600531181321 | |
| dG 1: 9.2183117885725e-06 | |
| dG 2: 1.1820192177478e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845046400 | |
| StyleLoss:updateOutput self.G 2: 216.71234130859 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.184805853427e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00072939513484016 | |
| dG 1: 1.5996538422769e-05 | |
| dG 2: 4.1023161430342e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94115616 | |
| StyleLoss:updateOutput self.G 2: 47.869682312012 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1774896790939e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00068464805372059 | |
| dG 1: 1.4440715858655e-06 | |
| dG 2: 7.344927617503e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7010960 | |
| StyleLoss:updateOutput self.G 2: 14.263836860657 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.7463248620261e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0028477949090302 | |
| dG 1: 5.9059550494567e-07 | |
| dG 2: 1.2015698765827e-12 | |
| --- | |
| Iteration 83 / 2500 | |
| Content 1 loss: 5107448.437500 | |
| Style 1 loss: 102.018803 | |
| Style 2 loss: 39069.677353 | |
| Style 3 loss: 251605.613708 | |
| Style 4 loss: 166178.398132 | |
| Style 5 loss: 19322.772503 | |
| Total loss: 5583726.918000 | |
| --- | |
| x1 value: -7.9047503471375 | |
| feval(x) grad value: 0.0073311305604875 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105343024 | |
| StyleLoss:updateOutput self.G 2: 6.7680282592773 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0417262425763e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011209601070732 | |
| dG 1: -2.1926911358605e-05 | |
| dG 2: -1.4087497444623e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921577216 | |
| StyleLoss:updateOutput self.G 2: 246.39451599121 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.1842554310279e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00042080954881385 | |
| dG 1: -3.0951599910622e-05 | |
| dG 2: -3.968772359364e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840458816 | |
| StyleLoss:updateOutput self.G 2: 215.53584289551 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3968723067137e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00086020567687228 | |
| dG 1: -1.9906514353352e-05 | |
| dG 2: -5.1050301139166e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93596200 | |
| StyleLoss:updateOutput self.G 2: 47.605495452881 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.1417085457124e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00051900953985751 | |
| dG 1: -5.7154068144882e-07 | |
| dG 2: -2.9070054198034e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6979221.5 | |
| StyleLoss:updateOutput self.G 2: 14.199262619019 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.2260774313927e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00037356480606832 | |
| dG 1: 9.7953389399663e-08 | |
| dG 2: 1.9928653725073e-13 | |
| --- | |
| Iteration 84 / 2500 | |
| Content 1 loss: 5072405.078125 | |
| Style 1 loss: 112.677533 | |
| Style 2 loss: 39549.682617 | |
| Style 3 loss: 256723.114014 | |
| Style 4 loss: 168086.872101 | |
| Style 5 loss: 19275.704384 | |
| Total loss: 5556153.128774 | |
| --- | |
| x1 value: -7.9081978797913 | |
| feval(x) grad value: -0.0094970278441906 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105563864 | |
| StyleLoss:updateOutput self.G 2: 6.7822179794312 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.8871589563551e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013033372815698 | |
| dG 1: -1.4998320693849e-05 | |
| dG 2: -9.6360495537712e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926360192 | |
| StyleLoss:updateOutput self.G 2: 247.00773620605 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.2924912331018e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00073709525167942 | |
| dG 1: 4.39086870756e-05 | |
| dG 2: 5.6301968295869e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847205504 | |
| StyleLoss:updateOutput self.G 2: 217.26609802246 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8360820774888e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011482575209811 | |
| dG 1: 3.289488813607e-05 | |
| dG 2: 8.4359021504388e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94460824 | |
| StyleLoss:updateOutput self.G 2: 48.045253753662 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.4699406253603e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014272624393925 | |
| dG 1: 2.7836747449328e-06 | |
| dG 2: 1.4158502929101e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7049772.5 | |
| StyleLoss:updateOutput self.G 2: 14.342798233032 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8300453171541e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0052980277687311 | |
| dG 1: 1.193038542624e-06 | |
| dG 2: 2.4272433021832e-12 | |
| --- | |
| Iteration 85 / 2500 | |
| Content 1 loss: 5127948.828125 | |
| Style 1 loss: 92.085242 | |
| Style 2 loss: 39545.207977 | |
| Style 3 loss: 264199.584961 | |
| Style 4 loss: 169002.182007 | |
| Style 5 loss: 19655.383587 | |
| Total loss: 5620443.271899 | |
| --- | |
| x1 value: -7.9094743728638 | |
| feval(x) grad value: 0.011951295658946 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105348112 | |
| StyleLoss:updateOutput self.G 2: 6.768355846405 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0561058511912e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00087172369239852 | |
| dG 1: -2.1767111320514e-05 | |
| dG 2: -1.3984832172506e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921603328 | |
| StyleLoss:updateOutput self.G 2: 246.39785766602 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2364952435037e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00059881544439122 | |
| dG 1: -3.0543858883902e-05 | |
| dG 2: -3.9164899622024e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840859584 | |
| StyleLoss:updateOutput self.G 2: 215.63859558105 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0223030244561e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058648985577747 | |
| dG 1: -1.6770547517808e-05 | |
| dG 2: -4.300811443092e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93060592 | |
| StyleLoss:updateOutput self.G 2: 47.333068847656 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3751773881031e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0015168987447396 | |
| dG 1: -2.6500088097237e-06 | |
| dG 2: -1.3478638778014e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6797610.5 | |
| StyleLoss:updateOutput self.G 2: 13.829774856567 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5492789771088e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0092956749722362 | |
| dG 1: -2.7210196549277e-06 | |
| dG 2: -5.5359284864553e-12 | |
| --- | |
| Iteration 86 / 2500 | |
| Content 1 loss: 5036835.156250 | |
| Style 1 loss: 109.057132 | |
| Style 2 loss: 38915.408134 | |
| Style 3 loss: 258582.527161 | |
| Style 4 loss: 173591.732025 | |
| Style 5 loss: 21766.812801 | |
| Total loss: 5529800.693504 | |
| --- | |
| x1 value: -7.9125323295593 | |
| feval(x) grad value: -0.011241160333157 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105578408 | |
| StyleLoss:updateOutput self.G 2: 6.7831516265869 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7833287674639e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016286100726575 | |
| dG 1: -1.4542299140885e-05 | |
| dG 2: -9.3430677691048e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926392960 | |
| StyleLoss:updateOutput self.G 2: 247.0119934082 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.4115312997947e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00067757599754259 | |
| dG 1: 4.4425378291635e-05 | |
| dG 2: 5.6964502559431e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846809728 | |
| StyleLoss:updateOutput self.G 2: 217.16461181641 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9110022719815e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010791599052027 | |
| dG 1: 2.979864984809e-05 | |
| dG 2: 7.6418706432269e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94587632 | |
| StyleLoss:updateOutput self.G 2: 48.109760284424 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.8377178296068e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017343864310533 | |
| dG 1: 3.2757436656539e-06 | |
| dG 2: 1.6661297992313e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7113523.5 | |
| StyleLoss:updateOutput self.G 2: 14.472499847412 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4159796819513e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084958774968982 | |
| dG 1: 2.1825840121892e-06 | |
| dG 2: 4.4404783669516e-12 | |
| --- | |
| Iteration 87 / 2500 | |
| Content 1 loss: 5134088.281250 | |
| Style 1 loss: 90.428330 | |
| Style 2 loss: 38747.036934 | |
| Style 3 loss: 252265.823364 | |
| Style 4 loss: 165104.381561 | |
| Style 5 loss: 20149.286270 | |
| Total loss: 5610445.237710 | |
| --- | |
| x1 value: -7.9148802757263 | |
| feval(x) grad value: 0.0093652522191405 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105309328 | |
| StyleLoss:updateOutput self.G 2: 6.7658638954163 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2569222813427e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0019692059140652 | |
| dG 1: -2.2983804228716e-05 | |
| dG 2: -1.476652784424e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920733696 | |
| StyleLoss:updateOutput self.G 2: 246.28630065918 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.4457019277597e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00052931607933715 | |
| dG 1: -4.4158929085825e-05 | |
| dG 2: -5.6622835760411e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840492032 | |
| StyleLoss:updateOutput self.G 2: 215.5443572998 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5477277770515e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00087043736129999 | |
| dG 1: -1.964693183254e-05 | |
| dG 2: -5.038460100526e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93499536 | |
| StyleLoss:updateOutput self.G 2: 47.556327819824 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4557397776116e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00090496696066111 | |
| dG 1: -9.4665591632292e-07 | |
| dG 2: -4.8149423901092e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6951357.5 | |
| StyleLoss:updateOutput self.G 2: 14.142572402954 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5727123304241e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0021436272654682 | |
| dG 1: -3.3456390724496e-07 | |
| dG 2: -6.8067204433628e-13 | |
| --- | |
| Iteration 88 / 2500 | |
| Content 1 loss: 5075191.796875 | |
| Style 1 loss: 111.094303 | |
| Style 2 loss: 37752.897263 | |
| Style 3 loss: 248654.136658 | |
| Style 4 loss: 163626.777649 | |
| Style 5 loss: 19008.550644 | |
| Total loss: 5544345.253392 | |
| --- | |
| x1 value: -7.9173340797424 | |
| feval(x) grad value: -0.0058673764578998 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105561712 | |
| StyleLoss:updateOutput self.G 2: 6.7820796966553 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.8715568589632e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021533647086471 | |
| dG 1: -1.5065999832586e-05 | |
| dG 2: -9.6795336503031e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925561472 | |
| StyleLoss:updateOutput self.G 2: 246.90534973145 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.8905509803253e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044579152017832 | |
| dG 1: 3.1407238566317e-05 | |
| dG 2: 4.0271965449923e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846508544 | |
| StyleLoss:updateOutput self.G 2: 217.0873260498 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.758075569569e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010882797650993 | |
| dG 1: 2.7440390113043e-05 | |
| dG 2: 7.0370957325205e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94240704 | |
| StyleLoss:updateOutput self.G 2: 47.933300018311 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9333417355938e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010971288429573 | |
| dG 1: 1.9294129742775e-06 | |
| dG 2: 9.8135009233419e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6998592 | |
| StyleLoss:updateOutput self.G 2: 14.238669395447 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.5351396832084e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021210839040577 | |
| dG 1: 3.9860614720055e-07 | |
| dG 2: 8.1096631146527e-13 | |
| --- | |
| Iteration 89 / 2500 | |
| Content 1 loss: 5104053.906250 | |
| Style 1 loss: 93.298204 | |
| Style 2 loss: 38083.768845 | |
| Style 3 loss: 255124.900818 | |
| Style 4 loss: 164750.564575 | |
| Style 5 loss: 19015.647411 | |
| Total loss: 5581122.086103 | |
| --- | |
| x1 value: -7.9196128845215 | |
| feval(x) grad value: 0.004969235509634 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105439104 | |
| StyleLoss:updateOutput self.G 2: 6.7742018699646 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.72334447718e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001160059357062 | |
| dG 1: -1.8912440282293e-05 | |
| dG 2: -1.2150775177688e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923148672 | |
| StyleLoss:updateOutput self.G 2: 246.59596252441 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3144585420122e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.490034138551e-05 | |
| dG 1: -6.3607380980102e-06 | |
| dG 2: -8.1560658834329e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842616960 | |
| StyleLoss:updateOutput self.G 2: 216.08935546875 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8379305067338e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00022480062034447 | |
| dG 1: -3.0157084438542e-06 | |
| dG 2: -7.733789737091e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93653168 | |
| StyleLoss:updateOutput self.G 2: 47.634471893311 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.4308872771999e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00056413974380121 | |
| dG 1: -3.5046630841862e-07 | |
| dG 2: -1.7825637613125e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6952865.5 | |
| StyleLoss:updateOutput self.G 2: 14.145644187927 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.397698549179e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020386190153658 | |
| dG 1: -3.1113887644096e-07 | |
| dG 2: -6.330135525999e-13 | |
| --- | |
| Iteration 90 / 2500 | |
| Content 1 loss: 5095650.390625 | |
| Style 1 loss: 95.008805 | |
| Style 2 loss: 36341.497421 | |
| Style 3 loss: 243815.025330 | |
| Style 4 loss: 164694.465637 | |
| Style 5 loss: 19066.839695 | |
| Total loss: 5559663.227513 | |
| --- | |
| x1 value: -7.921959400177 | |
| feval(x) grad value: -0.0038314040284604 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105468632 | |
| StyleLoss:updateOutput self.G 2: 6.7760992050171 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.439702922492e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012181896017864 | |
| dG 1: -1.7986145394389e-05 | |
| dG 2: -1.1555655521009e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923717504 | |
| StyleLoss:updateOutput self.G 2: 246.66889953613 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.091511414586e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 5.084788062959e-05 | |
| dG 1: 2.5453969101363e-06 | |
| dG 2: 3.2638404782667e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843963264 | |
| StyleLoss:updateOutput self.G 2: 216.43461608887 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.6321895531019e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00029994375654496 | |
| dG 1: 7.5211860348645e-06 | |
| dG 2: 1.9288099763204e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94199968 | |
| StyleLoss:updateOutput self.G 2: 47.912578582764 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6872546382274e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010149219306186 | |
| dG 1: 1.7713736042424e-06 | |
| dG 2: 9.0096707221557e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7032529 | |
| StyleLoss:updateOutput self.G 2: 14.307717323303 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.8680295700906e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0047208177857101 | |
| dG 1: 9.2538198259717e-07 | |
| dG 2: 1.8826945211159e-12 | |
| --- | |
| Iteration 91 / 2500 | |
| Content 1 loss: 5097401.953125 | |
| Style 1 loss: 100.759059 | |
| Style 2 loss: 37713.970184 | |
| Style 3 loss: 255370.056152 | |
| Style 4 loss: 166169.139862 | |
| Style 5 loss: 19160.390854 | |
| Total loss: 5575916.269237 | |
| --- | |
| x1 value: -7.9236764907837 | |
| feval(x) grad value: 0.0058892616070807 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105389728 | |
| StyleLoss:updateOutput self.G 2: 6.7710289955139 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9785565064676e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013879287289456 | |
| dG 1: -2.0461699023144e-05 | |
| dG 2: -1.3146133487352e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922017920 | |
| StyleLoss:updateOutput self.G 2: 246.45101928711 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4067351606382e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0001418293104507 | |
| dG 1: -2.4055520043476e-05 | |
| dG 2: -3.0845213536135e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842614848 | |
| StyleLoss:updateOutput self.G 2: 216.08879089355 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5133884185589e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00010133572504856 | |
| dG 1: -3.0325948046084e-06 | |
| dG 2: -7.7770971086688e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93556288 | |
| StyleLoss:updateOutput self.G 2: 47.585189819336 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1260971888305e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00072235445259139 | |
| dG 1: -7.2645474347155e-07 | |
| dG 2: -3.6949395908377e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6950563.5 | |
| StyleLoss:updateOutput self.G 2: 14.140956878662 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8889393749741e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0023333639837801 | |
| dG 1: -3.4690185657382e-07 | |
| dG 2: -7.0577387250442e-13 | |
| --- | |
| Iteration 92 / 2500 | |
| Content 1 loss: 5089766.406250 | |
| Style 1 loss: 98.776784 | |
| Style 2 loss: 35241.228104 | |
| Style 3 loss: 234380.447388 | |
| Style 4 loss: 160289.062500 | |
| Style 5 loss: 18890.596390 | |
| Total loss: 5538666.517415 | |
| --- | |
| x1 value: -7.9261794090271 | |
| feval(x) grad value: -0.0069306450895965 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105602424 | |
| StyleLoss:updateOutput self.G 2: 6.7846956253052 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6183974100036e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019929108675569 | |
| dG 1: -1.3788934666081e-05 | |
| dG 2: -8.8590495616464e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926283392 | |
| StyleLoss:updateOutput self.G 2: 246.99798583984 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.090245190966e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0004092866438441 | |
| dG 1: 4.2712672438938e-05 | |
| dG 2: 5.4768372996505e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845864320 | |
| StyleLoss:updateOutput self.G 2: 216.9221496582 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5985942525276e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00086921756155789 | |
| dG 1: 2.2399153749575e-05 | |
| dG 2: 5.7442683422393e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94324784 | |
| StyleLoss:updateOutput self.G 2: 47.976062774658 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0995869931539e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012324455892667 | |
| dG 1: 2.255732397316e-06 | |
| dG 2: 1.1473248566485e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7016860.5 | |
| StyleLoss:updateOutput self.G 2: 14.275838851929 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.7659656249598e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0034595795441419 | |
| dG 1: 6.8218054138924e-07 | |
| dG 2: 1.3878997777439e-12 | |
| --- | |
| Iteration 93 / 2500 | |
| Content 1 loss: 5115751.562500 | |
| Style 1 loss: 87.859746 | |
| Style 2 loss: 37863.879204 | |
| Style 3 loss: 242372.566223 | |
| Style 4 loss: 159019.569397 | |
| Style 5 loss: 18527.774334 | |
| Total loss: 5573623.211404 | |
| --- | |
| x1 value: -7.9277672767639 | |
| feval(x) grad value: 0.0084257712587714 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105422904 | |
| StyleLoss:updateOutput self.G 2: 6.7731604576111 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8044028372042e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013955610338598 | |
| dG 1: -1.9420960597927e-05 | |
| dG 2: -1.2477486491941e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922424448 | |
| StyleLoss:updateOutput self.G 2: 246.50311279297 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5344338183686e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037255036295392 | |
| dG 1: -1.7693968402455e-05 | |
| dG 2: -2.2688116576436e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841194880 | |
| StyleLoss:updateOutput self.G 2: 215.72463989258 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2861084996985e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00076048541814089 | |
| dG 1: -1.4145874956739e-05 | |
| dG 2: -3.6277137977631e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93586944 | |
| StyleLoss:updateOutput self.G 2: 47.600769042969 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1003519517772e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00067693163873628 | |
| dG 1: -6.0751239061574e-07 | |
| dG 2: -3.0899680600674e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6938035 | |
| StyleLoss:updateOutput self.G 2: 14.115468978882 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.489018803928e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0032934113405645 | |
| dG 1: -5.4135153959578e-07 | |
| dG 2: -1.1013825485309e-12 | |
| --- | |
| Iteration 94 / 2500 | |
| Content 1 loss: 5079715.234375 | |
| Style 1 loss: 96.851360 | |
| Style 2 loss: 34595.137596 | |
| Style 3 loss: 231129.364014 | |
| Style 4 loss: 158569.942474 | |
| Style 5 loss: 18545.867443 | |
| Total loss: 5522652.397262 | |
| --- | |
| x1 value: -7.9300541877747 | |
| feval(x) grad value: -0.01036958489567 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105569696 | |
| StyleLoss:updateOutput self.G 2: 6.7825922966003 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9567303272415e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015879076672718 | |
| dG 1: -1.4815781469224e-05 | |
| dG 2: -9.5187735731778e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925423616 | |
| StyleLoss:updateOutput self.G 2: 246.88768005371 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.9517731187952e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00073058216366917 | |
| dG 1: 2.9249562430778e-05 | |
| dG 2: 3.7505285335748e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847693760 | |
| StyleLoss:updateOutput self.G 2: 217.39129638672 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9666683215291e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012387213064358 | |
| dG 1: 3.6716599424835e-05 | |
| dG 2: 9.4159818830875e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94553024 | |
| StyleLoss:updateOutput self.G 2: 48.09215927124 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.771459151063e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016657528467476 | |
| dG 1: 3.1414388104167e-06 | |
| dG 2: 1.5978183403112e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7109388 | |
| StyleLoss:updateOutput self.G 2: 14.464087486267 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4001617500981e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084009701386094 | |
| dG 1: 2.1183941498748e-06 | |
| dG 2: 4.3098844805523e-12 | |
| --- | |
| Iteration 95 / 2500 | |
| Content 1 loss: 5142427.734375 | |
| Style 1 loss: 86.370159 | |
| Style 2 loss: 34968.864441 | |
| Style 3 loss: 254334.571838 | |
| Style 4 loss: 161893.970490 | |
| Style 5 loss: 19594.017506 | |
| Total loss: 5613305.528808 | |
| --- | |
| x1 value: -7.9314322471619 | |
| feval(x) grad value: 0.014021534472704 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105304440 | |
| StyleLoss:updateOutput self.G 2: 6.7655506134033 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.3291915829968e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001437816186808 | |
| dG 1: -2.3136937670643e-05 | |
| dG 2: -1.486491268618e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919677696 | |
| StyleLoss:updateOutput self.G 2: 246.15092468262 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.4797100896594e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00082408805610612 | |
| dG 1: -6.0685590142384e-05 | |
| dG 2: -7.7814178711666e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 839894912 | |
| StyleLoss:updateOutput self.G 2: 215.39126586914 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5269080222424e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00088103284360841 | |
| dG 1: -2.4319098884007e-05 | |
| dG 2: -6.2366396769153e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 92999248 | |
| StyleLoss:updateOutput self.G 2: 47.301864624023 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.63848846771e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016701796557754 | |
| dG 1: -2.8880604077131e-06 | |
| dG 2: -1.468943781717e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6827114 | |
| StyleLoss:updateOutput self.G 2: 13.889801979065 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4571810424968e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.008743085898459 | |
| dG 1: -2.2630626972386e-06 | |
| dG 2: -4.6042132779978e-12 | |
| --- | |
| Iteration 96 / 2500 | |
| Content 1 loss: 5046266.406250 | |
| Style 1 loss: 107.546758 | |
| Style 2 loss: 36119.479179 | |
| Style 3 loss: 244329.391479 | |
| Style 4 loss: 165498.607635 | |
| Style 5 loss: 20285.734177 | |
| Total loss: 5512607.165479 | |
| --- | |
| x1 value: -7.9345464706421 | |
| feval(x) grad value: -0.010835265740752 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105635248 | |
| StyleLoss:updateOutput self.G 2: 6.7868037223816 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.4546917387624e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015349210007116 | |
| dG 1: -1.2759070159518e-05 | |
| dG 2: -8.1973869966306e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926622976 | |
| StyleLoss:updateOutput self.G 2: 247.04145812988 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0576356253914e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065908645046875 | |
| dG 1: 4.8024583520601e-05 | |
| dG 2: 6.1579586915772e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846300736 | |
| StyleLoss:updateOutput self.G 2: 217.03407287598 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8378925403795e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010062429355457 | |
| dG 1: 2.5815052140388e-05 | |
| dG 2: 6.6202768093937e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94485032 | |
| StyleLoss:updateOutput self.G 2: 48.057582855225 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5858975050141e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015282499371096 | |
| dG 1: 2.8776253202523e-06 | |
| dG 2: 1.4636357447209e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7084038 | |
| StyleLoss:updateOutput self.G 2: 14.412515640259 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2451010888981e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0074706077575684 | |
| dG 1: 1.7249308257306e-06 | |
| dG 2: 3.5093809368919e-12 | |
| --- | |
| Iteration 97 / 2500 | |
| Content 1 loss: 5138885.546875 | |
| Style 1 loss: 81.012977 | |
| Style 2 loss: 35798.489571 | |
| Style 3 loss: 233042.289734 | |
| Style 4 loss: 157776.248932 | |
| Style 5 loss: 18996.547222 | |
| Total loss: 5584580.135311 | |
| --- | |
| x1 value: -7.9358286857605 | |
| feval(x) grad value: 0.006923905108124 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105450096 | |
| StyleLoss:updateOutput self.G 2: 6.7749080657959 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6826421023761e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00099194701761007 | |
| dG 1: -1.8567898223409e-05 | |
| dG 2: -1.1929416872739e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922664960 | |
| StyleLoss:updateOutput self.G 2: 246.53395080566 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0938209388532e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00025860202731565 | |
| dG 1: -1.392983358528e-05 | |
| dG 2: -1.7861544491724e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842195840 | |
| StyleLoss:updateOutput self.G 2: 215.98133850098 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.533812862448e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0003736317739822 | |
| dG 1: -6.3121387938736e-06 | |
| dG 2: -1.6187491885117e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93621656 | |
| StyleLoss:updateOutput self.G 2: 47.6184425354 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.6967546053529e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00056335458066314 | |
| dG 1: -4.7273002223847e-07 | |
| dG 2: -2.4044291948092e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6943320.5 | |
| StyleLoss:updateOutput self.G 2: 14.126221656799 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6060557679084e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0027636336162686 | |
| dG 1: -4.593064772962e-07 | |
| dG 2: -9.3446138414027e-13 | |
| --- | |
| Iteration 98 / 2500 | |
| Content 1 loss: 5084461.718750 | |
| Style 1 loss: 94.093636 | |
| Style 2 loss: 33218.459129 | |
| Style 3 loss: 222029.045105 | |
| Style 4 loss: 154985.195160 | |
| Style 5 loss: 18327.404022 | |
| Total loss: 5513115.915802 | |
| --- | |
| x1 value: -7.937979221344 | |
| feval(x) grad value: -0.0050512813031673 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105546920 | |
| StyleLoss:updateOutput self.G 2: 6.7811288833618 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.1127367350336e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015457507688552 | |
| dG 1: -1.5530094970018e-05 | |
| dG 2: -9.9777033423648e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924542208 | |
| StyleLoss:updateOutput self.G 2: 246.77465820312 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.8636564170247e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00032545736758038 | |
| dG 1: 1.5455245375051e-05 | |
| dG 2: 1.9817507010411e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845787712 | |
| StyleLoss:updateOutput self.G 2: 216.90242004395 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7259911544443e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010591431055218 | |
| dG 1: 2.1799160094815e-05 | |
| dG 2: 5.5904014056862e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94154120 | |
| StyleLoss:updateOutput self.G 2: 47.889278411865 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6145466474882e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00095372961368412 | |
| dG 1: 1.5935015653668e-06 | |
| dG 2: 8.1049690613469e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7009919.5 | |
| StyleLoss:updateOutput self.G 2: 14.261716842651 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.856905206907e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0029141432605684 | |
| dG 1: 5.7443867262919e-07 | |
| dG 2: 1.1686986014364e-12 | |
| --- | |
| Iteration 99 / 2500 | |
| Content 1 loss: 5114918.359375 | |
| Style 1 loss: 86.169977 | |
| Style 2 loss: 33160.792351 | |
| Style 3 loss: 226235.275269 | |
| Style 4 loss: 153688.545227 | |
| Style 5 loss: 18232.503891 | |
| Total loss: 5546321.646089 | |
| --- | |
| x1 value: -7.939887046814 | |
| feval(x) grad value: 0.0093156946823001 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105407320 | |
| StyleLoss:updateOutput self.G 2: 6.7721605300903 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9520703154494e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0017304429784417 | |
| dG 1: -1.9909190086764e-05 | |
| dG 2: -1.2791164611878e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921645824 | |
| StyleLoss:updateOutput self.G 2: 246.4033203125 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8401194163716e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00042191933607683 | |
| dG 1: -2.9877212000429e-05 | |
| dG 2: -3.8310092945193e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840940608 | |
| StyleLoss:updateOutput self.G 2: 215.65943908691 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5007670128853e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00091211160179228 | |
| dG 1: -1.6134961697389e-05 | |
| dG 2: -4.1378146568849e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93561408 | |
| StyleLoss:updateOutput self.G 2: 47.587802886963 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2141806848831e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00077056809095666 | |
| dG 1: -7.0653345574101e-07 | |
| dG 2: -3.5936157478586e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6957761.5 | |
| StyleLoss:updateOutput self.G 2: 14.155602455139 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7860889417752e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016716533573344 | |
| dG 1: -2.3515524105733e-07 | |
| dG 2: -4.7842453739996e-13 | |
| --- | |
| Iteration 100 / 2500 | |
| Content 1 loss: 5093976.171875 | |
| Style 1 loss: 94.074391 | |
| Style 2 loss: 33059.832573 | |
| Style 3 loss: 222276.374817 | |
| Style 4 loss: 155347.709656 | |
| Style 5 loss: 18270.528316 | |
| Total loss: 5523024.691628 | |
| --- | |
| x1 value: -7.942747592926 | |
| feval(x) grad value: -0.011226928792894 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105652312 | |
| StyleLoss:updateOutput self.G 2: 6.7878999710083 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3203966748042e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0023160134442151 | |
| dG 1: -1.2224128113303e-05 | |
| dG 2: -7.8537008710647e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926665600 | |
| StyleLoss:updateOutput self.G 2: 247.04693603516 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.9839253430691e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0006667633424513 | |
| dG 1: 4.869136682828e-05 | |
| dG 2: 6.2434562728142e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847298752 | |
| StyleLoss:updateOutput self.G 2: 217.28996276855 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9570448728246e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011423130054027 | |
| dG 1: 3.3624921343289e-05 | |
| dG 2: 8.6231204468601e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94530688 | |
| StyleLoss:updateOutput self.G 2: 48.080787658691 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.8974417887184e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017181094735861 | |
| dG 1: 3.0546868856618e-06 | |
| dG 2: 1.5536941308167e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7059127 | |
| StyleLoss:updateOutput self.G 2: 14.361832618713 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0898139635174e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0065388828516006 | |
| dG 1: 1.3382447150434e-06 | |
| dG 2: 2.7226656259399e-12 | |
| --- | |
| Iteration 101 / 2500 | |
| Content 1 loss: 5126064.843750 | |
| Style 1 loss: 82.154727 | |
| Style 2 loss: 35363.697052 | |
| Style 3 loss: 244412.796021 | |
| Style 4 loss: 158282.363892 | |
| Style 5 loss: 18565.531254 | |
| Total loss: 5582771.386695 | |
| --- | |
| x1 value: -7.9443106651306 | |
| feval(x) grad value: 0.011788188479841 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105396064 | |
| StyleLoss:updateOutput self.G 2: 6.7714376449585 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0327513772809e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0019329756032676 | |
| dG 1: -2.0262510588509e-05 | |
| dG 2: -1.3018162936529e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921319808 | |
| StyleLoss:updateOutput self.G 2: 246.36143493652 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.6032767759007e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00053687446052209 | |
| dG 1: -3.4985270758625e-05 | |
| dG 2: -4.4859905720673e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841211840 | |
| StyleLoss:updateOutput self.G 2: 215.72901916504 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3081847782814e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00067174242576584 | |
| dG 1: -1.401237295795e-05 | |
| dG 2: -3.5934764278794e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93205328 | |
| StyleLoss:updateOutput self.G 2: 47.406688690186 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.4704513634788e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0015595688018948 | |
| dG 1: -2.0883167053398e-06 | |
| dG 2: -1.0621729190641e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6860628.5 | |
| StyleLoss:updateOutput self.G 2: 13.957985877991 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2949788015248e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0077698705717921 | |
| dG 1: -1.7428541241316e-06 | |
| dG 2: -3.5458465590804e-12 | |
| --- | |
| Iteration 102 / 2500 | |
| Content 1 loss: 5069721.484375 | |
| Style 1 loss: 94.090965 | |
| Style 2 loss: 32748.207092 | |
| Style 3 loss: 223974.243164 | |
| Style 4 loss: 156444.030762 | |
| Style 5 loss: 19350.824833 | |
| Total loss: 5502332.881191 | |
| --- | |
| x1 value: -7.9468107223511 | |
| feval(x) grad value: -0.010870632715523 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105629088 | |
| StyleLoss:updateOutput self.G 2: 6.7864074707031 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5434863104579e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0020056904759258 | |
| dG 1: -1.2952297765878e-05 | |
| dG 2: -8.3215324821889e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926096512 | |
| StyleLoss:updateOutput self.G 2: 246.97396850586 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.3928369204168e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065401493338868 | |
| dG 1: 3.9783870306564e-05 | |
| dG 2: 5.1012917848259e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846519360 | |
| StyleLoss:updateOutput self.G 2: 217.09013366699 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9719635702131e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011041942052543 | |
| dG 1: 2.7525004043127e-05 | |
| dG 2: 7.0587932200772e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94615040 | |
| StyleLoss:updateOutput self.G 2: 48.12370300293 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0158386721268e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018160124309361 | |
| dG 1: 3.3821024771896e-06 | |
| dG 2: 1.7202261750476e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7099018 | |
| StyleLoss:updateOutput self.G 2: 14.442989349365 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3605812227979e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0081634875386953 | |
| dG 1: 1.9574263205868e-06 | |
| dG 2: 3.98239470914e-12 | |
| --- | |
| Iteration 103 / 2500 | |
| Content 1 loss: 5146658.984375 | |
| Style 1 loss: 79.943826 | |
| Style 2 loss: 33498.384476 | |
| Style 3 loss: 226256.950378 | |
| Style 4 loss: 153956.176758 | |
| Style 5 loss: 18927.145958 | |
| Total loss: 5579377.585771 | |
| --- | |
| x1 value: -7.9481821060181 | |
| feval(x) grad value: 0.010967185720801 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105387056 | |
| StyleLoss:updateOutput self.G 2: 6.7708592414856 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.033205414089e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013787731295452 | |
| dG 1: -2.0544855942717e-05 | |
| dG 2: -1.3199562970412e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920998144 | |
| StyleLoss:updateOutput self.G 2: 246.32022094727 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.6275632093493e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00070118258008733 | |
| dG 1: -4.0015762351686e-05 | |
| dG 2: -5.1310262462467e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840731904 | |
| StyleLoss:updateOutput self.G 2: 215.60589599609 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.457654832393e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00087656424148008 | |
| dG 1: -1.7769132682588e-05 | |
| dG 2: -4.5568986951905e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93490152 | |
| StyleLoss:updateOutput self.G 2: 47.551551818848 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4120924163308e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00088970124488696 | |
| dG 1: -9.830539511313e-07 | |
| dG 2: -5.0000709952633e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6917895 | |
| StyleLoss:updateOutput self.G 2: 14.074493408203 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.9469788261122e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0047681867145002 | |
| dG 1: -8.5396902704815e-07 | |
| dG 2: -1.7374043826887e-12 | |
| --- | |
| Iteration 104 / 2500 | |
| Content 1 loss: 5075818.750000 | |
| Style 1 loss: 97.336046 | |
| Style 2 loss: 32648.612022 | |
| Style 3 loss: 220408.882141 | |
| Style 4 loss: 153567.169189 | |
| Style 5 loss: 18314.075947 | |
| Total loss: 5500854.825346 | |
| --- | |
| x1 value: -7.9503178596497 | |
| feval(x) grad value: -0.0087640471756458 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105639128 | |
| StyleLoss:updateOutput self.G 2: 6.787052154541 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5600074948784e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0009688840364106 | |
| dG 1: -1.2637785403058e-05 | |
| dG 2: -8.119464844393e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926064640 | |
| StyleLoss:updateOutput self.G 2: 246.96987915039 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.2070413636993e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075346871744841 | |
| dG 1: 3.9286522223847e-05 | |
| dG 2: 5.0375194467212e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847348032 | |
| StyleLoss:updateOutput self.G 2: 217.30256652832 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7982061706334e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011757586617023 | |
| dG 1: 3.4009291994153e-05 | |
| dG 2: 8.7216926389355e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94343896 | |
| StyleLoss:updateOutput self.G 2: 47.985794067383 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.929923314492e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011374548776075 | |
| dG 1: 2.3299498934648e-06 | |
| dG 2: 1.1850741742092e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7058100.5 | |
| StyleLoss:updateOutput self.G 2: 14.359745025635 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.7641725460562e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0058585032820702 | |
| dG 1: 1.3223258292783e-06 | |
| dG 2: 2.6902785554839e-12 | |
| --- | |
| Iteration 105 / 2500 | |
| Content 1 loss: 5140620.703125 | |
| Style 1 loss: 75.788811 | |
| Style 2 loss: 33235.816956 | |
| Style 3 loss: 242845.733643 | |
| Style 4 loss: 157332.195282 | |
| Style 5 loss: 18785.553932 | |
| Total loss: 5592895.791748 | |
| --- | |
| x1 value: -7.951723575592 | |
| feval(x) grad value: 0.0067459582351148 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105497144 | |
| StyleLoss:updateOutput self.G 2: 6.7779321670532 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4595316833474e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00013037814642303 | |
| dG 1: -1.7091431800509e-05 | |
| dG 2: -1.0980822371179e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922843008 | |
| StyleLoss:updateOutput self.G 2: 246.55682373047 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2888482281426e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00033880636328831 | |
| dG 1: -1.1137345609313e-05 | |
| dG 2: -1.4280873575703e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841938880 | |
| StyleLoss:updateOutput self.G 2: 215.91540527344 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7165091388024e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0003059527662117 | |
| dG 1: -8.32328623801e-06 | |
| dG 2: -2.1345087156122e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93532176 | |
| StyleLoss:updateOutput self.G 2: 47.572925567627 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.9165504252596e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00063791696447879 | |
| dG 1: -8.1994892298098e-07 | |
| dG 2: -4.1704766268018e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6893806 | |
| StyleLoss:updateOutput self.G 2: 14.025485038757 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.9653323104576e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0059791994281113 | |
| dG 1: -1.227865823239e-06 | |
| dG 2: -2.4980996004426e-12 | |
| --- | |
| Iteration 106 / 2500 | |
| Content 1 loss: 5070392.968750 | |
| Style 1 loss: 87.293442 | |
| Style 2 loss: 31389.987946 | |
| Style 3 loss: 221578.605652 | |
| Style 4 loss: 156452.407837 | |
| Style 5 loss: 18662.601471 | |
| Total loss: 5498563.865098 | |
| --- | |
| x1 value: -7.9544615745544 | |
| feval(x) grad value: -0.0045899199321866 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105515136 | |
| StyleLoss:updateOutput self.G 2: 6.7790875434875 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4163168294299e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.0787434601225e-05 | |
| dG 1: -1.6526981198695e-05 | |
| dG 2: -1.0618176260122e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923700992 | |
| StyleLoss:updateOutput self.G 2: 246.66679382324 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3041036694972e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002541238500271 | |
| dG 1: 2.286276412633e-06 | |
| dG 2: 2.9315796751944e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844036032 | |
| StyleLoss:updateOutput self.G 2: 216.45324707031 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3530225585519e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00025284235016443 | |
| dG 1: 8.0899226304609e-06 | |
| dG 2: 2.0746620567336e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94250720 | |
| StyleLoss:updateOutput self.G 2: 47.938385009766 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7515660033496e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010337217245251 | |
| dG 1: 1.9682481706695e-06 | |
| dG 2: 1.0011027380338e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7087330.5 | |
| StyleLoss:updateOutput self.G 2: 14.419211387634 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2745346111842e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0076472070068121 | |
| dG 1: 1.776013732524e-06 | |
| dG 2: 3.6133095213803e-12 | |
| --- | |
| Iteration 107 / 2500 | |
| Content 1 loss: 5134982.421875 | |
| Style 1 loss: 83.475547 | |
| Style 2 loss: 30623.788834 | |
| Style 3 loss: 217515.586853 | |
| Style 4 loss: 152089.450836 | |
| Style 5 loss: 18640.172482 | |
| Total loss: 5553934.896426 | |
| --- | |
| x1 value: -7.9554963111877 | |
| feval(x) grad value: 0.007377668749541 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105458496 | |
| StyleLoss:updateOutput self.G 2: 6.775447845459 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7157292354095e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00050219293916598 | |
| dG 1: -1.8303860997548e-05 | |
| dG 2: -1.1759778264023e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922053376 | |
| StyleLoss:updateOutput self.G 2: 246.45555114746 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.1731354372132e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037997390609235 | |
| dG 1: -2.3500206225435e-05 | |
| dG 2: -3.0133168096164e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842706240 | |
| StyleLoss:updateOutput self.G 2: 216.11225891113 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1120751370063e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -8.9834829850588e-06 | |
| dG 1: -2.3164534468378e-06 | |
| dG 2: -5.9405518698652e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93532768 | |
| StyleLoss:updateOutput self.G 2: 47.573226928711 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0760243895902e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00072162749711424 | |
| dG 1: -8.1768359905254e-07 | |
| dG 2: -4.1589542682137e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6928243.5 | |
| StyleLoss:updateOutput self.G 2: 14.095544815063 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.5787503444881e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0039472500793636 | |
| dG 1: -6.9334004138e-07 | |
| dG 2: -1.4106042722783e-12 | |
| --- | |
| Iteration 108 / 2500 | |
| Content 1 loss: 5083659.765625 | |
| Style 1 loss: 89.603428 | |
| Style 2 loss: 30461.425781 | |
| Style 3 loss: 210422.447205 | |
| Style 4 loss: 150444.087982 | |
| Style 5 loss: 18112.895966 | |
| Total loss: 5493190.225986 | |
| --- | |
| x1 value: -7.9583444595337 | |
| feval(x) grad value: -0.0092305950820446 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105666504 | |
| StyleLoss:updateOutput self.G 2: 6.7888126373291 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3098790435938e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015565189532936 | |
| dG 1: -1.1778429325204e-05 | |
| dG 2: -7.5673500668852e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926547584 | |
| StyleLoss:updateOutput self.G 2: 247.03179931641 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.3038933951229e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0006280125817284 | |
| dG 1: 4.6841676521581e-05 | |
| dG 2: 6.0062783739656e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846062016 | |
| StyleLoss:updateOutput self.G 2: 216.97282409668 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8338964480336e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010537998750806 | |
| dG 1: 2.3946293367771e-05 | |
| dG 2: 6.1410329946221e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94314064 | |
| StyleLoss:updateOutput self.G 2: 47.970615386963 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.134186161129e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012763411505148 | |
| dG 1: 2.2141780391394e-06 | |
| dG 2: 1.1261892026576e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7036984 | |
| StyleLoss:updateOutput self.G 2: 14.316781044006 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.2536195122884e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0049521727487445 | |
| dG 1: 9.9455007784854e-07 | |
| dG 2: 2.0234170242106e-12 | |
| --- | |
| Iteration 109 / 2500 | |
| Content 1 loss: 5129538.281250 | |
| Style 1 loss: 73.853591 | |
| Style 2 loss: 32288.151741 | |
| Style 3 loss: 214045.166016 | |
| Style 4 loss: 148810.203552 | |
| Style 5 loss: 17913.489819 | |
| Total loss: 5542669.145969 | |
| --- | |
| x1 value: -7.9593152999878 | |
| feval(x) grad value: 0.01134586520493 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105430496 | |
| StyleLoss:updateOutput self.G 2: 6.7736492156982 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9011951002976e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016539067728445 | |
| dG 1: -1.9182363757864e-05 | |
| dG 2: -1.2324193315177e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921536640 | |
| StyleLoss:updateOutput self.G 2: 246.38931274414 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.5043052782366e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058963143965229 | |
| dG 1: -3.1585808756063e-05 | |
| dG 2: -4.0500948948752e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840271552 | |
| StyleLoss:updateOutput self.G 2: 215.4878692627 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7148424547031e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001024279394187 | |
| dG 1: -2.137094270438e-05 | |
| dG 2: -5.4805834349969e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93433648 | |
| StyleLoss:updateOutput self.G 2: 47.522796630859 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6780252565241e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010368836810812 | |
| dG 1: -1.2023950830553e-06 | |
| dG 2: -6.1156993098194e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6910675.5 | |
| StyleLoss:updateOutput self.G 2: 14.059810638428 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.8332893710685e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0052999733015895 | |
| dG 1: -9.660064961281e-07 | |
| dG 2: -1.9653454211288e-12 | |
| --- | |
| Iteration 110 / 2500 | |
| Content 1 loss: 5078261.328125 | |
| Style 1 loss: 88.618107 | |
| Style 2 loss: 30534.433365 | |
| Style 3 loss: 212573.455811 | |
| Style 4 loss: 150673.679352 | |
| Style 5 loss: 18136.515141 | |
| Total loss: 5490268.029900 | |
| --- | |
| x1 value: -7.9615993499756 | |
| feval(x) grad value: -0.011567815206945 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105633184 | |
| StyleLoss:updateOutput self.G 2: 6.7866711616516 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5927670677106e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021369068417698 | |
| dG 1: -1.2823988981836e-05 | |
| dG 2: -8.2390957121051e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925764096 | |
| StyleLoss:updateOutput self.G 2: 246.93133544922 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.9219709075696e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00072350952541456 | |
| dG 1: 3.458248465904e-05 | |
| dG 2: 4.4343435173788e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848030208 | |
| StyleLoss:updateOutput self.G 2: 217.47750854492 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1290931329077e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012892306549475 | |
| dG 1: 3.9348935388261e-05 | |
| dG 2: 1.0091044319593e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94667520 | |
| StyleLoss:updateOutput self.G 2: 48.150394439697 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.2238830272036e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019034607103094 | |
| dG 1: 3.5857508464687e-06 | |
| dG 2: 1.8238073811599e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7100936 | |
| StyleLoss:updateOutput self.G 2: 14.446891784668 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3877920537197e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0083267511799932 | |
| dG 1: 1.9872074972227e-06 | |
| dG 2: 4.0429838296663e-12 | |
| --- | |
| Iteration 111 / 2500 | |
| Content 1 loss: 5150433.203125 | |
| Style 1 loss: 78.695912 | |
| Style 2 loss: 30847.798347 | |
| Style 3 loss: 232395.790100 | |
| Style 4 loss: 152448.509216 | |
| Style 5 loss: 18819.281101 | |
| Total loss: 5585023.277802 | |
| --- | |
| x1 value: -7.9628891944885 | |
| feval(x) grad value: 0.011976409703493 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105406264 | |
| StyleLoss:updateOutput self.G 2: 6.7720918655396 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0442298399057e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0019093108130619 | |
| dG 1: -1.9942794097005e-05 | |
| dG 2: -1.2812753245536e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920927232 | |
| StyleLoss:updateOutput self.G 2: 246.31112670898 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0476303903888e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061538169393316 | |
| dG 1: -4.1128441807814e-05 | |
| dG 2: -5.2736990122093e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841146368 | |
| StyleLoss:updateOutput self.G 2: 215.71220397949 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4490375122023e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00078065088018775 | |
| dG 1: -1.4524136531691e-05 | |
| dG 2: -3.7247180166566e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93299568 | |
| StyleLoss:updateOutput self.G 2: 47.454616546631 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2408879374325e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001413730555214 | |
| dG 1: -1.7226129784831e-06 | |
| dG 2: -8.7616648962105e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6894731.5 | |
| StyleLoss:updateOutput self.G 2: 14.027370452881 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0561536782916e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.006336921826005 | |
| dG 1: -1.2134944427089e-06 | |
| dG 2: -2.4688612699358e-12 | |
| --- | |
| Iteration 112 / 2500 | |
| Content 1 loss: 5078351.562500 | |
| Style 1 loss: 90.596829 | |
| Style 2 loss: 30268.143654 | |
| Style 3 loss: 208666.099548 | |
| Style 4 loss: 149667.411804 | |
| Style 5 loss: 18345.663071 | |
| Total loss: 5485389.477406 | |
| --- | |
| x1 value: -7.964870929718 | |
| feval(x) grad value: -0.010268521495163 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105715640 | |
| StyleLoss:updateOutput self.G 2: 6.7919683456421 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8619164876554e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0020561197306961 | |
| dG 1: -1.0237466995022e-05 | |
| dG 2: -6.5773200687985e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927091456 | |
| StyleLoss:updateOutput self.G 2: 247.10153198242 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.0815710507286e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0006465965998359 | |
| dG 1: 5.5357722885674e-05 | |
| dG 2: 7.0982508065809e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845595904 | |
| StyleLoss:updateOutput self.G 2: 216.85325622559 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6822822601625e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00090946682030335 | |
| dG 1: 2.0297313312767e-05 | |
| dG 2: 5.2052507269751e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94399104 | |
| StyleLoss:updateOutput self.G 2: 48.013877868652 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3743767485485e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014531600754708 | |
| dG 1: 2.5441988782404e-06 | |
| dG 2: 1.294046467204e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7066824.5 | |
| StyleLoss:updateOutput self.G 2: 14.377492904663 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1298960771455e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0067793778143823 | |
| dG 1: 1.4577238971469e-06 | |
| dG 2: 2.9657470056177e-12 | |
| --- | |
| Iteration 113 / 2500 | |
| Content 1 loss: 5130116.406250 | |
| Style 1 loss: 71.076147 | |
| Style 2 loss: 32729.653358 | |
| Style 3 loss: 214230.514526 | |
| Style 4 loss: 149644.992828 | |
| Style 5 loss: 18291.516781 | |
| Total loss: 5545084.159891 | |
| --- | |
| x1 value: -7.9663624763489 | |
| feval(x) grad value: 0.0071155894547701 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105488352 | |
| StyleLoss:updateOutput self.G 2: 6.7773661613464 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6268534842065e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013973717577755 | |
| dG 1: -1.7367654436384e-05 | |
| dG 2: -1.1158290003782e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922629888 | |
| StyleLoss:updateOutput self.G 2: 246.52946472168 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5031923200913e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002752025029622 | |
| dG 1: -1.4475409443548e-05 | |
| dG 2: -1.8561111848892e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842390592 | |
| StyleLoss:updateOutput self.G 2: 216.03134155273 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0462768064772e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037426350172609 | |
| dG 1: -4.7867638386379e-06 | |
| dG 2: -1.2275668762746e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93644096 | |
| StyleLoss:updateOutput self.G 2: 47.629848480225 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.3478590201812e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00064875185489655 | |
| dG 1: -3.8566062698919e-07 | |
| dG 2: -1.961571638627e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6929332.5 | |
| StyleLoss:updateOutput self.G 2: 14.097763061523 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.5828783135657e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0039497264660895 | |
| dG 1: -6.7642912426891e-07 | |
| dG 2: -1.3761986342176e-12 | |
| --- | |
| Iteration 114 / 2500 | |
| Content 1 loss: 5100071.093750 | |
| Style 1 loss: 83.555153 | |
| Style 2 loss: 29056.895256 | |
| Style 3 loss: 207152.000427 | |
| Style 4 loss: 148247.097015 | |
| Style 5 loss: 18032.874584 | |
| Total loss: 5502643.516186 | |
| --- | |
| x1 value: -7.9677500724792 | |
| feval(x) grad value: -0.0051737339235842 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105578856 | |
| StyleLoss:updateOutput self.G 2: 6.7831811904907 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0040808951526e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018131342949346 | |
| dG 1: -1.4528317478835e-05 | |
| dG 2: -9.3340851541057e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924100480 | |
| StyleLoss:updateOutput self.G 2: 246.7180480957 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9299703996344e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00025378874852322 | |
| dG 1: 8.5438614405575e-06 | |
| dG 2: 1.0955376146191e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845480512 | |
| StyleLoss:updateOutput self.G 2: 216.82368469238 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5440791401033e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00095158204203472 | |
| dG 1: 1.9395163690206e-05 | |
| dG 2: 4.9738945601119e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94155368 | |
| StyleLoss:updateOutput self.G 2: 47.88990020752 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7275284847074e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010074975434691 | |
| dG 1: 1.598256858415e-06 | |
| dG 2: 8.1291538171074e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7010266 | |
| StyleLoss:updateOutput self.G 2: 14.262422561646 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.3293325663617e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0031975987367332 | |
| dG 1: 5.7981452528111e-07 | |
| dG 2: 1.1796357076918e-12 | |
| --- | |
| Iteration 115 / 2500 | |
| Content 1 loss: 5113632.421875 | |
| Style 1 loss: 80.348879 | |
| Style 2 loss: 29192.347527 | |
| Style 3 loss: 219538.009644 | |
| Style 4 loss: 150397.121429 | |
| Style 5 loss: 17776.296616 | |
| Total loss: 5530616.545969 | |
| --- | |
| x1 value: -7.9692707061768 | |
| feval(x) grad value: 0.0089750094339252 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105428784 | |
| StyleLoss:updateOutput self.G 2: 6.7735390663147 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9819560981869e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020324343349785 | |
| dG 1: -1.9236020307289e-05 | |
| dG 2: -1.2358668775858e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921283456 | |
| StyleLoss:updateOutput self.G 2: 246.35684204102 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.3338660538648e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037327647441998 | |
| dG 1: -3.5549419408198e-05 | |
| dG 2: -4.5583285410156e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840999424 | |
| StyleLoss:updateOutput self.G 2: 215.6745300293 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4978495244122e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00086078781168908 | |
| dG 1: -1.5674939277233e-05 | |
| dG 2: -4.0198426184967e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93600216 | |
| StyleLoss:updateOutput self.G 2: 47.607543945312 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1644691255697e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00071287859464064 | |
| dG 1: -5.5587156566617e-07 | |
| dG 2: -2.8273084286094e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6988230 | |
| StyleLoss:updateOutput self.G 2: 14.217588424683 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2554023953726e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075324135832489 | |
| dG 1: 2.3776794932928e-07 | |
| dG 2: 4.8374032644155e-13 | |
| --- | |
| Iteration 116 / 2500 | |
| Content 1 loss: 5109599.609375 | |
| Style 1 loss: 85.939351 | |
| Style 2 loss: 29261.312485 | |
| Style 3 loss: 209488.128662 | |
| Style 4 loss: 149850.460052 | |
| Style 5 loss: 17706.749439 | |
| Total loss: 5515992.199365 | |
| --- | |
| x1 value: -7.9713473320007 | |
| feval(x) grad value: -0.0087312292307615 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105707536 | |
| StyleLoss:updateOutput self.G 2: 6.7914481163025 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.956169981554e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0022113465238363 | |
| dG 1: -1.0491444299987e-05 | |
| dG 2: -6.740495206263e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926775808 | |
| StyleLoss:updateOutput self.G 2: 247.0611114502 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.6790528541769e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0004733995301649 | |
| dG 1: 5.041775511927e-05 | |
| dG 2: 6.4648221671793e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846043008 | |
| StyleLoss:updateOutput self.G 2: 216.96795654297 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8442898408466e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010156015632674 | |
| dG 1: 2.3797536414349e-05 | |
| dG 2: 6.1028851243428e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94283896 | |
| StyleLoss:updateOutput self.G 2: 47.955265045166 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1614826550831e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012574879219756 | |
| dG 1: 2.0970430796297e-06 | |
| dG 2: 1.0666113174976e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6987552 | |
| StyleLoss:updateOutput self.G 2: 14.216213226318 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0176656789772e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012105993228033 | |
| dG 1: 2.2726290183073e-07 | |
| dG 2: 4.6236761164567e-13 | |
| --- | |
| Iteration 117 / 2500 | |
| Content 1 loss: 5114923.437500 | |
| Style 1 loss: 71.867097 | |
| Style 2 loss: 31299.839973 | |
| Style 3 loss: 209920.944214 | |
| Style 4 loss: 146864.650726 | |
| Style 5 loss: 17436.734676 | |
| Total loss: 5520517.474187 | |
| --- | |
| x1 value: -7.972119808197 | |
| feval(x) grad value: 0.0046233860775828 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105533216 | |
| StyleLoss:updateOutput self.G 2: 6.7802481651306 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.400892367722e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00089483743067831 | |
| dG 1: -1.5960345990607e-05 | |
| dG 2: -1.0254127191453e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923043968 | |
| StyleLoss:updateOutput self.G 2: 246.58253479004 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.8508950451806e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -2.2335898393067e-05 | |
| dG 1: -8.0004410847323e-06 | |
| dG 2: -1.025857242036e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843024448 | |
| StyleLoss:updateOutput self.G 2: 216.19383239746 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.221184735087e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00011978668771917 | |
| dG 1: 1.7355904446958e-07 | |
| dG 2: 4.4509304553651e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93640344 | |
| StyleLoss:updateOutput self.G 2: 47.627944946289 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.3221237900943e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00055200105998665 | |
| dG 1: -4.0021350855568e-07 | |
| dG 2: -2.0355914761953e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6962546 | |
| StyleLoss:updateOutput self.G 2: 14.165336608887 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1905520952714e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013143313117325 | |
| dG 1: -1.6089855137125e-07 | |
| dG 2: -3.2734890644502e-13 | |
| --- | |
| Iteration 118 / 2500 | |
| Content 1 loss: 5106273.046875 | |
| Style 1 loss: 78.218820 | |
| Style 2 loss: 28601.337433 | |
| Style 3 loss: 200388.175964 | |
| Style 4 loss: 145214.275360 | |
| Style 5 loss: 17456.105232 | |
| Total loss: 5498011.159684 | |
| --- | |
| x1 value: -7.9751048088074 | |
| feval(x) grad value: 0.00054324860684574 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105472056 | |
| StyleLoss:updateOutput self.G 2: 6.7763185501099 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6588854612774e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00034854377736337 | |
| dG 1: -1.7878805010696e-05 | |
| dG 2: -1.1486690505019e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922273408 | |
| StyleLoss:updateOutput self.G 2: 246.48371887207 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.3390083586937e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00023919115483295 | |
| dG 1: -2.0058334484929e-05 | |
| dG 2: -2.5719819069653e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842681088 | |
| StyleLoss:updateOutput self.G 2: 216.10581970215 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6004860487205e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00027479682466947 | |
| dG 1: -2.5131821530522e-06 | |
| dG 2: -6.4450614983835e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94167216 | |
| StyleLoss:updateOutput self.G 2: 47.895927429199 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5023894661681e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00086807314073667 | |
| dG 1: 1.6443201502625e-06 | |
| dG 2: 8.3634455697729e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7043082 | |
| StyleLoss:updateOutput self.G 2: 14.329187393188 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.310580253441e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0055863475427032 | |
| dG 1: 1.0891892543441e-06 | |
| dG 2: 2.215961355384e-12 | |
| --- | |
| Iteration 119 / 2500 | |
| Content 1 loss: 5123867.968750 | |
| Style 1 loss: 87.985313 | |
| Style 2 loss: 30423.254013 | |
| Style 3 loss: 212053.985596 | |
| Style 4 loss: 146059.959412 | |
| Style 5 loss: 17534.417152 | |
| Total loss: 5530027.570235 | |
| --- | |
| x1 value: -7.9756531715393 | |
| feval(x) grad value: -0.0001943003735505 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105626560 | |
| StyleLoss:updateOutput self.G 2: 6.786244392395 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7535914881582e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -4.6690001909155e-05 | |
| dG 1: -1.3032027709414e-05 | |
| dG 2: -8.3727545296258e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924641792 | |
| StyleLoss:updateOutput self.G 2: 246.78736877441 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.1424886632058e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00025938168982975 | |
| dG 1: 1.7009670045809e-05 | |
| dG 2: 2.1810671758243e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844018496 | |
| StyleLoss:updateOutput self.G 2: 216.44871520996 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.6465795473268e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00057448429288343 | |
| dG 1: 7.9523724707542e-06 | |
| dG 2: 2.0393881895731e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93696488 | |
| StyleLoss:updateOutput self.G 2: 47.656497955322 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2260442256747e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0002557311381679 | |
| dG 1: -1.8239671817355e-07 | |
| dG 2: -9.2771784991532e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6939079.5 | |
| StyleLoss:updateOutput self.G 2: 14.117597579956 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.6348034149778e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0033808809239417 | |
| dG 1: -5.2512433512675e-07 | |
| dG 2: -1.0683681586979e-12 | |
| --- | |
| Iteration 120 / 2500 | |
| Content 1 loss: 5103221.484375 | |
| Style 1 loss: 69.522891 | |
| Style 2 loss: 31393.375397 | |
| Style 3 loss: 210323.158264 | |
| Style 4 loss: 145659.931183 | |
| Style 5 loss: 17428.874016 | |
| Total loss: 5508096.346126 | |
| --- | |
| x1 value: -7.9774560928345 | |
| feval(x) grad value: -0.00088893866632134 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105587680 | |
| StyleLoss:updateOutput self.G 2: 6.7837481498718 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9798918887991e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00078307639341801 | |
| dG 1: -1.4251236279961e-05 | |
| dG 2: -9.1560667467988e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924234624 | |
| StyleLoss:updateOutput self.G 2: 246.73524475098 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2516955411334e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00017828957061283 | |
| dG 1: 1.0643599125615e-05 | |
| dG 2: 1.3647765643304e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844395968 | |
| StyleLoss:updateOutput self.G 2: 216.54556274414 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0655439660404e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00047181019908749 | |
| dG 1: 1.0907205250987e-05 | |
| dG 2: 2.7971555193601e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94025720 | |
| StyleLoss:updateOutput self.G 2: 47.823959350586 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4365034897237e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00050272047519684 | |
| dG 1: 1.0952647926388e-06 | |
| dG 2: 5.5708058608814e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6995438.5 | |
| StyleLoss:updateOutput self.G 2: 14.232256889343 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0043798915358e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018026278121397 | |
| dG 1: 3.4966745943166e-07 | |
| dG 2: 7.1140039388864e-13 | |
| --- | |
| Iteration 121 / 2500 | |
| Content 1 loss: 5121401.562500 | |
| Style 1 loss: 79.140801 | |
| Style 2 loss: 29725.696564 | |
| Style 3 loss: 205724.922180 | |
| Style 4 loss: 143876.026154 | |
| Style 5 loss: 17267.091751 | |
| Total loss: 5518074.439950 | |
| --- | |
| x1 value: -7.9795827865601 | |
| feval(x) grad value: 0.0019738711416721 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105512768 | |
| StyleLoss:updateOutput self.G 2: 6.7789344787598 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5160176992122e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00066763383802027 | |
| dG 1: -1.6601561583229e-05 | |
| dG 2: -1.0666093659337e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922417536 | |
| StyleLoss:updateOutput self.G 2: 246.50224304199 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.4660476327654e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00034369211061858 | |
| dG 1: -1.77998063009e-05 | |
| dG 2: -2.2823823993962e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841765696 | |
| StyleLoss:updateOutput self.G 2: 215.87103271484 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.8875551423607e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00054234761046246 | |
| dG 1: -9.6775147540029e-06 | |
| dG 2: -2.4818007891836e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93913392 | |
| StyleLoss:updateOutput self.G 2: 47.766822814941 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.2144542078404e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002616478886921 | |
| dG 1: 6.5931493509197e-07 | |
| dG 2: 3.353448974671e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7006665 | |
| StyleLoss:updateOutput self.G 2: 14.255095481873 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.6434615796898e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0027860768605024 | |
| dG 1: 5.2392482530195e-07 | |
| dG 2: 1.0659277280278e-12 | |
| --- | |
| Iteration 122 / 2500 | |
| Content 1 loss: 5113476.953125 | |
| Style 1 loss: 77.972341 | |
| Style 2 loss: 29133.110046 | |
| Style 3 loss: 202945.655823 | |
| Style 4 loss: 143618.637085 | |
| Style 5 loss: 17168.340683 | |
| Total loss: 5506420.669103 | |
| --- | |
| x1 value: -7.980619430542 | |
| feval(x) grad value: -0.0015116464346647 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105592480 | |
| StyleLoss:updateOutput self.G 2: 6.784056186676 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0242784276879e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00090276054106653 | |
| dG 1: -1.4100958651397e-05 | |
| dG 2: -9.0595174591368e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924036096 | |
| StyleLoss:updateOutput self.G 2: 246.70974731445 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3511994129422e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00049935642164201 | |
| dG 1: 7.5319776442484e-06 | |
| dG 2: 9.6578897223337e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845323968 | |
| StyleLoss:updateOutput self.G 2: 216.78355407715 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3642251417423e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00098506058566272 | |
| dG 1: 1.8169082977693e-05 | |
| dG 2: 4.659466389112e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93972232 | |
| StyleLoss:updateOutput self.G 2: 47.79674911499 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.282503299346e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00048533649533056 | |
| dG 1: 8.8765580130712e-07 | |
| dG 2: 4.5148506786462e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6991515 | |
| StyleLoss:updateOutput self.G 2: 14.224274635315 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.4404485543528e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014642692403868 | |
| dG 1: 2.8878147873002e-07 | |
| dG 2: 5.8752742254642e-13 | |
| --- | |
| Iteration 123 / 2500 | |
| Content 1 loss: 5129544.140625 | |
| Style 1 loss: 76.302443 | |
| Style 2 loss: 27949.012756 | |
| Style 3 loss: 205125.205994 | |
| Style 4 loss: 142931.705475 | |
| Style 5 loss: 17281.411171 | |
| Total loss: 5522907.778464 | |
| --- | |
| x1 value: -7.9827404022217 | |
| feval(x) grad value: 0.0080659184604883 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105523656 | |
| StyleLoss:updateOutput self.G 2: 6.7796354293823 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4420569505755e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00088372069876641 | |
| dG 1: -1.6259695257759e-05 | |
| dG 2: -1.0446452730628e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922741760 | |
| StyleLoss:updateOutput self.G 2: 246.54377746582 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.9702987685832e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058558135060593 | |
| dG 1: -1.2727023204206e-05 | |
| dG 2: -1.6319245217319e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841051968 | |
| StyleLoss:updateOutput self.G 2: 215.68797302246 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2049838460371e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00082070223288611 | |
| dG 1: -1.5264971807483e-05 | |
| dG 2: -3.9147057991074e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93545904 | |
| StyleLoss:updateOutput self.G 2: 47.579898834229 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.196107746182e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00076357810758054 | |
| dG 1: -7.6671648230331e-07 | |
| dG 2: -3.8997220708735e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6887955 | |
| StyleLoss:updateOutput self.G 2: 14.013580322266 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0909378715951e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0065456270240247 | |
| dG 1: -1.3186964906708e-06 | |
| dG 2: -2.6828951386892e-12 | |
| --- | |
| Iteration 124 / 2500 | |
| Content 1 loss: 5082154.296875 | |
| Style 1 loss: 78.355540 | |
| Style 2 loss: 27858.558655 | |
| Style 3 loss: 202228.111267 | |
| Style 4 loss: 146389.892578 | |
| Style 5 loss: 17962.573528 | |
| Total loss: 5476671.788443 | |
| --- | |
| x1 value: -7.984760761261 | |
| feval(x) grad value: -0.014389205724001 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105747712 | |
| StyleLoss:updateOutput self.G 2: 6.7940301895142 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7102491867149e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018429156625643 | |
| dG 1: -9.2307991508278e-06 | |
| dG 2: -5.9305604047447e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927368832 | |
| StyleLoss:updateOutput self.G 2: 247.1371307373 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.447417426145e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00091620936291292 | |
| dG 1: 5.9699781559175e-05 | |
| dG 2: 7.6550120409191e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848355264 | |
| StyleLoss:updateOutput self.G 2: 217.5609588623 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0926273691657e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012126062065363 | |
| dG 1: 4.189373794361e-05 | |
| dG 2: 1.0743658566958e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94957544 | |
| StyleLoss:updateOutput self.G 2: 48.297901153564 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.4643346680241e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021010562777519 | |
| dG 1: 4.7111761887209e-06 | |
| dG 2: 2.3962281811563e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7195758 | |
| StyleLoss:updateOutput self.G 2: 14.639803886414 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.721786361486e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.010330720804632 | |
| dG 1: 3.4590059385664e-06 | |
| dG 2: 7.0373646146593e-12 | |
| --- | |
| Iteration 125 / 2500 | |
| Content 1 loss: 5187130.078125 | |
| Style 1 loss: 64.982146 | |
| Style 2 loss: 31284.195900 | |
| Style 3 loss: 231624.183655 | |
| Style 4 loss: 152409.484863 | |
| Style 5 loss: 20412.079811 | |
| Total loss: 5622925.004500 | |
| --- | |
| x1 value: -7.9854125976562 | |
| feval(x) grad value: 0.01320234220475 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105410544 | |
| StyleLoss:updateOutput self.G 2: 6.772367477417 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0960483005392e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016292108921334 | |
| dG 1: -1.9808361685136e-05 | |
| dG 2: -1.272638244787e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920117120 | |
| StyleLoss:updateOutput self.G 2: 246.20726013184 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.8855891533512e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0005648709484376 | |
| dG 1: -5.3807299991604e-05 | |
| dG 2: -6.8994475931061e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841179712 | |
| StyleLoss:updateOutput self.G 2: 215.72080993652 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3939376231065e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00068077590549365 | |
| dG 1: -1.4263279808802e-05 | |
| dG 2: -3.6578218752525e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93292080 | |
| StyleLoss:updateOutput self.G 2: 47.450817108154 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2539430233337e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0014042161637917 | |
| dG 1: -1.7516545085527e-06 | |
| dG 2: -8.9093749738867e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6922262.5 | |
| StyleLoss:updateOutput self.G 2: 14.083381652832 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.8710451134612e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0047226268798113 | |
| dG 1: -7.8616051268909e-07 | |
| dG 2: -1.599447679336e-12 | |
| --- | |
| Iteration 126 / 2500 | |
| Content 1 loss: 5091405.078125 | |
| Style 1 loss: 88.302283 | |
| Style 2 loss: 28837.140083 | |
| Style 3 loss: 196200.370789 | |
| Style 4 loss: 142852.958679 | |
| Style 5 loss: 17322.438240 | |
| Total loss: 5476706.288199 | |
| --- | |
| x1 value: -7.9880895614624 | |
| feval(x) grad value: -0.0063618756830692 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105633480 | |
| StyleLoss:updateOutput self.G 2: 6.7866902351379 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6809438224973e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013307862682268 | |
| dG 1: -1.281479580939e-05 | |
| dG 2: -8.2331895207705e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924941824 | |
| StyleLoss:updateOutput self.G 2: 246.82591247559 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.4217800027345e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00011919259122806 | |
| dG 1: 2.1710835426347e-05 | |
| dG 2: 2.7838746932685e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844913408 | |
| StyleLoss:updateOutput self.G 2: 216.6782989502 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.37602953032e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00070105603663251 | |
| dG 1: 1.4957266103011e-05 | |
| dG 2: 3.8357945292278e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94161616 | |
| StyleLoss:updateOutput self.G 2: 47.893074035645 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6215113873841e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00091436615912244 | |
| dG 1: 1.6225648096224e-06 | |
| dG 2: 8.252791896049e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6987986.5 | |
| StyleLoss:updateOutput self.G 2: 14.217094421387 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8033877324797e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010820326860994 | |
| dG 1: 2.340062934536e-07 | |
| dG 2: 4.7608705172619e-13 | |
| --- | |
| Iteration 127 / 2500 | |
| Content 1 loss: 5118827.343750 | |
| Style 1 loss: 71.712960 | |
| Style 2 loss: 27752.520561 | |
| Style 3 loss: 197072.547913 | |
| Style 4 loss: 141955.341339 | |
| Style 5 loss: 17095.252991 | |
| Total loss: 5502774.719513 | |
| --- | |
| x1 value: -7.9880089759827 | |
| feval(x) grad value: -1.125626386056e-05 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105601712 | |
| StyleLoss:updateOutput self.G 2: 6.7846493721008 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.98929414555e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00011065755825257 | |
| dG 1: -1.3811371900374e-05 | |
| dG 2: -8.8734656558329e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924003072 | |
| StyleLoss:updateOutput self.G 2: 246.70550537109 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3026982987062e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00039869631291367 | |
| dG 1: 7.0133860390342e-06 | |
| dG 2: 8.9929214248941e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843545408 | |
| StyleLoss:updateOutput self.G 2: 216.32740783691 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9864676065472e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00024451891658828 | |
| dG 1: 4.249700850778e-06 | |
| dG 2: 1.0898366627557e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93936648 | |
| StyleLoss:updateOutput self.G 2: 47.778648376465 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.8771731542274e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00028866034699604 | |
| dG 1: 7.4955642048735e-07 | |
| dG 2: 3.8124416275841e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7045468 | |
| StyleLoss:updateOutput self.G 2: 14.334043502808 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.1603385499184e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0054962043650448 | |
| dG 1: 1.1262317229921e-06 | |
| dG 2: 2.2913242483935e-12 | |
| --- | |
| Iteration 128 / 2500 | |
| Content 1 loss: 5135796.093750 | |
| Style 1 loss: 72.639640 | |
| Style 2 loss: 27143.305779 | |
| Style 3 loss: 198167.861938 | |
| Style 4 loss: 144047.172546 | |
| Style 5 loss: 17547.071457 | |
| Total loss: 5522774.145111 | |
| --- | |
| x1 value: -7.990620136261 | |
| feval(x) grad value: 0.0086744800209999 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105470928 | |
| StyleLoss:updateOutput self.G 2: 6.7762460708618 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7606806557733e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00070944242179394 | |
| dG 1: -1.7914182535606e-05 | |
| dG 2: -1.1509420803565e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921265024 | |
| StyleLoss:updateOutput self.G 2: 246.35443115234 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.5614194966201e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00071536743780598 | |
| dG 1: -3.5841287171934e-05 | |
| dG 2: -4.595754332648e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841558272 | |
| StyleLoss:updateOutput self.G 2: 215.81784057617 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.2461532713296e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0004754328110721 | |
| dG 1: -1.1300698133709e-05 | |
| dG 2: -2.8980669860429e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93473456 | |
| StyleLoss:updateOutput self.G 2: 47.543067932129 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2557963202653e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00083968963008374 | |
| dG 1: -1.0478346439413e-06 | |
| dG 2: -5.3295605775827e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6868561 | |
| StyleLoss:updateOutput self.G 2: 13.97412109375 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2573095773405e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0075438576750457 | |
| dG 1: -1.6197184322664e-06 | |
| dG 2: -3.2953258177759e-12 | |
| --- | |
| Iteration 129 / 2500 | |
| Content 1 loss: 5082125.781250 | |
| Style 1 loss: 82.950795 | |
| Style 2 loss: 27561.235428 | |
| Style 3 loss: 202373.977661 | |
| Style 4 loss: 144630.134583 | |
| Style 5 loss: 18062.423229 | |
| Total loss: 5474836.502946 | |
| --- | |
| x1 value: -7.9926810264587 | |
| feval(x) grad value: -0.013273762539029 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105735824 | |
| StyleLoss:updateOutput self.G 2: 6.7932662963867 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8873128837527e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017836410552263 | |
| dG 1: -9.6040675998665e-06 | |
| dG 2: -6.17037670958e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926953216 | |
| StyleLoss:updateOutput self.G 2: 247.0838470459 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.9668915848051e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00083669123705477 | |
| dG 1: 5.3197076340439e-05 | |
| dG 2: 6.8212015896796e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 848132992 | |
| StyleLoss:updateOutput self.G 2: 217.50387573242 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0635285125081e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012184993829578 | |
| dG 1: 4.0153150621336e-05 | |
| dG 2: 1.0297287328376e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94727088 | |
| StyleLoss:updateOutput self.G 2: 48.180690765381 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1596977123627e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018742376705632 | |
| dG 1: 3.8168836908881e-06 | |
| dG 2: 1.9413678564034e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7120144.5 | |
| StyleLoss:updateOutput self.G 2: 14.485973358154 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4800268672843e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0088801626116037 | |
| dG 1: 2.285378968736e-06 | |
| dG 2: 4.6496148944919e-12 | |
| --- | |
| Iteration 130 / 2500 | |
| Content 1 loss: 5172793.359375 | |
| Style 1 loss: 64.549014 | |
| Style 2 loss: 29872.372627 | |
| Style 3 loss: 223748.657227 | |
| Style 4 loss: 147217.128754 | |
| Style 5 loss: 18343.787670 | |
| Total loss: 5592039.854667 | |
| --- | |
| x1 value: -7.9924478530884 | |
| feval(x) grad value: 0.012822026386857 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105509976 | |
| StyleLoss:updateOutput self.G 2: 6.7787561416626 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6391909929989e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016700215637684 | |
| dG 1: -1.6688974938006e-05 | |
| dG 2: -1.0722254247669e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921962752 | |
| StyleLoss:updateOutput self.G 2: 246.44389343262 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8156667986632e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00039836121140979 | |
| dG 1: -2.4921249860199e-05 | |
| dG 2: -3.1955299951286e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840662080 | |
| StyleLoss:updateOutput self.G 2: 215.58804321289 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6134148950186e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00088888761820272 | |
| dG 1: -1.831431654864e-05 | |
| dG 2: -4.6967100347794e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93315200 | |
| StyleLoss:updateOutput self.G 2: 47.462551116943 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1253366355722e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0012925241608173 | |
| dG 1: -1.6619871985313e-06 | |
| dG 2: -8.4533042458285e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6928160.5 | |
| StyleLoss:updateOutput self.G 2: 14.09538269043 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.1343396257362e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0042806039564312 | |
| dG 1: -6.9459827045648e-07 | |
| dG 2: -1.4131637483469e-12 | |
| --- | |
| Iteration 131 / 2500 | |
| Content 1 loss: 5092126.171875 | |
| Style 1 loss: 77.341927 | |
| Style 2 loss: 26811.624527 | |
| Style 3 loss: 193811.622620 | |
| Style 4 loss: 142956.882477 | |
| Style 5 loss: 17168.756962 | |
| Total loss: 5472952.400387 | |
| --- | |
| x1 value: -7.9952001571655 | |
| feval(x) grad value: -0.0085279308259487 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105686688 | |
| StyleLoss:updateOutput self.G 2: 6.7901082038879 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2482582906491e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019669006578624 | |
| dG 1: -1.1145899406984e-05 | |
| dG 2: -7.1609650717855e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925539072 | |
| StyleLoss:updateOutput self.G 2: 246.90249633789 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.7758591626443e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00030658472678624 | |
| dG 1: 3.1061194022186e-05 | |
| dG 2: 3.9828253542429e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845565760 | |
| StyleLoss:updateOutput self.G 2: 216.84555053711 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7124931162016e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00091859541134909 | |
| dG 1: 2.0062332623638e-05 | |
| dG 2: 5.1449907702283e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94306384 | |
| StyleLoss:updateOutput self.G 2: 47.966705322266 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1887890966354e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012975747231394 | |
| dG 1: 2.1843279682798e-06 | |
| dG 2: 1.1110069027959e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7003899 | |
| StyleLoss:updateOutput self.G 2: 14.249467849731 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.5661445824408e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0027396869845688 | |
| dG 1: 4.809889446733e-07 | |
| dG 2: 9.7857464319284e-13 | |
| --- | |
| Iteration 132 / 2500 | |
| Content 1 loss: 5126475.390625 | |
| Style 1 loss: 68.262918 | |
| Style 2 loss: 27516.114235 | |
| Style 3 loss: 195115.150452 | |
| Style 4 loss: 140057.704926 | |
| Style 5 loss: 16913.394928 | |
| Total loss: 5506146.018083 | |
| --- | |
| x1 value: -7.9958996772766 | |
| feval(x) grad value: 0.0047468231059611 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105518536 | |
| StyleLoss:updateOutput self.G 2: 6.7793064117432 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6275757508974e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0014233643887565 | |
| dG 1: -1.642022652959e-05 | |
| dG 2: -1.054958963069e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921921792 | |
| StyleLoss:updateOutput self.G 2: 246.43864440918 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4361271989146e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00013269211922307 | |
| dG 1: -2.5562992959749e-05 | |
| dG 2: -3.2778176874138e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842918784 | |
| StyleLoss:updateOutput self.G 2: 216.1667175293 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0355817415284e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.0532292991411e-05 | |
| dG 1: -6.5464865883769e-07 | |
| dG 2: -1.6788476262398e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93758592 | |
| StyleLoss:updateOutput self.G 2: 47.688083648682 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0509192444915e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00030439120018855 | |
| dG 1: 5.8607604103145e-08 | |
| dG 2: 2.9809359462177e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6987649.5 | |
| StyleLoss:updateOutput self.G 2: 14.21641254425 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2737943677621e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00076427619205788 | |
| dG 1: 2.2877063088345e-07 | |
| dG 2: 4.6543498222196e-13 | |
| --- | |
| Iteration 133 / 2500 | |
| Content 1 loss: 5128560.156250 | |
| Style 1 loss: 74.994676 | |
| Style 2 loss: 26877.679825 | |
| Style 3 loss: 198221.649170 | |
| Style 4 loss: 142090.003967 | |
| Style 5 loss: 17110.011578 | |
| Total loss: 5512934.495465 | |
| --- | |
| x1 value: -7.9973306655884 | |
| feval(x) grad value: 0.0019848074298352 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105593648 | |
| StyleLoss:updateOutput self.G 2: 6.7841305732727 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9968905579381e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011034649796784 | |
| dG 1: -1.4064439710637e-05 | |
| dG 2: -9.0360553241242e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923349120 | |
| StyleLoss:updateOutput self.G 2: 246.62168884277 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.498896114124e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.712089816574e-05 | |
| dG 1: -3.2189686862694e-06 | |
| dG 2: -4.1275254156377e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842875392 | |
| StyleLoss:updateOutput self.G 2: 216.15560913086 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.7332920876356e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 8.9712819317356e-05 | |
| dG 1: -9.9310796031205e-07 | |
| dG 2: -2.5468277660423e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93862880 | |
| StyleLoss:updateOutput self.G 2: 47.741146087646 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.1116575860988e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002687104861252 | |
| dG 1: 4.6337538606167e-07 | |
| dG 2: 2.3568489824696e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6981052 | |
| StyleLoss:updateOutput self.G 2: 14.202987670898 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4840955486761e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00089045683853328 | |
| dG 1: 1.2636576229852e-07 | |
| dG 2: 2.5709184672644e-13 | |
| --- | |
| Iteration 134 / 2500 | |
| Content 1 loss: 5104125.781250 | |
| Style 1 loss: 76.698242 | |
| Style 2 loss: 28211.697578 | |
| Style 3 loss: 215455.421448 | |
| Style 4 loss: 149651.401520 | |
| Style 5 loss: 17268.635273 | |
| Total loss: 5514789.635311 | |
| --- | |
| x1 value: -8.0003643035889 | |
| feval(x) grad value: -0.0031100625637919 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105660128 | |
| StyleLoss:updateOutput self.G 2: 6.7884030342102 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.578712710033e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00019301382417325 | |
| dG 1: -1.1978483598796e-05 | |
| dG 2: -7.6958811502312e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925147520 | |
| StyleLoss:updateOutput self.G 2: 246.85229492188 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.6372169154884e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00028686368023045 | |
| dG 1: 2.4931910957093e-05 | |
| dG 2: 3.1968971740681e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843999936 | |
| StyleLoss:updateOutput self.G 2: 216.44398498535 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.6792759889058e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00023524698917754 | |
| dG 1: 7.8080920502543e-06 | |
| dG 2: 2.0023871883518e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94011672 | |
| StyleLoss:updateOutput self.G 2: 47.816802978516 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.9806474439483e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0003164381487295 | |
| dG 1: 1.0406876072011e-06 | |
| dG 2: 5.2932110734469e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6989585 | |
| StyleLoss:updateOutput self.G 2: 14.220349311829 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5265082708993e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00091590505326167 | |
| dG 1: 2.5881215037771e-07 | |
| dG 2: 5.2655460287127e-13 | |
| --- | |
| Iteration 135 / 2500 | |
| Content 1 loss: 5132861.718750 | |
| Style 1 loss: 65.162648 | |
| Style 2 loss: 27324.577332 | |
| Style 3 loss: 202220.626831 | |
| Style 4 loss: 142935.585022 | |
| Style 5 loss: 17025.044918 | |
| Total loss: 5522432.715500 | |
| --- | |
| x1 value: -8.0011072158813 | |
| feval(x) grad value: -0.00027952951495536 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105610072 | |
| StyleLoss:updateOutput self.G 2: 6.7851858139038 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9573570259345e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00063910649623722 | |
| dG 1: -1.3549208233599e-05 | |
| dG 2: -8.705033222034e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923573248 | |
| StyleLoss:updateOutput self.G 2: 246.650390625 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0057610744241e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00015033723320812 | |
| dG 1: 2.859845835701e-07 | |
| dG 2: 3.6670441531418e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843822976 | |
| StyleLoss:updateOutput self.G 2: 216.39859008789 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0036890892643e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00053293158998713 | |
| dG 1: 6.4226828726532e-06 | |
| dG 2: 1.6470989069178e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93879240 | |
| StyleLoss:updateOutput self.G 2: 47.749454498291 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.1773130732281e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00023738251184113 | |
| dG 1: 5.2682946716232e-07 | |
| dG 2: 2.679593743074e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6982161 | |
| StyleLoss:updateOutput self.G 2: 14.205242156982 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0298072794512e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061788462335244 | |
| dG 1: 1.4358134592385e-07 | |
| dG 2: 2.9211707922375e-13 | |
| --- | |
| Iteration 136 / 2500 | |
| Content 1 loss: 5121801.171875 | |
| Style 1 loss: 71.167136 | |
| Style 2 loss: 25464.451790 | |
| Style 3 loss: 185863.449097 | |
| Style 4 loss: 137256.225586 | |
| Style 5 loss: 16560.894012 | |
| Total loss: 5487017.359496 | |
| --- | |
| x1 value: -8.0029964447021 | |
| feval(x) grad value: 0.0077411718666553 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105477048 | |
| StyleLoss:updateOutput self.G 2: 6.7766399383545 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8020914416848e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00096667243633419 | |
| dG 1: -1.7721988115227e-05 | |
| dG 2: -1.1385937765535e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921213696 | |
| StyleLoss:updateOutput self.G 2: 246.34785461426 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.044413763424e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00049556244630367 | |
| dG 1: -3.6644782085204e-05 | |
| dG 2: -4.6987821619715e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841465344 | |
| StyleLoss:updateOutput self.G 2: 215.79399108887 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2924049030971e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00073008419713005 | |
| dG 1: -1.2029268873448e-05 | |
| dG 2: -3.0849090643104e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93806144 | |
| StyleLoss:updateOutput self.G 2: 47.712272644043 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.4168786971757e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -3.3951146178879e-05 | |
| dG 1: 2.4313544599863e-07 | |
| dG 2: 1.2366507557565e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6984742.5 | |
| StyleLoss:updateOutput self.G 2: 14.210493087769 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3468392978666e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00080810341751203 | |
| dG 1: 1.836304193148e-07 | |
| dG 2: 3.7359698668027e-13 | |
| --- | |
| Iteration 137 / 2500 | |
| Content 1 loss: 5116444.531250 | |
| Style 1 loss: 80.064688 | |
| Style 2 loss: 26383.841515 | |
| Style 3 loss: 185846.534729 | |
| Style 4 loss: 137185.009003 | |
| Style 5 loss: 16537.589550 | |
| Total loss: 5482477.570734 | |
| --- | |
| x1 value: -8.005410194397 | |
| feval(x) grad value: -0.011022688820958 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105794544 | |
| StyleLoss:updateOutput self.G 2: 6.7970371246338 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3029415768528e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013862423365936 | |
| dG 1: -7.7624736150028e-06 | |
| dG 2: -4.9871971786672e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927424768 | |
| StyleLoss:updateOutput self.G 2: 247.14424133301 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4077819312588e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00067640998167917 | |
| dG 1: 6.0571223002626e-05 | |
| dG 2: 7.7667533862624e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846977984 | |
| StyleLoss:updateOutput self.G 2: 217.20774841309 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9522479988154e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011194647522643 | |
| dG 1: 3.1115047022467e-05 | |
| dG 2: 7.9794617094398e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94406440 | |
| StyleLoss:updateOutput self.G 2: 48.017601013184 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.3563903539525e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013903558719903 | |
| dG 1: 2.5726312742336e-06 | |
| dG 2: 1.3085079894615e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7042144.5 | |
| StyleLoss:updateOutput self.G 2: 14.327279090881 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.1231243004586e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0054738754406571 | |
| dG 1: 1.0746467751233e-06 | |
| dG 2: 2.1863745622991e-12 | |
| --- | |
| Iteration 138 / 2500 | |
| Content 1 loss: 5156067.578125 | |
| Style 1 loss: 59.089735 | |
| Style 2 loss: 30220.470428 | |
| Style 3 loss: 204137.878418 | |
| Style 4 loss: 139830.436707 | |
| Style 5 loss: 16800.081253 | |
| Total loss: 5547115.534666 | |
| --- | |
| x1 value: -8.0061845779419 | |
| feval(x) grad value: 0.0099856471642852 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105595848 | |
| StyleLoss:updateOutput self.G 2: 6.7842717170715 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0656615024327e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00053883000509813 | |
| dG 1: -1.3995411791257e-05 | |
| dG 2: -8.9917065763598e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923053568 | |
| StyleLoss:updateOutput self.G 2: 246.58377075195 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.4183478793752e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00035489982110448 | |
| dG 1: -7.8466018749168e-06 | |
| dG 2: -1.0061314845503e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841489856 | |
| StyleLoss:updateOutput self.G 2: 215.80024719238 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.9937522318451e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00050740869482979 | |
| dG 1: -1.1838346836157e-05 | |
| dG 2: -3.0359467110413e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93248480 | |
| StyleLoss:updateOutput self.G 2: 47.428630828857 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1605144695513e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013525740941986 | |
| dG 1: -1.9208498542866e-06 | |
| dG 2: -9.7699506904775e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6851528.5 | |
| StyleLoss:updateOutput self.G 2: 13.939469337463 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.394745709149e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0083684744313359 | |
| dG 1: -1.8841038809114e-06 | |
| dG 2: -3.8332188985468e-12 | |
| --- | |
| Iteration 139 / 2500 | |
| Content 1 loss: 5079401.562500 | |
| Style 1 loss: 71.237041 | |
| Style 2 loss: 25833.114624 | |
| Style 3 loss: 192479.896545 | |
| Style 4 loss: 143325.290680 | |
| Style 5 loss: 17892.964840 | |
| Total loss: 5459004.066230 | |
| --- | |
| x1 value: -8.0087184906006 | |
| feval(x) grad value: -0.0085098268464208 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105606312 | |
| StyleLoss:updateOutput self.G 2: 6.7849435806274 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.025903794196e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012157722376287 | |
| dG 1: -1.3667422535946e-05 | |
| dG 2: -8.7809810421155e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923937664 | |
| StyleLoss:updateOutput self.G 2: 246.69715881348 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1400762051371e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00051934306975454 | |
| dG 1: 5.9930116549367e-06 | |
| dG 2: 7.6845463586189e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846014144 | |
| StyleLoss:updateOutput self.G 2: 216.96051025391 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4985674567924e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0009262838284485 | |
| dG 1: 2.3571130441269e-05 | |
| dG 2: 6.0448234959209e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94547752 | |
| StyleLoss:updateOutput self.G 2: 48.089473724365 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.8731898282786e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017212186940014 | |
| dG 1: 3.1209765438689e-06 | |
| dG 2: 1.5874110836575e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7130941.5 | |
| StyleLoss:updateOutput self.G 2: 14.507939338684 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5643404367438e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0093860412016511 | |
| dG 1: 2.4529576876375e-06 | |
| dG 2: 4.990555278056e-12 | |
| --- | |
| Iteration 140 / 2500 | |
| Content 1 loss: 5167894.921875 | |
| Style 1 loss: 70.160357 | |
| Style 2 loss: 25403.077126 | |
| Style 3 loss: 203982.330322 | |
| Style 4 loss: 141835.830688 | |
| Style 5 loss: 18013.926029 | |
| Total loss: 5557200.246398 | |
| --- | |
| x1 value: -8.009955406189 | |
| feval(x) grad value: 0.01153380703181 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105428464 | |
| StyleLoss:updateOutput self.G 2: 6.7735185623169 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.1199196942898e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0022501421626657 | |
| dG 1: -1.9246208466939e-05 | |
| dG 2: -1.2365214104373e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919970944 | |
| StyleLoss:updateOutput self.G 2: 246.18852233887 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.2344855318061e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00084519438678399 | |
| dG 1: -5.6094835599652e-05 | |
| dG 2: -7.1927672151695e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840477568 | |
| StyleLoss:updateOutput self.G 2: 215.54063415527 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6377971689963e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00096051680156961 | |
| dG 1: -1.9759812857956e-05 | |
| dG 2: -5.0674087322122e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93433632 | |
| StyleLoss:updateOutput self.G 2: 47.522819519043 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8484669794816e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001177433761768 | |
| dG 1: -1.2023698445773e-06 | |
| dG 2: -6.1155692055587e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6922526.5 | |
| StyleLoss:updateOutput self.G 2: 14.083914756775 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.0571015814712e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0048342617228627 | |
| dG 1: -7.8208688591985e-07 | |
| dG 2: -1.5911596042487e-12 | |
| --- | |
| Iteration 141 / 2500 | |
| Content 1 loss: 5102711.718750 | |
| Style 1 loss: 80.572827 | |
| Style 2 loss: 27424.209595 | |
| Style 3 loss: 194737.335205 | |
| Style 4 loss: 138264.175415 | |
| Style 5 loss: 16841.308594 | |
| Total loss: 5480059.320385 | |
| --- | |
| x1 value: -8.0114078521729 | |
| feval(x) grad value: -0.010707290843129 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105837912 | |
| StyleLoss:updateOutput self.G 2: 6.7998237609863 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.3334183997863e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0026558260433376 | |
| dG 1: -6.4018067860161e-06 | |
| dG 2: -4.1130027985878e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927956736 | |
| StyleLoss:updateOutput self.G 2: 247.2124786377 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0068729494606e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00070305884582922 | |
| dG 1: 6.8899265897926e-05 | |
| dG 2: 8.8346179330512e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847350720 | |
| StyleLoss:updateOutput self.G 2: 217.30328369141 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0355780350201e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011592383962125 | |
| dG 1: 3.4031181712635e-05 | |
| dG 2: 8.7273036020186e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94384488 | |
| StyleLoss:updateOutput self.G 2: 48.006435394287 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.430669212572e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014395472826436 | |
| dG 1: 2.4874186692614e-06 | |
| dG 2: 1.2651670076164e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7040040 | |
| StyleLoss:updateOutput self.G 2: 14.322996139526 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.5935581612139e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0057561355642974 | |
| dG 1: 1.0419628324598e-06 | |
| dG 2: 2.1198787083371e-12 | |
| --- | |
| Iteration 142 / 2500 | |
| Content 1 loss: 5144273.046875 | |
| Style 1 loss: 61.675256 | |
| Style 2 loss: 30358.234406 | |
| Style 3 loss: 209824.928284 | |
| Style 4 loss: 139658.660889 | |
| Style 5 loss: 16760.127068 | |
| Total loss: 5540936.672777 | |
| --- | |
| x1 value: -8.0125255584717 | |
| feval(x) grad value: 0.0040401699952781 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105668032 | |
| StyleLoss:updateOutput self.G 2: 6.7889094352722 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5823643667877e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00081607763422653 | |
| dG 1: -1.1730694495782e-05 | |
| dG 2: -7.5366817821332e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924688768 | |
| StyleLoss:updateOutput self.G 2: 246.79348754883 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.7536303782181e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00031114282319322 | |
| dG 1: 1.7748921891325e-05 | |
| dG 2: 2.2758578875626e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842980864 | |
| StyleLoss:updateOutput self.G 2: 216.1826171875 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4863185349157e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 4.0975486626849e-05 | |
| dG 1: -1.6806237113087e-07 | |
| dG 2: -4.3099570678877e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93551088 | |
| StyleLoss:updateOutput self.G 2: 47.582542419434 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2625058332105e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00081958930240944 | |
| dG 1: -7.4660363225121e-07 | |
| dG 2: -3.7974227169897e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6923886.5 | |
| StyleLoss:updateOutput self.G 2: 14.086681365967 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.8334909403566e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0047000953927636 | |
| dG 1: -7.6097012424725e-07 | |
| dG 2: -1.5481975510628e-12 | |
| --- | |
| Iteration 143 / 2500 | |
| Content 1 loss: 5114129.687500 | |
| Style 1 loss: 63.260855 | |
| Style 2 loss: 25307.733536 | |
| Style 3 loss: 184371.105194 | |
| Style 4 loss: 137794.166565 | |
| Style 5 loss: 16811.619759 | |
| Total loss: 5478477.573409 | |
| --- | |
| x1 value: -8.014440536499 | |
| feval(x) grad value: 0.000328349735355 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105496400 | |
| StyleLoss:updateOutput self.G 2: 6.7778835296631 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7382505979622e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020149555348326 | |
| dG 1: -1.7115195078077e-05 | |
| dG 2: -1.099608902197e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921276800 | |
| StyleLoss:updateOutput self.G 2: 246.35598754883 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.0191369161421e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00056545936968178 | |
| dG 1: -3.5653843951877e-05 | |
| dG 2: -4.5717188715266e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842753984 | |
| StyleLoss:updateOutput self.G 2: 216.12451171875 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.9163832742919e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00014030614693183 | |
| dG 1: -1.9420197077125e-06 | |
| dG 2: -4.9803124948719e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94148656 | |
| StyleLoss:updateOutput self.G 2: 47.886489868164 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.709757810886e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010092961601913 | |
| dG 1: 1.5723045407867e-06 | |
| dG 2: 7.9971532868095e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7031806 | |
| StyleLoss:updateOutput self.G 2: 14.306246757507 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.1688602904251e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0049013155512512 | |
| dG 1: 9.1416256964294e-07 | |
| dG 2: 1.8598685959381e-12 | |
| --- | |
| Iteration 144 / 2500 | |
| Content 1 loss: 5132205.468750 | |
| Style 1 loss: 77.249771 | |
| Style 2 loss: 25259.556770 | |
| Style 3 loss: 187642.925262 | |
| Style 4 loss: 137075.454712 | |
| Style 5 loss: 16612.752914 | |
| Total loss: 5498873.408180 | |
| --- | |
| x1 value: -8.0152530670166 | |
| feval(x) grad value: 0.0038389503024518 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105572288 | |
| StyleLoss:updateOutput self.G 2: 6.7827596664429 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.3044290859152e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00031795067479834 | |
| dG 1: -1.4733890566276e-05 | |
| dG 2: -9.4661615785557e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922536704 | |
| StyleLoss:updateOutput self.G 2: 246.51748657227 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.101572332298e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 5.7844634284265e-05 | |
| dG 1: -1.5936779163894e-05 | |
| dG 2: -2.0434960147642e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843835456 | |
| StyleLoss:updateOutput self.G 2: 216.40174865723 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.3783934700059e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00040400531725027 | |
| dG 1: 6.5194767557841e-06 | |
| dG 2: 1.6719217156569e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93740352 | |
| StyleLoss:updateOutput self.G 2: 47.678813934326 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5051038455313e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00031971308635548 | |
| dG 1: -1.2150586314874e-08 | |
| dG 2: -6.1800743559118e-15 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6990045 | |
| StyleLoss:updateOutput self.G 2: 14.221282958984 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8802863621659e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011281720362604 | |
| dG 1: 2.6594429414217e-07 | |
| dG 2: 5.4106502264673e-13 | |
| --- | |
| Iteration 145 / 2500 | |
| Content 1 loss: 5135159.765625 | |
| Style 1 loss: 72.964467 | |
| Style 2 loss: 24869.656563 | |
| Style 3 loss: 185557.880402 | |
| Style 4 loss: 136084.522247 | |
| Style 5 loss: 16620.843887 | |
| Total loss: 5498365.633191 | |
| --- | |
| x1 value: -8.0175380706787 | |
| feval(x) grad value: -0.0035449499264359 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105762712 | |
| StyleLoss:updateOutput self.G 2: 6.7949924468994 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5979415124434e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00065801694290712 | |
| dG 1: -8.7606113083893e-06 | |
| dG 2: -5.6284756586381e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926374912 | |
| StyleLoss:updateOutput self.G 2: 247.00965881348 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.1913064775563e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020532398775686 | |
| dG 1: 4.4140324462205e-05 | |
| dG 2: 5.6598983312617e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843312640 | |
| StyleLoss:updateOutput self.G 2: 216.2677154541 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.5244934909761e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00024465858587064 | |
| dG 1: 2.4282417143695e-06 | |
| dG 2: 6.2272333557073e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93906808 | |
| StyleLoss:updateOutput self.G 2: 47.763492584229 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.5602284615143e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00026616203831509 | |
| dG 1: 6.3385579096575e-07 | |
| dG 2: 3.2239572882002e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6935120.5 | |
| StyleLoss:updateOutput self.G 2: 14.109538078308 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.2044063042777e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0037226437125355 | |
| dG 1: -5.8659259138949e-07 | |
| dG 2: -1.1934257835439e-12 | |
| --- | |
| Iteration 146 / 2500 | |
| Content 1 loss: 5108890.234375 | |
| Style 1 loss: 58.015862 | |
| Style 2 loss: 28498.188972 | |
| Style 3 loss: 188648.689270 | |
| Style 4 loss: 137362.380981 | |
| Style 5 loss: 16606.704712 | |
| Total loss: 5480064.214173 | |
| --- | |
| x1 value: -8.0184497833252 | |
| feval(x) grad value: -0.0047481344081461 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105661840 | |
| StyleLoss:updateOutput self.G 2: 6.7885122299194 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.6390216234518e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010347662027925 | |
| dG 1: -1.1925159924431e-05 | |
| dG 2: -7.6616198194795e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924576512 | |
| StyleLoss:updateOutput self.G 2: 246.77908325195 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.7149149534343e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00045312679139897 | |
| dG 1: 1.5992669432308e-05 | |
| dG 2: 2.0506623742839e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844601856 | |
| StyleLoss:updateOutput self.G 2: 216.59838867188 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4543047168827e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00050716620171443 | |
| dG 1: 1.2518799849204e-05 | |
| dG 2: 3.2104492675433e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94352064 | |
| StyleLoss:updateOutput self.G 2: 47.989944458008 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2842753821806e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013467042008415 | |
| dG 1: 2.3615543796041e-06 | |
| dG 2: 1.2011485556185e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7095174 | |
| StyleLoss:updateOutput self.G 2: 14.435171127319 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4053764516575e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084322588518262 | |
| dG 1: 1.8977843865287e-06 | |
| dG 2: 3.861052103038e-12 | |
| --- | |
| Iteration 147 / 2500 | |
| Content 1 loss: 5160293.750000 | |
| Style 1 loss: 66.906050 | |
| Style 2 loss: 25709.126472 | |
| Style 3 loss: 190442.207336 | |
| Style 4 loss: 135715.278625 | |
| Style 5 loss: 17122.488499 | |
| Total loss: 5529349.756983 | |
| --- | |
| x1 value: -8.0191040039062 | |
| feval(x) grad value: 0.014956108294427 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105423040 | |
| StyleLoss:updateOutput self.G 2: 6.7731695175171 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2027686220081e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0022431588731706 | |
| dG 1: -1.9416425857344e-05 | |
| dG 2: -1.2474572156501e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919011072 | |
| StyleLoss:updateOutput self.G 2: 246.06539916992 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0368410130468e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00086343957809731 | |
| dG 1: -7.1121408836916e-05 | |
| dG 2: -9.1195523355125e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 839904704 | |
| StyleLoss:updateOutput self.G 2: 215.39370727539 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7366768645388e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00093012338038534 | |
| dG 1: -2.4243754523923e-05 | |
| dG 2: -6.2173165921164e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93072736 | |
| StyleLoss:updateOutput self.G 2: 47.339248657227 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7879158892574e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0017404053360224 | |
| dG 1: -2.6028394586319e-06 | |
| dG 2: -1.3238730858095e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6870951 | |
| StyleLoss:updateOutput self.G 2: 13.978985786438 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2886954436908e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0077321720309556 | |
| dG 1: -1.5826408343855e-06 | |
| dG 2: -3.2198913674231e-12 | |
| --- | |
| Iteration 148 / 2500 | |
| Content 1 loss: 5087872.656250 | |
| Style 1 loss: 79.261987 | |
| Style 2 loss: 28470.293999 | |
| Style 3 loss: 196086.914062 | |
| Style 4 loss: 139662.460327 | |
| Style 5 loss: 17271.901131 | |
| Total loss: 5469443.487756 | |
| --- | |
| x1 value: -8.021800994873 | |
| feval(x) grad value: -0.014382942579687 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105803048 | |
| StyleLoss:updateOutput self.G 2: 6.7975845336914 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2414545835782e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0023092478513718 | |
| dG 1: -7.4952572504117e-06 | |
| dG 2: -4.8155170172606e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927119872 | |
| StyleLoss:updateOutput self.G 2: 247.10520935059 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.5787925147306e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00081925734411925 | |
| dG 1: 5.5806154705351e-05 | |
| dG 2: 7.1557508182774e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847650880 | |
| StyleLoss:updateOutput self.G 2: 217.38031005859 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1959343143862e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012878822162747 | |
| dG 1: 3.6380897654453e-05 | |
| dG 2: 9.3298910264217e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94655096 | |
| StyleLoss:updateOutput self.G 2: 48.144073486328 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.3329931170556e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019718443509191 | |
| dG 1: 3.5375151128392e-06 | |
| dG 2: 1.7992732954594e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7095696.5 | |
| StyleLoss:updateOutput self.G 2: 14.436233520508 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4153306437947e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084919836372137 | |
| dG 1: 1.905890371745e-06 | |
| dG 2: 3.8775441191241e-12 | |
| --- | |
| Iteration 149 / 2500 | |
| Content 1 loss: 5167819.531250 | |
| Style 1 loss: 58.126889 | |
| Style 2 loss: 27585.210800 | |
| Style 3 loss: 197635.162354 | |
| Style 4 loss: 137287.319183 | |
| Style 5 loss: 17161.213875 | |
| Total loss: 5547546.564351 | |
| --- | |
| x1 value: -8.0222215652466 | |
| feval(x) grad value: 0.0076340157538652 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105618880 | |
| StyleLoss:updateOutput self.G 2: 6.7857527732849 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9806447088276e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00084528280422091 | |
| dG 1: -1.3272530850372e-05 | |
| dG 2: -8.527272312743e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923584256 | |
| StyleLoss:updateOutput self.G 2: 246.65184020996 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.4027654289589e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0001204807049362 | |
| dG 1: 4.6156975486156e-07 | |
| dG 2: 5.9184861872508e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841388608 | |
| StyleLoss:updateOutput self.G 2: 215.77429199219 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1934962174109e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00072078674566001 | |
| dG 1: -1.2630036508199e-05 | |
| dG 2: -3.2389763614249e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93560424 | |
| StyleLoss:updateOutput self.G 2: 47.587299346924 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2593055487287e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00083097215974703 | |
| dG 1: -7.1036515691958e-07 | |
| dG 2: -3.6131048240101e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6929074 | |
| StyleLoss:updateOutput self.G 2: 14.097239494324 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.9561798454743e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0041737081483006 | |
| dG 1: -6.8042970724491e-07 | |
| dG 2: -1.3843378483466e-12 | |
| --- | |
| Iteration 150 / 2500 | |
| Content 1 loss: 5109269.531250 | |
| Style 1 loss: 67.358941 | |
| Style 2 loss: 25172.069550 | |
| Style 3 loss: 186950.477600 | |
| Style 4 loss: 135845.649719 | |
| Style 5 loss: 16629.529953 | |
| Total loss: 5473934.617013 | |
| --- | |
| x1 value: -8.0231065750122 | |
| feval(x) grad value: -0.0039887432940304 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105672248 | |
| StyleLoss:updateOutput self.G 2: 6.7891812324524 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5959193905578e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00081932061584666 | |
| dG 1: -1.1598164746829e-05 | |
| dG 2: -7.451533964517e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924108928 | |
| StyleLoss:updateOutput self.G 2: 246.71908569336 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9408202095738e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00021646487584803 | |
| dG 1: 8.67102517077e-06 | |
| dG 2: 1.1118433647719e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844889536 | |
| StyleLoss:updateOutput self.G 2: 216.67211914062 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3157101363959e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00081157236127183 | |
| dG 1: 1.4768913388252e-05 | |
| dG 2: 3.7874920214009e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94036680 | |
| StyleLoss:updateOutput self.G 2: 47.829524993896 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1252628695502e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00068413239205256 | |
| dG 1: 1.13772387067e-06 | |
| dG 2: 5.7867615864057e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7008615 | |
| StyleLoss:updateOutput self.G 2: 14.259066581726 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.0416400654285e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0030249839182943 | |
| dG 1: 5.5419661748601e-07 | |
| dG 2: 1.1275159408561e-12 | |
| --- | |
| Iteration 151 / 2500 | |
| Content 1 loss: 5137281.250000 | |
| Style 1 loss: 62.048882 | |
| Style 2 loss: 26069.993019 | |
| Style 3 loss: 190807.559967 | |
| Style 4 loss: 134388.496399 | |
| Style 5 loss: 16309.318542 | |
| Total loss: 5504918.666810 | |
| --- | |
| x1 value: -8.0252923965454 | |
| feval(x) grad value: 0.0051414235495031 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105497024 | |
| StyleLoss:updateOutput self.G 2: 6.7779231071472 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7689655840722e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00059488700935617 | |
| dG 1: -1.7095657312893e-05 | |
| dG 2: -1.0983537213419e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920898304 | |
| StyleLoss:updateOutput self.G 2: 246.30741882324 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.6201743581805e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00050096155609936 | |
| dG 1: -4.1577404772397e-05 | |
| dG 2: -5.3312679791639e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842905088 | |
| StyleLoss:updateOutput self.G 2: 216.16320800781 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.0835579423183e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00017116058734246 | |
| dG 1: -7.6149365213496e-07 | |
| dG 2: -1.9528522137054e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93905216 | |
| StyleLoss:updateOutput self.G 2: 47.762672424316 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.2539460903536e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012243307719473 | |
| dG 1: 6.2763513142272e-07 | |
| dG 2: 3.1923175583017e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6977252.5 | |
| StyleLoss:updateOutput self.G 2: 14.195256233215 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.8576631606068e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.9146063752705e-05 | |
| dG 1: 6.7382778468073e-08 | |
| dG 2: 1.370906291048e-13 | |
| --- | |
| Iteration 152 / 2500 | |
| Content 1 loss: 5130255.859375 | |
| Style 1 loss: 78.021213 | |
| Style 2 loss: 26112.733841 | |
| Style 3 loss: 184118.282318 | |
| Style 4 loss: 133923.763275 | |
| Style 5 loss: 16356.263638 | |
| Total loss: 5490844.923660 | |
| --- | |
| x1 value: -8.0257368087769 | |
| feval(x) grad value: -0.0036532622762024 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105726024 | |
| StyleLoss:updateOutput self.G 2: 6.7926359176636 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1170434294504e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00045520361163653 | |
| dG 1: -9.9115231932956e-06 | |
| dG 2: -6.3679085875873e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925248896 | |
| StyleLoss:updateOutput self.G 2: 246.86529541016 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.4047263492739e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00050568615552038 | |
| dG 1: 2.6520225219429e-05 | |
| dG 2: 3.4005589143182e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843712832 | |
| StyleLoss:updateOutput self.G 2: 216.37036132812 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.6569697310115e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00043821745202877 | |
| dG 1: 5.5606446949241e-06 | |
| dG 2: 1.4260291081661e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93939824 | |
| StyleLoss:updateOutput self.G 2: 47.7802734375 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.2441621651033e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00038711485103704 | |
| dG 1: 7.6192378628548e-07 | |
| dG 2: 3.8753445981768e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7020166.5 | |
| StyleLoss:updateOutput self.G 2: 14.282567024231 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.4855277059905e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0038913164753467 | |
| dG 1: 7.3350122420379e-07 | |
| dG 2: 1.4923122416619e-12 | |
| --- | |
| Iteration 153 / 2500 | |
| Content 1 loss: 5136690.234375 | |
| Style 1 loss: 58.871610 | |
| Style 2 loss: 25558.605194 | |
| Style 3 loss: 179793.766022 | |
| Style 4 loss: 133471.046448 | |
| Style 5 loss: 16288.262844 | |
| Total loss: 5491860.786493 | |
| --- | |
| x1 value: -8.0271158218384 | |
| feval(x) grad value: 0.0035581055562943 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105641088 | |
| StyleLoss:updateOutput self.G 2: 6.7871780395508 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.8361538895183e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0003245931584388 | |
| dG 1: -1.2576317203639e-05 | |
| dG 2: -8.0799744065635e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923724544 | |
| StyleLoss:updateOutput self.G 2: 246.66983032227 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3218417471705e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00020792102441192 | |
| dG 1: 2.656020114955e-06 | |
| dG 2: 3.4056852932403e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843129856 | |
| StyleLoss:updateOutput self.G 2: 216.2208404541 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6590179896525e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 6.5671316406224e-05 | |
| dG 1: 9.982777555706e-07 | |
| dG 2: 2.5600848481064e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93727632 | |
| StyleLoss:updateOutput self.G 2: 47.672348022461 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0897074171653e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00033182103652507 | |
| dG 1: -6.1526257866262e-08 | |
| dG 2: -3.1293899898721e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6912718 | |
| StyleLoss:updateOutput self.G 2: 14.063961982727 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.858784781296e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0053152707405388 | |
| dG 1: -9.3431270897781e-07 | |
| dG 2: -1.9008640148033e-12 | |
| --- | |
| Iteration 154 / 2500 | |
| Content 1 loss: 5112276.562500 | |
| Style 1 loss: 65.597406 | |
| Style 2 loss: 24123.767853 | |
| Style 3 loss: 175804.161072 | |
| Style 4 loss: 132640.720367 | |
| Style 5 loss: 16613.749981 | |
| Total loss: 5461524.559179 | |
| --- | |
| x1 value: -8.0286722183228 | |
| feval(x) grad value: -0.0077784927561879 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105639312 | |
| StyleLoss:updateOutput self.G 2: 6.7870655059814 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9127322775935e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00071165343979374 | |
| dG 1: -1.2631832760235e-05 | |
| dG 2: -8.1156403212296e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923621632 | |
| StyleLoss:updateOutput self.G 2: 246.65660095215 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0050237975179e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00045402967953123 | |
| dG 1: 1.0446572105138e-06 | |
| dG 2: 1.3395136237195e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845485632 | |
| StyleLoss:updateOutput self.G 2: 216.82498168945 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4267945402935e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00087975146016106 | |
| dG 1: 1.9435476133367e-05 | |
| dG 2: 4.9842317773052e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94423552 | |
| StyleLoss:updateOutput self.G 2: 48.026302337646 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5233850919903e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014940889086574 | |
| dG 1: 2.6389820959594e-06 | |
| dG 2: 1.3422556252238e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7103157 | |
| StyleLoss:updateOutput self.G 2: 14.451411247253 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4227366591513e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0085364188998938 | |
| dG 1: 2.0216712073307e-06 | |
| dG 2: 4.1131013525653e-12 | |
| --- | |
| Iteration 155 / 2500 | |
| Content 1 loss: 5172491.406250 | |
| Style 1 loss: 65.075504 | |
| Style 2 loss: 23546.583652 | |
| Style 3 loss: 185984.230042 | |
| Style 4 loss: 136143.424988 | |
| Style 5 loss: 17349.050045 | |
| Total loss: 5535579.770480 | |
| --- | |
| x1 value: -8.029239654541 | |
| feval(x) grad value: 0.014915864914656 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105456608 | |
| StyleLoss:updateOutput self.G 2: 6.7753262519836 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.044315105034e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020447121933103 | |
| dG 1: -1.8363498384133e-05 | |
| dG 2: -1.1798091800394e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919521280 | |
| StyleLoss:updateOutput self.G 2: 246.13084411621 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0038782960464e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0008999141282402 | |
| dG 1: -6.3135812524706e-05 | |
| dG 2: -8.0955979767094e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 838408256 | |
| StyleLoss:updateOutput self.G 2: 215.00997924805 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0557300217661e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011742225615308 | |
| dG 1: -3.595480666263e-05 | |
| dG 2: -9.2206181925847e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93121160 | |
| StyleLoss:updateOutput self.G 2: 47.363883972168 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5753087129488e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0015738508664072 | |
| dG 1: -2.414861228317e-06 | |
| dG 2: -1.2282619582873e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6882860 | |
| StyleLoss:updateOutput self.G 2: 14.003213882446 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1935958355025e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.007161577232182 | |
| dG 1: -1.3977831940792e-06 | |
| dG 2: -2.8437968126183e-12 | |
| --- | |
| Iteration 156 / 2500 | |
| Content 1 loss: 5084193.359375 | |
| Style 1 loss: 76.575978 | |
| Style 2 loss: 26480.506897 | |
| Style 3 loss: 200310.356140 | |
| Style 4 loss: 139120.010376 | |
| Style 5 loss: 16969.509602 | |
| Total loss: 5467150.318367 | |
| --- | |
| x1 value: -8.031192779541 | |
| feval(x) grad value: -0.012572881765664 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105870008 | |
| StyleLoss:updateOutput self.G 2: 6.8018860816956 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.427077454101e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0023029590956867 | |
| dG 1: -5.3944459068589e-06 | |
| dG 2: -3.4657980695421e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928171648 | |
| StyleLoss:updateOutput self.G 2: 247.2400970459 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.072738058383e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075144559377804 | |
| dG 1: 7.226785965031e-05 | |
| dG 2: 9.2665545375903e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846741760 | |
| StyleLoss:updateOutput self.G 2: 217.14710998535 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1539348438182e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012191351270303 | |
| dG 1: 2.9265769626363e-05 | |
| dG 2: 7.5052134645981e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94539800 | |
| StyleLoss:updateOutput self.G 2: 48.085426330566 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0535218797922e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001811636146158 | |
| dG 1: 3.0901117042959e-06 | |
| dG 2: 1.5717124867212e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7077608.5 | |
| StyleLoss:updateOutput self.G 2: 14.399430274963 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2964271718374e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0077785616740584 | |
| dG 1: 1.6250984344879e-06 | |
| dG 2: 3.3062710555476e-12 | |
| --- | |
| Iteration 157 / 2500 | |
| Content 1 loss: 5166720.703125 | |
| Style 1 loss: 54.908093 | |
| Style 2 loss: 28477.480888 | |
| Style 3 loss: 181206.344604 | |
| Style 4 loss: 131724.861145 | |
| Style 5 loss: 16597.610950 | |
| Total loss: 5524781.908806 | |
| --- | |
| x1 value: -8.0314121246338 | |
| feval(x) grad value: 0.0027826519217342 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105721512 | |
| StyleLoss:updateOutput self.G 2: 6.7923469543457 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.1848752140841e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00039693582220934 | |
| dG 1: -1.0052797733806e-05 | |
| dG 2: -6.4586747408613e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924877184 | |
| StyleLoss:updateOutput self.G 2: 246.81762695312 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.4736229770924e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00030190544202924 | |
| dG 1: 2.0697256331914e-05 | |
| dG 2: 2.6539081262461e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843066752 | |
| StyleLoss:updateOutput self.G 2: 216.2046661377 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5876566727968e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 7.0631023845635e-05 | |
| dG 1: 5.0445908073016e-07 | |
| dG 2: 1.2936864307676e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93647032 | |
| StyleLoss:updateOutput self.G 2: 47.631343841553 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.9542666614761e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0005987350596115 | |
| dG 1: -3.742725596112e-07 | |
| dG 2: -1.9036493572894e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6937769.5 | |
| StyleLoss:updateOutput self.G 2: 14.114931106567 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.974367240924e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0035846198443323 | |
| dG 1: -5.4545819239138e-07 | |
| dG 2: -1.1097374104721e-12 | |
| --- | |
| Iteration 158 / 2500 | |
| Content 1 loss: 5122526.171875 | |
| Style 1 loss: 59.227332 | |
| Style 2 loss: 24003.499031 | |
| Style 3 loss: 170037.197113 | |
| Style 4 loss: 130159.366608 | |
| Style 5 loss: 16187.677860 | |
| Total loss: 5462973.139820 | |
| --- | |
| x1 value: -8.0325975418091 | |
| feval(x) grad value: 0.0036463199649006 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105509624 | |
| StyleLoss:updateOutput self.G 2: 6.7787322998047 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.7397015262286e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00012021051952615 | |
| dG 1: -1.6700203559594e-05 | |
| dG 2: -1.0729467444723e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920763136 | |
| StyleLoss:updateOutput self.G 2: 246.29008483887 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.2644866949977e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00053631310584024 | |
| dG 1: -4.3695625208784e-05 | |
| dG 2: -5.6028771032446e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841979200 | |
| StyleLoss:updateOutput self.G 2: 215.92576599121 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.7217927345855e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00031093932921067 | |
| dG 1: -8.0067611634149e-06 | |
| dG 2: -2.0533362336816e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93969744 | |
| StyleLoss:updateOutput self.G 2: 47.795486450195 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.063839647486e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00061141327023506 | |
| dG 1: 8.7795422132331e-07 | |
| dG 2: 4.465506469372e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6993176 | |
| StyleLoss:updateOutput self.G 2: 14.227655410767 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.2447536568725e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019468519603834 | |
| dG 1: 3.1455795124202e-07 | |
| dG 2: 6.3996984794867e-13 | |
| --- | |
| Iteration 159 / 2500 | |
| Content 1 loss: 5124687.109375 | |
| Style 1 loss: 73.692502 | |
| Style 2 loss: 24798.394203 | |
| Style 3 loss: 184148.414612 | |
| Style 4 loss: 134870.727539 | |
| Style 5 loss: 16267.480373 | |
| Total loss: 5484845.818605 | |
| --- | |
| x1 value: -8.0337753295898 | |
| feval(x) grad value: -0.00079415290383622 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105642088 | |
| StyleLoss:updateOutput self.G 2: 6.7872428894043 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9517185140548e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00060593895614147 | |
| dG 1: -1.2544682249427e-05 | |
| dG 2: -8.0596477842337e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923619328 | |
| StyleLoss:updateOutput self.G 2: 246.65629577637 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1748538142342e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00021496292902157 | |
| dG 1: 1.0087819646287e-06 | |
| dG 2: 1.2935128228947e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844053312 | |
| StyleLoss:updateOutput self.G 2: 216.45768737793 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.3862086468162e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00024891478824429 | |
| dG 1: 8.2251199273742e-06 | |
| dG 2: 2.1093344085288e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93929824 | |
| StyleLoss:updateOutput self.G 2: 47.775173187256 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0055734850644e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.0229048510082e-05 | |
| dG 1: 7.2303856768485e-07 | |
| dG 2: 3.6775641668214e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7029131 | |
| StyleLoss:updateOutput self.G 2: 14.300805091858 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.3263240690503e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0037957939784974 | |
| dG 1: 8.7264692183453e-07 | |
| dG 2: 1.7754048014726e-12 | |
| --- | |
| Iteration 160 / 2500 | |
| Content 1 loss: 5156405.859375 | |
| Style 1 loss: 61.159004 | |
| Style 2 loss: 24047.884941 | |
| Style 3 loss: 196448.776245 | |
| Style 4 loss: 139647.502899 | |
| Style 5 loss: 16807.913303 | |
| Total loss: 5533419.095767 | |
| --- | |
| x1 value: -8.0346403121948 | |
| feval(x) grad value: -0.00043025644845329 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105790120 | |
| StyleLoss:updateOutput self.G 2: 6.796754360199 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4583558360926e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001336915534921 | |
| dG 1: -7.9005149018485e-06 | |
| dG 2: -5.0758860005787e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926098944 | |
| StyleLoss:updateOutput self.G 2: 246.97427368164 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.7188822444896e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002643609768711 | |
| dG 1: 3.9822160033509e-05 | |
| dG 2: 5.1062019196246e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844057472 | |
| StyleLoss:updateOutput self.G 2: 216.45872497559 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.3586563448534e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0004974520415999 | |
| dG 1: 8.2569995356607e-06 | |
| dG 2: 2.1175099434306e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93725272 | |
| StyleLoss:updateOutput self.G 2: 47.671138763428 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.5133496112394e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00025081986677833 | |
| dG 1: -7.0674900598533e-08 | |
| dG 2: -3.5947116052044e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6923559 | |
| StyleLoss:updateOutput self.G 2: 14.08602142334 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.4422774787308e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0044653667137027 | |
| dG 1: -7.6602941589954e-07 | |
| dG 2: -1.5584912917491e-12 | |
| --- | |
| Iteration 161 / 2500 | |
| Content 1 loss: 5111326.171875 | |
| Style 1 loss: 59.510048 | |
| Style 2 loss: 26048.795700 | |
| Style 3 loss: 183254.024506 | |
| Style 4 loss: 134353.626251 | |
| Style 5 loss: 16404.852390 | |
| Total loss: 5471446.980771 | |
| --- | |
| x1 value: -8.0366621017456 | |
| feval(x) grad value: -0.0052526709623635 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105662552 | |
| StyleLoss:updateOutput self.G 2: 6.7885580062866 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7139362757111e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00039518211269751 | |
| dG 1: -1.1902619917237e-05 | |
| dG 2: -7.647140299466e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923827968 | |
| StyleLoss:updateOutput self.G 2: 246.68310546875 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0406378808957e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00022548543347511 | |
| dG 1: 4.2776955524459e-06 | |
| dG 2: 5.4850807056084e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843749312 | |
| StyleLoss:updateOutput self.G 2: 216.37973022461 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.3493558855043e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00035523332189769 | |
| dG 1: 5.8456275837671e-06 | |
| dG 2: 1.4991125745281e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94285440 | |
| StyleLoss:updateOutput self.G 2: 47.956066131592 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2055441206703e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013060681521893 | |
| dG 1: 2.1031023607065e-06 | |
| dG 2: 1.0696932705931e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7052184 | |
| StyleLoss:updateOutput self.G 2: 14.347705841064 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0769657592391e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.006461794488132 | |
| dG 1: 1.2304709571254e-06 | |
| dG 2: 2.5034002648638e-12 | |
| --- | |
| Iteration 162 / 2500 | |
| Content 1 loss: 5153758.984375 | |
| Style 1 loss: 61.480327 | |
| Style 2 loss: 23124.854565 | |
| Style 3 loss: 170353.431702 | |
| Style 4 loss: 130503.879547 | |
| Style 5 loss: 16237.611294 | |
| Total loss: 5494040.241809 | |
| --- | |
| x1 value: -8.0373096466064 | |
| feval(x) grad value: 0.013876117765903 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105423136 | |
| StyleLoss:updateOutput self.G 2: 6.7731757164001 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.2665369903716e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020691431127489 | |
| dG 1: -1.9413360860199e-05 | |
| dG 2: -1.2472603245356e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1918835712 | |
| StyleLoss:updateOutput self.G 2: 246.04290771484 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0828998853185e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00071775366086513 | |
| dG 1: -7.3867420724127e-05 | |
| dG 2: -9.4716613024959e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840730624 | |
| StyleLoss:updateOutput self.G 2: 215.60556030273 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6431236815606e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00089867215137929 | |
| dG 1: -1.7778789697331e-05 | |
| dG 2: -4.5593745792716e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93384512 | |
| StyleLoss:updateOutput self.G 2: 47.497817993164 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.0208268836086e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0012989420210943 | |
| dG 1: -1.3929862916484e-06 | |
| dG 2: -7.0850936879571e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6924498 | |
| StyleLoss:updateOutput self.G 2: 14.087929725647 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.7871271741969e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0046722767874599 | |
| dG 1: -7.5147352163185e-07 | |
| dG 2: -1.5288765262481e-12 | |
| --- | |
| Iteration 163 / 2500 | |
| Content 1 loss: 5115763.671875 | |
| Style 1 loss: 79.203358 | |
| Style 2 loss: 26506.854057 | |
| Style 3 loss: 181073.787689 | |
| Style 4 loss: 132455.154419 | |
| Style 5 loss: 16362.569332 | |
| Total loss: 5472241.240731 | |
| --- | |
| x1 value: -8.0387868881226 | |
| feval(x) grad value: -0.012982498854399 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105849824 | |
| StyleLoss:updateOutput self.G 2: 6.8005895614624 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.3436040299034e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0027295954059809 | |
| dG 1: -6.0280981415417e-06 | |
| dG 2: -3.872903788036e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927256704 | |
| StyleLoss:updateOutput self.G 2: 247.12272644043 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.9059505487276e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00076845195144415 | |
| dG 1: 5.7943925639847e-05 | |
| dG 2: 7.4298674851425e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847590272 | |
| StyleLoss:updateOutput self.G 2: 217.36479187012 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1332961352982e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001218656427227 | |
| dG 1: 3.590765845729e-05 | |
| dG 2: 9.2085263025954e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94572120 | |
| StyleLoss:updateOutput self.G 2: 48.101879119873 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.1089430763132e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018571735126898 | |
| dG 1: 3.2156212910195e-06 | |
| dG 2: 1.635549226435e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7079590 | |
| StyleLoss:updateOutput self.G 2: 14.403460502625 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3279321819937e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0079675940796733 | |
| dG 1: 1.6558541346967e-06 | |
| dG 2: 3.3688442660496e-12 | |
| --- | |
| Iteration 164 / 2500 | |
| Content 1 loss: 5162236.718750 | |
| Style 1 loss: 55.075893 | |
| Style 2 loss: 26913.614273 | |
| Style 3 loss: 199955.841064 | |
| Style 4 loss: 134917.968750 | |
| Style 5 loss: 16638.985634 | |
| Total loss: 5540718.204365 | |
| --- | |
| x1 value: -8.0395460128784 | |
| feval(x) grad value: 0.0066244346089661 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105678688 | |
| StyleLoss:updateOutput self.G 2: 6.7895951271057 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.5982577867012e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001425324822776 | |
| dG 1: -1.1396445188439e-05 | |
| dG 2: -7.3219343999301e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923894528 | |
| StyleLoss:updateOutput self.G 2: 246.69158935547 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1716349607459e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00014418398495764 | |
| dG 1: 5.3154776651354e-06 | |
| dG 2: 6.8157778683117e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841628096 | |
| StyleLoss:updateOutput self.G 2: 215.83569335938 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.05409895923e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00057843938702717 | |
| dG 1: -1.0755085895653e-05 | |
| dG 2: -2.7581446241515e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93448816 | |
| StyleLoss:updateOutput self.G 2: 47.530521392822 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6934363600285e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0010749521898106 | |
| dG 1: -1.143444706031e-06 | |
| dG 2: -5.8158610306142e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6889730 | |
| StyleLoss:updateOutput self.G 2: 14.017191886902 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1618883490883e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0069713294506073 | |
| dG 1: -1.291152216254e-06 | |
| dG 2: -2.6268564146809e-12 | |
| --- | |
| Iteration 165 / 2500 | |
| Content 1 loss: 5107476.171875 | |
| Style 1 loss: 58.388712 | |
| Style 2 loss: 23658.092022 | |
| Style 3 loss: 174042.446136 | |
| Style 4 loss: 132262.561798 | |
| Style 5 loss: 16538.463593 | |
| Total loss: 5454036.124136 | |
| --- | |
| x1 value: -8.0409574508667 | |
| feval(x) grad value: -0.0044155577197671 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105635712 | |
| StyleLoss:updateOutput self.G 2: 6.7868332862854 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9579259930301e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013023373903707 | |
| dG 1: -1.2744830200973e-05 | |
| dG 2: -8.1882390408003e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923300480 | |
| StyleLoss:updateOutput self.G 2: 246.61541748047 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.6195061937103e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00022607583377976 | |
| dG 1: -3.9793617361283e-06 | |
| dG 2: -5.1025427372925e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845654912 | |
| StyleLoss:updateOutput self.G 2: 216.86846923828 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4500479039725e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00084795377915725 | |
| dG 1: 2.0760262486874e-05 | |
| dG 2: 5.3239730332666e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94324248 | |
| StyleLoss:updateOutput self.G 2: 47.975799560547 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2301550473003e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012975302524865 | |
| dG 1: 2.2536796677741e-06 | |
| dG 2: 1.1462805531159e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7058981.5 | |
| StyleLoss:updateOutput self.G 2: 14.36153793335 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1264884278717e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0067589296959341 | |
| dG 1: 1.3359937156565e-06 | |
| dG 2: 2.7180859559633e-12 | |
| --- | |
| Iteration 166 / 2500 | |
| Content 1 loss: 5164145.312500 | |
| Style 1 loss: 67.183916 | |
| Style 2 loss: 24655.325890 | |
| Style 3 loss: 186349.983215 | |
| Style 4 loss: 130941.066742 | |
| Style 5 loss: 16247.581959 | |
| Total loss: 5522406.454221 | |
| --- | |
| x1 value: -8.0412292480469 | |
| feval(x) grad value: 0.0088677695021033 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105539608 | |
| StyleLoss:updateOutput self.G 2: 6.7806587219238 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6752588528088e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011455750791356 | |
| dG 1: -1.5759638699819e-05 | |
| dG 2: -1.0125178690271e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920669184 | |
| StyleLoss:updateOutput self.G 2: 246.27807617188 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.6779876678756e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00069876422639936 | |
| dG 1: -4.516542685451e-05 | |
| dG 2: -5.791343099526e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841521344 | |
| StyleLoss:updateOutput self.G 2: 215.80838012695 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.0270687874417e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00042881033732556 | |
| dG 1: -1.1589786481636e-05 | |
| dG 2: -2.9722042969166e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93502408 | |
| StyleLoss:updateOutput self.G 2: 47.557788848877 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3193461256833e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00077627104474232 | |
| dG 1: -9.3548982249558e-07 | |
| dG 2: -4.7581475435057e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6930905 | |
| StyleLoss:updateOutput self.G 2: 14.100961685181 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.9642970856876e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0041785780340433 | |
| dG 1: -6.5202692667299e-07 | |
| dG 2: -1.3265522577979e-12 | |
| --- | |
| Iteration 167 / 2500 | |
| Content 1 loss: 5109621.093750 | |
| Style 1 loss: 66.988913 | |
| Style 2 loss: 25217.516899 | |
| Style 3 loss: 184602.687836 | |
| Style 4 loss: 133384.426117 | |
| Style 5 loss: 16147.112846 | |
| Total loss: 5469039.826361 | |
| --- | |
| x1 value: -8.0427532196045 | |
| feval(x) grad value: -0.0095124011859298 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105787744 | |
| StyleLoss:updateOutput self.G 2: 6.7966017723083 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5572894085381e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012145722284913 | |
| dG 1: -7.9750816439628e-06 | |
| dG 2: -5.1237936419743e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926170624 | |
| StyleLoss:updateOutput self.G 2: 246.98347473145 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.443095111943e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00073487911140546 | |
| dG 1: 4.0943694330053e-05 | |
| dG 2: 5.2500100621022e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845458432 | |
| StyleLoss:updateOutput self.G 2: 216.81802368164 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5700956623732e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00093842041678727 | |
| dG 1: 1.9221935872338e-05 | |
| dG 2: 4.9294717613391e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94330888 | |
| StyleLoss:updateOutput self.G 2: 47.979179382324 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2413028943902e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0013041832717136 | |
| dG 1: 2.2794858978159e-06 | |
| dG 2: 1.1594065551374e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7066592.5 | |
| StyleLoss:updateOutput self.G 2: 14.377018928528 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2088491985196e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0072530945762992 | |
| dG 1: 1.4541398059009e-06 | |
| dG 2: 2.9584550954864e-12 | |
| --- | |
| Iteration 168 / 2500 | |
| Content 1 loss: 5165256.640625 | |
| Style 1 loss: 55.610809 | |
| Style 2 loss: 24286.239624 | |
| Style 3 loss: 174111.156464 | |
| Style 4 loss: 130506.740570 | |
| Style 5 loss: 16365.896702 | |
| Total loss: 5510582.284794 | |
| --- | |
| x1 value: -8.0434627532959 | |
| feval(x) grad value: 0.009735781699419 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105636912 | |
| StyleLoss:updateOutput self.G 2: 6.7869110107422 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9613211438573e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00078038044739515 | |
| dG 1: -1.2706919733319e-05 | |
| dG 2: -8.1638818968943e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922838784 | |
| StyleLoss:updateOutput self.G 2: 246.5562286377 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.086126293932e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00044587871525437 | |
| dG 1: -1.1205524060642e-05 | |
| dG 2: -1.4368300386286e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841173440 | |
| StyleLoss:updateOutput self.G 2: 215.71917724609 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2607725352609e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0007607807056047 | |
| dG 1: -1.4313488463813e-05 | |
| dG 2: -3.6706974265721e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93424312 | |
| StyleLoss:updateOutput self.G 2: 47.518062591553 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6900285970678e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011070722248405 | |
| dG 1: -1.2385490890665e-06 | |
| dG 2: -6.2995859613849e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6885603 | |
| StyleLoss:updateOutput self.G 2: 14.008794784546 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1645456652332e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0069872732274234 | |
| dG 1: -1.3552157724916e-06 | |
| dG 2: -2.7571931283249e-12 | |
| --- | |
| Iteration 169 / 2500 | |
| Content 1 loss: 5101839.843750 | |
| Style 1 loss: 62.492799 | |
| Style 2 loss: 23216.455936 | |
| Style 3 loss: 174925.048828 | |
| Style 4 loss: 131505.615234 | |
| Style 5 loss: 16635.314941 | |
| Total loss: 5448184.771489 | |
| --- | |
| x1 value: -8.0451650619507 | |
| feval(x) grad value: -0.011122759431601 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105786408 | |
| StyleLoss:updateOutput self.G 2: 6.7965159416199 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6458747253978e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015171199338511 | |
| dG 1: -8.0172058005701e-06 | |
| dG 2: -5.1508564124017e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925795840 | |
| StyleLoss:updateOutput self.G 2: 246.93545532227 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.7680495874356e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00070361827965826 | |
| dG 1: 3.5078435757896e-05 | |
| dG 2: 4.4979367452846e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847096320 | |
| StyleLoss:updateOutput self.G 2: 217.23808288574 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8179223104653e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011062072589993 | |
| dG 1: 3.2040665246313e-05 | |
| dG 2: 8.2168351983603e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94448768 | |
| StyleLoss:updateOutput self.G 2: 48.039123535156 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.6016857646027e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001573700690642 | |
| dG 1: 2.736857140917e-06 | |
| dG 2: 1.3920374184948e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7093479 | |
| StyleLoss:updateOutput self.G 2: 14.431719779968 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3710564417124e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0082263397052884 | |
| dG 1: 1.8714514453677e-06 | |
| dG 2: 3.8074777705677e-12 | |
| --- | |
| Iteration 170 / 2500 | |
| Content 1 loss: 5174293.750000 | |
| Style 1 loss: 52.196022 | |
| Style 2 loss: 27421.568871 | |
| Style 3 loss: 207593.261719 | |
| Style 4 loss: 133893.310547 | |
| Style 5 loss: 16771.682739 | |
| Total loss: 5560025.769898 | |
| --- | |
| x1 value: -8.0461397171021 | |
| feval(x) grad value: 0.012423357926309 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105507232 | |
| StyleLoss:updateOutput self.G 2: 6.7785792350769 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8938399171684e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0018043763702735 | |
| dG 1: -1.6775122276158e-05 | |
| dG 2: -1.077760060017e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920351232 | |
| StyleLoss:updateOutput self.G 2: 246.23727416992 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.3205834161836e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00063807429978624 | |
| dG 1: -5.014477937948e-05 | |
| dG 2: -6.4298210852853e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841300544 | |
| StyleLoss:updateOutput self.G 2: 215.75178527832 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5495038496738e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00085009157191962 | |
| dG 1: -1.3316893273441e-05 | |
| dG 2: -3.4151206168564e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93478072 | |
| StyleLoss:updateOutput self.G 2: 47.545406341553 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7202576430009e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001073801657185 | |
| dG 1: -1.0299183941243e-06 | |
| dG 2: -5.2384355533897e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6934027.5 | |
| StyleLoss:updateOutput self.G 2: 14.107314109802 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.6655127284321e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0039993063546717 | |
| dG 1: -6.0356137510098e-07 | |
| dG 2: -1.2279487322797e-12 | |
| --- | |
| Iteration 171 / 2500 | |
| Content 1 loss: 5117563.671875 | |
| Style 1 loss: 69.707170 | |
| Style 2 loss: 23686.397552 | |
| Style 3 loss: 166458.286285 | |
| Style 4 loss: 127934.349060 | |
| Style 5 loss: 15942.615509 | |
| Total loss: 5451655.027452 | |
| --- | |
| x1 value: -8.0476179122925 | |
| feval(x) grad value: -0.009196063503623 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105822504 | |
| StyleLoss:updateOutput self.G 2: 6.7988343238831 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1504035946075e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0021000495180488 | |
| dG 1: -6.8849244598823e-06 | |
| dG 2: -4.4233938885883e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926793344 | |
| StyleLoss:updateOutput self.G 2: 247.06330871582 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.8325919023191e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00048582640010864 | |
| dG 1: 5.0690538046183e-05 | |
| dG 2: 6.4998011313488e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845657152 | |
| StyleLoss:updateOutput self.G 2: 216.8690032959 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6607819475212e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00087030360009521 | |
| dG 1: 2.0778283214895e-05 | |
| dG 2: 5.3285965050109e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94379800 | |
| StyleLoss:updateOutput self.G 2: 48.004047393799 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.543872028582e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0014669492375106 | |
| dG 1: 2.4691764792806e-06 | |
| dG 2: 1.2558881885838e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7027010.5 | |
| StyleLoss:updateOutput self.G 2: 14.29649066925 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.0304403127229e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0048182639293373 | |
| dG 1: 8.397317969866e-07 | |
| dG 2: 1.708439054289e-12 | |
| --- | |
| Iteration 172 / 2500 | |
| Content 1 loss: 5153938.281250 | |
| Style 1 loss: 56.270761 | |
| Style 2 loss: 25894.775391 | |
| Style 3 loss: 175722.393036 | |
| Style 4 loss: 128480.197906 | |
| Style 5 loss: 15863.378048 | |
| Total loss: 5499955.296392 | |
| --- | |
| x1 value: -8.0477437973022 | |
| feval(x) grad value: 0.0037776480894536 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105711016 | |
| StyleLoss:updateOutput self.G 2: 6.7916717529297 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.3972090090751e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001112069003284 | |
| dG 1: -1.0382426808064e-05 | |
| dG 2: -6.6704524933139e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1924028544 | |
| StyleLoss:updateOutput self.G 2: 246.70875549316 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.574033786118e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 9.3158530944493e-05 | |
| dG 1: 7.4136951297987e-06 | |
| dG 2: 9.5062206804247e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842804160 | |
| StyleLoss:updateOutput self.G 2: 216.13734436035 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8556718828222e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.5714152798173e-05 | |
| dG 1: -1.5506325325987e-06 | |
| dG 2: -3.9766009495706e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93566208 | |
| StyleLoss:updateOutput self.G 2: 47.590236663818 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3115094077421e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00080255535431206 | |
| dG 1: -6.87886824835e-07 | |
| dG 2: -3.4987740786183e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6942139.5 | |
| StyleLoss:updateOutput self.G 2: 14.123818397522 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.8207359643347e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0034924412611872 | |
| dG 1: -4.776336481882e-07 | |
| dG 2: -9.7174839789466e-13 | |
| --- | |
| Iteration 173 / 2500 | |
| Content 1 loss: 5127980.468750 | |
| Style 1 loss: 54.633508 | |
| Style 2 loss: 25239.326477 | |
| Style 3 loss: 182296.749115 | |
| Style 4 loss: 129977.050781 | |
| Style 5 loss: 15960.813046 | |
| Total loss: 5481509.041676 | |
| --- | |
| x1 value: -8.0495824813843 | |
| feval(x) grad value: -0.0013294892851263 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105591536 | |
| StyleLoss:updateOutput self.G 2: 6.7839956283569 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.2750865130993e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012302962131798 | |
| dG 1: -1.4130491763353e-05 | |
| dG 2: -9.0784920813575e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921985920 | |
| StyleLoss:updateOutput self.G 2: 246.44692993164 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.6902620454148e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00018682281370275 | |
| dG 1: -2.4553344701417e-05 | |
| dG 2: -3.148355491242e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844172608 | |
| StyleLoss:updateOutput self.G 2: 216.48831176758 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.092287373529e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0004346870991867 | |
| dG 1: 9.1583160610753e-06 | |
| dG 2: 2.3486518719446e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94222448 | |
| StyleLoss:updateOutput self.G 2: 47.924026489258 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0137062506365e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011667428771034 | |
| dG 1: 1.8586760006656e-06 | |
| dG 2: 9.4537160218178e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7027315 | |
| StyleLoss:updateOutput self.G 2: 14.29711151123 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.1638870597089e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0048983311280608 | |
| dG 1: 8.4447134440779e-07 | |
| dG 2: 1.7180816231505e-12 | |
| --- | |
| Iteration 174 / 2500 | |
| Content 1 loss: 5147591.015625 | |
| Style 1 loss: 68.900060 | |
| Style 2 loss: 25071.043968 | |
| Style 3 loss: 179823.692322 | |
| Style 4 loss: 129583.637238 | |
| Style 5 loss: 15874.620438 | |
| Total loss: 5498012.909650 | |
| --- | |
| x1 value: -8.0505037307739 | |
| feval(x) grad value: 0.0071779573336244 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105596312 | |
| StyleLoss:updateOutput self.G 2: 6.7843022346497 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.3785862996183e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001843209611252 | |
| dG 1: -1.3980798030389e-05 | |
| dG 2: -8.9823173855461e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922007936 | |
| StyleLoss:updateOutput self.G 2: 246.44970703125 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.9283126912769e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.000377500342438 | |
| dG 1: -2.4214405129896e-05 | |
| dG 2: -3.1048950303175e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841708288 | |
| StyleLoss:updateOutput self.G 2: 215.85626220703 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0658196458735e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00061739521333948 | |
| dG 1: -1.0128079338756e-05 | |
| dG 2: -2.5973481178332e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93642632 | |
| StyleLoss:updateOutput self.G 2: 47.62911605835 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.4771273495553e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00062474294099957 | |
| dG 1: -3.9131592188824e-07 | |
| dG 2: -1.9903353867378e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6959142.5 | |
| StyleLoss:updateOutput self.G 2: 14.158409118652 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9956066782688e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001797363976948 | |
| dG 1: -2.1372262892783e-07 | |
| dG 2: -4.3481985048145e-13 | |
| --- | |
| Iteration 175 / 2500 | |
| Content 1 loss: 5131942.968750 | |
| Style 1 loss: 61.151538 | |
| Style 2 loss: 22826.964855 | |
| Style 3 loss: 171958.534241 | |
| Style 4 loss: 128553.474426 | |
| Style 5 loss: 15794.583321 | |
| Total loss: 5471137.677131 | |
| --- | |
| x1 value: -8.0512666702271 | |
| feval(x) grad value: -0.010227820836008 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105933152 | |
| StyleLoss:updateOutput self.G 2: 6.8059425354004 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5335911429659e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0023988077882677 | |
| dG 1: -3.4140111893066e-06 | |
| dG 2: -2.1934178377672e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928462592 | |
| StyleLoss:updateOutput self.G 2: 247.27735900879 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0988718202043e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075765064684674 | |
| dG 1: 7.6818861998618e-05 | |
| dG 2: 9.8501068412915e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846512064 | |
| StyleLoss:updateOutput self.G 2: 217.08819580078 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.9033339526686e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011414109030738 | |
| dG 1: 2.7467745894683e-05 | |
| dG 2: 7.0441122553e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94191248 | |
| StyleLoss:updateOutput self.G 2: 47.908145904541 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8461017248228e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.001106888288632 | |
| dG 1: 1.7375804191033e-06 | |
| dG 2: 8.8377916096505e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7023445 | |
| StyleLoss:updateOutput self.G 2: 14.289236068726 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.5729496984422e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0045437696389854 | |
| dG 1: 7.84379039942e-07 | |
| dG 2: 1.5958230830532e-12 | |
| --- | |
| Iteration 176 / 2500 | |
| Content 1 loss: 5148085.156250 | |
| Style 1 loss: 50.056284 | |
| Style 2 loss: 28441.308975 | |
| Style 3 loss: 188412.185669 | |
| Style 4 loss: 130222.103119 | |
| Style 5 loss: 15810.350418 | |
| Total loss: 5511021.160715 | |
| --- | |
| x1 value: -8.0524082183838 | |
| feval(x) grad value: 0.0070539931766689 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105686272 | |
| StyleLoss:updateOutput self.G 2: 6.7900824546814 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.602853044209e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00091482594143599 | |
| dG 1: -1.115845861932e-05 | |
| dG 2: -7.1690342464542e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923697536 | |
| StyleLoss:updateOutput self.G 2: 246.66635131836 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1024638091328e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00010296535037924 | |
| dG 1: 2.2350211565936e-06 | |
| dG 2: 2.8658572368521e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842106048 | |
| StyleLoss:updateOutput self.G 2: 215.95829772949 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.109605514368e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00048033404164016 | |
| dG 1: -7.0139763010957e-06 | |
| dG 2: -1.7987357480223e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93571664 | |
| StyleLoss:updateOutput self.G 2: 47.593017578125 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4110641188836e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00092333543580025 | |
| dG 1: -6.667341949651e-07 | |
| dG 2: -3.3911849023349e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6923523.5 | |
| StyleLoss:updateOutput self.G 2: 14.085945129395 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.0491429343965e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0048294868320227 | |
| dG 1: -7.6659898695652e-07 | |
| dG 2: -1.5596497617704e-12 | |
| --- | |
| Iteration 177 / 2500 | |
| Content 1 loss: 5126444.140625 | |
| Style 1 loss: 58.232551 | |
| Style 2 loss: 22597.865582 | |
| Style 3 loss: 167945.640564 | |
| Style 4 loss: 127319.984436 | |
| Style 5 loss: 15961.164951 | |
| Total loss: 5460327.028709 | |
| --- | |
| x1 value: -8.0537071228027 | |
| feval(x) grad value: -0.0072352178394794 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105650960 | |
| StyleLoss:updateOutput self.G 2: 6.7878136634827 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9659378952829e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00085945089813322 | |
| dG 1: -1.2266305930098e-05 | |
| dG 2: -7.8807977938605e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923004672 | |
| StyleLoss:updateOutput self.G 2: 246.57746887207 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0665803351628e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00016810280794743 | |
| dG 1: -8.6128093244042e-06 | |
| dG 2: -1.1043788496548e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845183616 | |
| StyleLoss:updateOutput self.G 2: 216.74751281738 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3843704493866e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00079121009912342 | |
| dG 1: 1.7070397007046e-05 | |
| dG 2: 4.3777078316543e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94502024 | |
| StyleLoss:updateOutput self.G 2: 48.066219329834 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9332807116589e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017548010218889 | |
| dG 1: 2.9435377655318e-06 | |
| dG 2: 1.4971610106176e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7087920 | |
| StyleLoss:updateOutput self.G 2: 14.420413017273 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3816426189805e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0082898568361998 | |
| dG 1: 1.7851741631603e-06 | |
| dG 2: 3.6319465230444e-12 | |
| --- | |
| Iteration 178 / 2500 | |
| Content 1 loss: 5176597.656250 | |
| Style 1 loss: 59.356101 | |
| Style 2 loss: 22899.981022 | |
| Style 3 loss: 178657.276154 | |
| Style 4 loss: 129105.171204 | |
| Style 5 loss: 16314.339638 | |
| Total loss: 5523633.780368 | |
| --- | |
| x1 value: -8.0538558959961 | |
| feval(x) grad value: 0.014771028421819 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105496576 | |
| StyleLoss:updateOutput self.G 2: 6.7778949737549 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9696018916402e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0016011900734156 | |
| dG 1: -1.7109368855017e-05 | |
| dG 2: -1.099234635607e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919666304 | |
| StyleLoss:updateOutput self.G 2: 246.14947509766 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0025174645989e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00085875613149256 | |
| dG 1: -6.0864331317134e-05 | |
| dG 2: -7.8043361703695e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840164928 | |
| StyleLoss:updateOutput self.G 2: 215.46049499512 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7283822728587e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00097915017977357 | |
| dG 1: -2.220670285169e-05 | |
| dG 2: -5.6949154593477e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93248408 | |
| StyleLoss:updateOutput self.G 2: 47.428611755371 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.286630405024e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0014055264182389 | |
| dG 1: -1.9211049675505e-06 | |
| dG 2: -9.7712452278714e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6884979.5 | |
| StyleLoss:updateOutput self.G 2: 14.007526397705 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1976698033322e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0071860188618302 | |
| dG 1: -1.3648726735482e-06 | |
| dG 2: -2.7768408232542e-12 | |
| --- | |
| Iteration 179 / 2500 | |
| Content 1 loss: 5100475.781250 | |
| Style 1 loss: 70.694050 | |
| Style 2 loss: 24971.712112 | |
| Style 3 loss: 175573.093414 | |
| Style 4 loss: 130732.521057 | |
| Style 5 loss: 16279.891491 | |
| Total loss: 5448103.693375 | |
| --- | |
| x1 value: -8.0558423995972 | |
| feval(x) grad value: -0.012855801731348 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105928872 | |
| StyleLoss:updateOutput self.G 2: 6.8056678771973 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.1848871296964e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018521464662626 | |
| dG 1: -3.5481650684233e-06 | |
| dG 2: -2.2796082512974e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928617472 | |
| StyleLoss:updateOutput self.G 2: 247.29721069336 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1067958638478e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00092307658633217 | |
| dG 1: 7.9244389780797e-05 | |
| dG 2: 1.0161121076491e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847497408 | |
| StyleLoss:updateOutput self.G 2: 217.34091186523 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0421761348643e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012058708816767 | |
| dG 1: 3.5179080441594e-05 | |
| dG 2: 9.0216809717214e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94511768 | |
| StyleLoss:updateOutput self.G 2: 48.071186065674 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.6019719712167e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0015311302850023 | |
| dG 1: 2.9813706987625e-06 | |
| dG 2: 1.5164037560356e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7083741 | |
| StyleLoss:updateOutput self.G 2: 14.411908149719 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3178779454392e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0079072657972574 | |
| dG 1: 1.720300474517e-06 | |
| dG 2: 3.4999605210556e-12 | |
| --- | |
| Iteration 180 / 2500 | |
| Content 1 loss: 5182779.687500 | |
| Style 1 loss: 48.460023 | |
| Style 2 loss: 27897.688866 | |
| Style 3 loss: 188252.140045 | |
| Style 4 loss: 130193.309784 | |
| Style 5 loss: 16330.917835 | |
| Total loss: 5545502.204053 | |
| --- | |
| x1 value: -8.0562610626221 | |
| feval(x) grad value: 0.0011292846174911 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105797256 | |
| StyleLoss:updateOutput self.G 2: 6.7972121238708 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.47901753067e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0002453618508298 | |
| dG 1: -7.6770174928242e-06 | |
| dG 2: -4.9322948069558e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925513728 | |
| StyleLoss:updateOutput self.G 2: 246.89924621582 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.2870577128015e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00013661386037711 | |
| dG 1: 3.0661802156828e-05 | |
| dG 2: 3.9316128477851e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842704832 | |
| StyleLoss:updateOutput self.G 2: 216.11187744141 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.784911464943e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -6.8444845965132e-05 | |
| dG 1: -2.3272073121916e-06 | |
| dG 2: -5.9681312626278e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93684344 | |
| StyleLoss:updateOutput self.G 2: 47.650329589844 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.1118883226973e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00047668040497229 | |
| dG 1: -2.2949446076836e-07 | |
| dG 2: -1.1672692706074e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6923457 | |
| StyleLoss:updateOutput self.G 2: 14.085812568665 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.1527917927815e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0048916754312813 | |
| dG 1: -7.6761557465943e-07 | |
| dG 2: -1.5617180942548e-12 | |
| --- | |
| Iteration 181 / 2500 | |
| Content 1 loss: 5120073.828125 | |
| Style 1 loss: 51.603366 | |
| Style 2 loss: 23585.635185 | |
| Style 3 loss: 166495.445251 | |
| Style 4 loss: 126436.077118 | |
| Style 5 loss: 15726.837158 | |
| Total loss: 5452369.426204 | |
| --- | |
| x1 value: -8.0571212768555 | |
| feval(x) grad value: 0.0044347858056426 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105521752 | |
| StyleLoss:updateOutput self.G 2: 6.7795119285583 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8639601740442e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0005210536182858 | |
| dG 1: -1.6319780115737e-05 | |
| dG 2: -1.0485055748979e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920365952 | |
| StyleLoss:updateOutput self.G 2: 246.23918151855 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.0178666312113e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00023538486857433 | |
| dG 1: -4.9914277042262e-05 | |
| dG 2: -6.4002648667016e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843044288 | |
| StyleLoss:updateOutput self.G 2: 216.19888305664 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4470072251527e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 2.7216151465836e-06 | |
| dG 1: 3.2761494139777e-07 | |
| dG 2: 8.401700152651e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93912080 | |
| StyleLoss:updateOutput self.G 2: 47.766174316406 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.4034424812908e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00042831170139834 | |
| dG 1: 6.5434886664661e-07 | |
| dG 2: 3.3281897745575e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7043342 | |
| StyleLoss:updateOutput self.G 2: 14.329719543457 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.9452665835997e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0059671606868505 | |
| dG 1: 1.0932419627352e-06 | |
| dG 2: 2.2242060623845e-12 | |
| --- | |
| Iteration 182 / 2500 | |
| Content 1 loss: 5151767.968750 | |
| Style 1 loss: 69.390351 | |
| Style 2 loss: 24544.169426 | |
| Style 3 loss: 172305.805206 | |
| Style 4 loss: 125962.841034 | |
| Style 5 loss: 15804.940224 | |
| Total loss: 5490455.114991 | |
| --- | |
| x1 value: -8.0590019226074 | |
| feval(x) grad value: 0.0033354163169861 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105636944 | |
| StyleLoss:updateOutput self.G 2: 6.7869129180908 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.1048820403612e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00075086654396728 | |
| dG 1: -1.2705691915471e-05 | |
| dG 2: -8.1630936819149e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1922617984 | |
| StyleLoss:updateOutput self.G 2: 246.5279083252 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.42940956241e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00041147734737024 | |
| dG 1: -1.466245703341e-05 | |
| dG 2: -1.8800952548276e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842793792 | |
| StyleLoss:updateOutput self.G 2: 216.13470458984 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8698766979242e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00017105764709413 | |
| dG 1: -1.6322413785019e-06 | |
| dG 2: -4.1858861212288e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93824248 | |
| StyleLoss:updateOutput self.G 2: 47.721492767334 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.8952981102655e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -9.5831157523207e-05 | |
| dG 1: 3.1348236007034e-07 | |
| dG 2: 1.5944533291335e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6946916 | |
| StyleLoss:updateOutput self.G 2: 14.133535385132 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.8542278818786e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0029125364962965 | |
| dG 1: -4.0351397956329e-07 | |
| dG 2: -8.2095121716266e-13 | |
| --- | |
| Iteration 183 / 2500 | |
| Content 1 loss: 5136526.171875 | |
| Style 1 loss: 57.622612 | |
| Style 2 loss: 22674.047470 | |
| Style 3 loss: 170779.254913 | |
| Style 4 loss: 125849.761963 | |
| Style 5 loss: 15705.277920 | |
| Total loss: 5471592.136753 | |
| --- | |
| x1 value: -8.059289932251 | |
| feval(x) grad value: -0.0089938500896096 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105931232 | |
| StyleLoss:updateOutput self.G 2: 6.8058199882507 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.7471893083184e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0022645676508546 | |
| dG 1: -3.4739482543955e-06 | |
| dG 2: -2.2319257173779e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928192512 | |
| StyleLoss:updateOutput self.G 2: 247.24273681641 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0198007061035e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00078360683983192 | |
| dG 1: 7.2589340561535e-05 | |
| dG 2: 9.3077776389117e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846172288 | |
| StyleLoss:updateOutput self.G 2: 217.00105285645 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6724550278013e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010084504028782 | |
| dG 1: 2.4808005036903e-05 | |
| dG 2: 6.3620198519077e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94207592 | |
| StyleLoss:updateOutput self.G 2: 47.916465759277 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8139665769468e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0010702556464821 | |
| dG 1: 1.800956738407e-06 | |
| dG 2: 9.1601379259565e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7031013.5 | |
| StyleLoss:updateOutput self.G 2: 14.304633140564 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.610471695647e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0051662824116647 | |
| dG 1: 9.0185523049513e-07 | |
| dG 2: 1.8348289467646e-12 | |
| --- | |
| Iteration 184 / 2500 | |
| Content 1 loss: 5157603.515625 | |
| Style 1 loss: 50.491489 | |
| Style 2 loss: 28973.113060 | |
| Style 3 loss: 186880.325317 | |
| Style 4 loss: 127846.961975 | |
| Style 5 loss: 15656.785011 | |
| Total loss: 5517011.192478 | |
| --- | |
| x1 value: -8.0604095458984 | |
| feval(x) grad value: 0.0081157544627786 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105683424 | |
| StyleLoss:updateOutput self.G 2: 6.7898993492126 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.7248267642221e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001479726517573 | |
| dG 1: -1.1247797374381e-05 | |
| dG 2: -7.2264319094656e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923282176 | |
| StyleLoss:updateOutput self.G 2: 246.61309814453 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9195506012769e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -8.9850414951798e-05 | |
| dG 1: -4.2664810280257e-06 | |
| dG 2: -5.4707009321947e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842073024 | |
| StyleLoss:updateOutput self.G 2: 215.94981384277 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0057704002975e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00046340117114596 | |
| dG 1: -7.2737962000247e-06 | |
| dG 2: -1.8653665851548e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93458152 | |
| StyleLoss:updateOutput self.G 2: 47.535266876221 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8754749930849e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011957985116169 | |
| dG 1: -1.1073011592089e-06 | |
| dG 2: -5.6320253365508e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6916927.5 | |
| StyleLoss:updateOutput self.G 2: 14.07252407074 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.0240661165808e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0054144393652678 | |
| dG 1: -8.6898580775596e-07 | |
| dG 2: -1.7679563325476e-12 | |
| --- | |
| Iteration 185 / 2500 | |
| Content 1 loss: 5125220.703125 | |
| Style 1 loss: 56.423262 | |
| Style 2 loss: 21936.189651 | |
| Style 3 loss: 162849.025726 | |
| Style 4 loss: 125203.353882 | |
| Style 5 loss: 15782.319546 | |
| Total loss: 5451048.015192 | |
| --- | |
| x1 value: -8.0628156661987 | |
| feval(x) grad value: -0.0088973045349121 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105657312 | |
| StyleLoss:updateOutput self.G 2: 6.7882204055786 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.9130777789987e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00173486454878 | |
| dG 1: -1.2067268471583e-05 | |
| dG 2: -7.7529215686267e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1923079168 | |
| StyleLoss:updateOutput self.G 2: 246.58708190918 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.3358408236857e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -2.894815588661e-05 | |
| dG 1: -7.4401978054084e-06 | |
| dG 2: -9.5402017449148e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845743360 | |
| StyleLoss:updateOutput self.G 2: 216.89106750488 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.6875810615602e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00084510608576238 | |
| dG 1: 2.1450787244248e-05 | |
| dG 2: 5.5010601109073e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94680200 | |
| StyleLoss:updateOutput self.G 2: 48.156829833984 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.3751967976059e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0020011276938021 | |
| dG 1: 3.6348587855173e-06 | |
| dG 2: 1.8487850139692e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7072637 | |
| StyleLoss:updateOutput self.G 2: 14.389317512512 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2771657793564e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0076629938557744 | |
| dG 1: 1.5479558896914e-06 | |
| dG 2: 3.1493245511438e-12 | |
| --- | |
| Iteration 186 / 2500 | |
| Content 1 loss: 5174970.703125 | |
| Style 1 loss: 58.865122 | |
| Style 2 loss: 22124.486446 | |
| Style 3 loss: 180314.586639 | |
| Style 4 loss: 129845.878601 | |
| Style 5 loss: 16027.011395 | |
| Total loss: 5523341.531329 | |
| --- | |
| x1 value: -8.0624561309814 | |
| feval(x) grad value: 0.015308393165469 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105491256 | |
| StyleLoss:updateOutput self.G 2: 6.7775530815125 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0703756809535e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0020516111981124 | |
| dG 1: -1.7276412108913e-05 | |
| dG 2: -1.1099668276518e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919468160 | |
| StyleLoss:updateOutput self.G 2: 246.12405395508 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0235132208436e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00067643512738869 | |
| dG 1: -6.3965148001444e-05 | |
| dG 2: -8.2019382605103e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840285504 | |
| StyleLoss:updateOutput self.G 2: 215.49145507812 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.8851032734801e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.001031071646139 | |
| dG 1: -2.126270919689e-05 | |
| dG 2: -5.4528265583387e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93338688 | |
| StyleLoss:updateOutput self.G 2: 47.474491119385 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.2375571973043e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0013688068138435 | |
| dG 1: -1.5708876617282e-06 | |
| dG 2: -7.9899493054744e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6930366.5 | |
| StyleLoss:updateOutput self.G 2: 14.099868774414 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.2010931262412e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0043206559494138 | |
| dG 1: -6.6037864598911e-07 | |
| dG 2: -1.3435437658249e-12 | |
| --- | |
| Iteration 187 / 2500 | |
| Content 1 loss: 5120112.109375 | |
| Style 1 loss: 69.703605 | |
| Style 2 loss: 24946.555138 | |
| Style 3 loss: 168858.604431 | |
| Style 4 loss: 125857.589722 | |
| Style 5 loss: 15598.481655 | |
| Total loss: 5455443.043926 | |
| --- | |
| x1 value: -8.0642213821411 | |
| feval(x) grad value: -0.012277008965611 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105947392 | |
| StyleLoss:updateOutput self.G 2: 6.8068585395813 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.7456916740221e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016527945408598 | |
| dG 1: -2.966937245219e-06 | |
| dG 2: -1.9061840864434e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928694400 | |
| StyleLoss:updateOutput self.G 2: 247.30706787109 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1201201743916e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00086913688573986 | |
| dG 1: 8.044448622968e-05 | |
| dG 2: 1.0315003191874e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846838016 | |
| StyleLoss:updateOutput self.G 2: 217.17184448242 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0166406500266e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012608177494258 | |
| dG 1: 3.0018352845218e-05 | |
| dG 2: 7.6982144617266e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94286016 | |
| StyleLoss:updateOutput self.G 2: 47.956352233887 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.0951783596956e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012467001797631 | |
| dG 1: 2.1053228920209e-06 | |
| dG 2: 1.0708226839962e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7044957 | |
| StyleLoss:updateOutput self.G 2: 14.333002090454 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.8599070952332e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.005915944930166 | |
| dG 1: 1.1183070682819e-06 | |
| dG 2: 2.2752021620887e-12 | |
| --- | |
| Iteration 188 / 2500 | |
| Content 1 loss: 5165902.343750 | |
| Style 1 loss: 46.799395 | |
| Style 2 loss: 27616.776466 | |
| Style 3 loss: 176126.873016 | |
| Style 4 loss: 125810.726166 | |
| Style 5 loss: 15686.953068 | |
| Total loss: 5511190.471862 | |
| --- | |
| x1 value: -8.0648050308228 | |
| feval(x) grad value: 0.00059650529874489 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105819808 | |
| StyleLoss:updateOutput self.G 2: 6.7986612319946 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2560913198456e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00060262449551374 | |
| dG 1: -6.9695761339972e-06 | |
| dG 2: -4.4777804511162e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1925797504 | |
| StyleLoss:updateOutput self.G 2: 246.93566894531 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.9895777105321e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 1.7693078916636e-05 | |
| dG 1: 3.510721217026e-05 | |
| dG 2: 4.5016269357989e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842674176 | |
| StyleLoss:updateOutput self.G 2: 216.10403442383 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.4571635271495e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00010541614756221 | |
| dG 1: -2.5683257263154e-06 | |
| dG 2: -6.5864772403473e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93717776 | |
| StyleLoss:updateOutput self.G 2: 47.667324066162 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.5233282836298e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00040233854088001 | |
| dG 1: -9.9754068116908e-08 | |
| dG 2: -5.0737568307998e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6903057 | |
| StyleLoss:updateOutput self.G 2: 14.044305801392 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0179389846598e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0061076334677637 | |
| dG 1: -1.0842843494174e-06 | |
| dG 2: -2.2059817080672e-12 | |
| --- | |
| Iteration 189 / 2500 | |
| Content 1 loss: 5118308.203125 | |
| Style 1 loss: 50.474055 | |
| Style 2 loss: 23540.981770 | |
| Style 3 loss: 170357.769012 | |
| Style 4 loss: 127116.210938 | |
| Style 5 loss: 15983.365059 | |
| Total loss: 5455357.003959 | |
| --- | |
| x1 value: -8.0655241012573 | |
| feval(x) grad value: 0.0023288140073419 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105547824 | |
| StyleLoss:updateOutput self.G 2: 6.781186580658 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.800832359957e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0006155805895105 | |
| dG 1: -1.5501971574849e-05 | |
| dG 2: -9.959632944756e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920613248 | |
| StyleLoss:updateOutput self.G 2: 246.27087402344 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.7928327729969e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -2.472469714121e-05 | |
| dG 1: -4.6043813199503e-05 | |
| dG 2: -5.9039734906507e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843687552 | |
| StyleLoss:updateOutput self.G 2: 216.36387634277 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.8631261866631e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00019088614499196 | |
| dG 1: 5.3617495723302e-06 | |
| dG 2: 1.3750219495998e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94130920 | |
| StyleLoss:updateOutput self.G 2: 47.877464294434 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3258416231565e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00077191827585921 | |
| dG 1: 1.5034577245387e-06 | |
| dG 2: 7.6469820059488e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7101571.5 | |
| StyleLoss:updateOutput self.G 2: 14.448183059692 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4010463473824e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084062777459621 | |
| dG 1: 1.9970575522166e-06 | |
| dG 2: 4.0630242226225e-12 | |
| --- | |
| Iteration 190 / 2500 | |
| Content 1 loss: 5182360.937500 | |
| Style 1 loss: 65.914968 | |
| Style 2 loss: 23213.755131 | |
| Style 3 loss: 179460.948944 | |
| Style 4 loss: 129843.910217 | |
| Style 5 loss: 16682.401657 | |
| Total loss: 5531627.868417 | |
| --- | |
| x1 value: -8.0670938491821 | |
| feval(x) grad value: 0.0087223555892706 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105561928 | |
| StyleLoss:updateOutput self.G 2: 6.7820930480957 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6641754519451e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00096519495127723 | |
| dG 1: -1.5059215002111e-05 | |
| dG 2: -9.6751729891653e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920763648 | |
| StyleLoss:updateOutput self.G 2: 246.29014587402 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.5863689491816e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00072479556547478 | |
| dG 1: -4.3688156438293e-05 | |
| dG 2: -5.601919102205e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841712000 | |
| StyleLoss:updateOutput self.G 2: 215.85726928711 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.6658226311774e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00055381114361808 | |
| dG 1: -1.0097518497787e-05 | |
| dG 2: -2.5895110708496e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93604568 | |
| StyleLoss:updateOutput self.G 2: 47.609745025635 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.9048248835534e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00050754653057083 | |
| dG 1: -5.390463115873e-07 | |
| dG 2: -2.7417307248319e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6930138.5 | |
| StyleLoss:updateOutput self.G 2: 14.099404335022 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.8423014454311e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0041053807362914 | |
| dG 1: -6.6391612563166e-07 | |
| dG 2: -1.3507410251065e-12 | |
| --- | |
| Iteration 191 / 2500 | |
| Content 1 loss: 5119032.421875 | |
| Style 1 loss: 62.697854 | |
| Style 2 loss: 22479.978561 | |
| Style 3 loss: 163897.167206 | |
| Style 4 loss: 125701.492310 | |
| Style 5 loss: 15611.555099 | |
| Total loss: 5446785.312905 | |
| --- | |
| x1 value: -8.0681133270264 | |
| feval(x) grad value: -0.011377980932593 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 106010456 | |
| StyleLoss:updateOutput self.G 2: 6.8109107017517 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.191604148687e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0018727548886091 | |
| dG 1: -9.8842838269775e-07 | |
| dG 2: -6.3504079382201e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1929884032 | |
| StyleLoss:updateOutput self.G 2: 247.45962524414 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1731985694041e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00072010193252936 | |
| dG 1: 9.906709601637e-05 | |
| dG 2: 1.2702889955196e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 846498048 | |
| StyleLoss:updateOutput self.G 2: 217.08465576172 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.984750781503e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0011043908307329 | |
| dG 1: 2.7358519218978e-05 | |
| dG 2: 7.0160995069291e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94322048 | |
| StyleLoss:updateOutput self.G 2: 47.974681854248 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2193854931629e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012601057533175 | |
| dG 1: 2.2451686163549e-06 | |
| dG 2: 1.1419518759423e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7022913 | |
| StyleLoss:updateOutput self.G 2: 14.288153648376 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.1466553208666e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0042879930697381 | |
| dG 1: 7.7611844062631e-07 | |
| dG 2: 1.5790168651775e-12 | |
| --- | |
| Iteration 192 / 2500 | |
| Content 1 loss: 5167739.062500 | |
| Style 1 loss: 44.814806 | |
| Style 2 loss: 30470.832825 | |
| Style 3 loss: 171761.363983 | |
| Style 4 loss: 123929.912567 | |
| Style 5 loss: 15408.356667 | |
| Total loss: 5509354.343348 | |
| --- | |
| x1 value: -8.0681486129761 | |
| feval(x) grad value: 0.0023293022532016 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105871664 | |
| StyleLoss:updateOutput self.G 2: 6.801992893219 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.2267001272053e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00018567133520264 | |
| dG 1: -5.3428789215104e-06 | |
| dG 2: -3.4326672906058e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1926668800 | |
| StyleLoss:updateOutput self.G 2: 247.04733276367 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.3103835752117e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00044890443678014 | |
| dG 1: 4.8742062062956e-05 | |
| dG 2: 6.2499571490404e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842797312 | |
| StyleLoss:updateOutput self.G 2: 216.13555908203 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.1294192248101e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 5.9011003941123e-06 | |
| dG 1: -1.604176645742e-06 | |
| dG 2: -4.1139154257665e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93548784 | |
| StyleLoss:updateOutput self.G 2: 47.58137512207 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4286231930782e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00088678655447438 | |
| dG 1: -7.5557875334198e-07 | |
| dG 2: -3.8430733205128e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6944691.5 | |
| StyleLoss:updateOutput self.G 2: 14.129014968872 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.2433222208492e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0031459934543818 | |
| dG 1: -4.3801290416923e-07 | |
| dG 2: -8.911394842534e-13 | |
| --- | |
| Iteration 193 / 2500 | |
| Content 1 loss: 5129509.765625 | |
| Style 1 loss: 48.074154 | |
| Style 2 loss: 24503.940582 | |
| Style 3 loss: 162389.751434 | |
| Style 4 loss: 123674.057007 | |
| Style 5 loss: 15338.211536 | |
| Total loss: 5455463.800339 | |
| --- | |
| x1 value: -8.0707597732544 | |
| feval(x) grad value: -0.00090406142408028 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105530280 | |
| StyleLoss:updateOutput self.G 2: 6.7800598144531 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8943252178569e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00012037714623148 | |
| dG 1: -1.605216129974e-05 | |
| dG 2: -1.0313116463254e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920368000 | |
| StyleLoss:updateOutput self.G 2: 246.23947143555 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.1428688619999e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00043087621452287 | |
| dG 1: -4.9876238335855e-05 | |
| dG 2: -6.3953868242872e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843759936 | |
| StyleLoss:updateOutput self.G 2: 216.38243103027 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.9235455773223e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00024406884040218 | |
| dG 1: 5.9297858570062e-06 | |
| dG 2: 1.5206950282343e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94233456 | |
| StyleLoss:updateOutput self.G 2: 47.929618835449 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.1043130971066e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012597714085132 | |
| dG 1: 1.9013627934328e-06 | |
| dG 2: 9.6708307594645e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7040541.5 | |
| StyleLoss:updateOutput self.G 2: 14.324017524719 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.5334166871908e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0057200500741601 | |
| dG 1: 1.0497369657969e-06 | |
| dG 2: 2.1356952664697e-12 | |
| --- | |
| Iteration 194 / 2500 | |
| Content 1 loss: 5158724.609375 | |
| Style 1 loss: 64.445117 | |
| Style 2 loss: 23462.875843 | |
| Style 3 loss: 174329.853058 | |
| Style 4 loss: 126575.603485 | |
| Style 5 loss: 15627.262115 | |
| Total loss: 5498784.648994 | |
| --- | |
| x1 value: -8.0703754425049 | |
| feval(x) grad value: 0.013453031890094 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105522856 | |
| StyleLoss:updateOutput self.G 2: 6.7795829772949 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9638948123866e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0018232606817037 | |
| dG 1: -1.6285024685203e-05 | |
| dG 2: -1.0462725521035e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919882112 | |
| StyleLoss:updateOutput self.G 2: 246.17713928223 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.890199947904e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00065000442555174 | |
| dG 1: -5.7484765420668e-05 | |
| dG 2: -7.3709918377296e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841017280 | |
| StyleLoss:updateOutput self.G 2: 215.67907714844 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.6334570318577e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00092390977079049 | |
| dG 1: -1.5535848433501e-05 | |
| dG 2: -3.984172367022e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93512656 | |
| StyleLoss:updateOutput self.G 2: 47.562995910645 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.5374317285932e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00096919311909005 | |
| dG 1: -8.9569482497609e-07 | |
| dG 2: -4.5557405504298e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6941952.5 | |
| StyleLoss:updateOutput self.G 2: 14.12343788147 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.5444508006985e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0033266702666879 | |
| dG 1: -4.8055085244414e-07 | |
| dG 2: -9.7768321216662e-13 | |
| --- | |
| Iteration 195 / 2500 | |
| Content 1 loss: 5131373.828125 | |
| Style 1 loss: 65.675989 | |
| Style 2 loss: 23537.760258 | |
| Style 3 loss: 162040.260315 | |
| Style 4 loss: 122941.703796 | |
| Style 5 loss: 15347.528458 | |
| Total loss: 5455306.756940 | |
| --- | |
| x1 value: -8.0714931488037 | |
| feval(x) grad value: -0.014299281872809 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 106113848 | |
| StyleLoss:updateOutput self.G 2: 6.8175520896912 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.5891779387166e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0025708347093314 | |
| dG 1: 2.2547883418156e-06 | |
| dG 2: 1.4486458907448e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1931510784 | |
| StyleLoss:updateOutput self.G 2: 247.66822814941 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.210723468148e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00085445592412725 | |
| dG 1: 0.00012453178351279 | |
| dG 2: 1.5968103575514e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847939392 | |
| StyleLoss:updateOutput self.G 2: 217.45426940918 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.2000313038006e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012450679205358 | |
| dG 1: 3.8638649130007e-05 | |
| dG 2: 9.9088905483602e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94462864 | |
| StyleLoss:updateOutput self.G 2: 48.046298980713 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.7352317033547e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016367227071896 | |
| dG 1: 2.791598717522e-06 | |
| dG 2: 1.4198805976459e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7062839 | |
| StyleLoss:updateOutput self.G 2: 14.369384765625 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.2160893447799e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0072965365834534 | |
| dG 1: 1.3958675708636e-06 | |
| dG 2: 2.8398997563295e-12 | |
| --- | |
| Iteration 196 / 2500 | |
| Content 1 loss: 5174375.000000 | |
| Style 1 loss: 44.177312 | |
| Style 2 loss: 36317.739487 | |
| Style 3 loss: 185603.290558 | |
| Style 4 loss: 124866.039276 | |
| Style 5 loss: 15571.427822 | |
| Total loss: 5536777.674455 | |
| --- | |
| x1 value: -8.0724477767944 | |
| feval(x) grad value: -0.001479291706346 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105966336 | |
| StyleLoss:updateOutput self.G 2: 6.8080749511719 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.9008944397476e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00027157561271451 | |
| dG 1: -2.3729769509373e-06 | |
| dG 2: -1.5245792096222e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928490880 | |
| StyleLoss:updateOutput self.G 2: 247.28099060059 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0916052417542e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00036433286732063 | |
| dG 1: 7.726029434707e-05 | |
| dG 2: 9.9067091335892e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843669696 | |
| StyleLoss:updateOutput self.G 2: 216.35928344727 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.9616566855093e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00034991069696844 | |
| dG 1: 5.222384515946e-06 | |
| dG 2: 1.3392815502444e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93532440 | |
| StyleLoss:updateOutput self.G 2: 47.573059082031 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.4900793132711e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00098623475059867 | |
| dG 1: -8.1899895576498e-07 | |
| dG 2: -4.1656437956179e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6880602 | |
| StyleLoss:updateOutput self.G 2: 13.998623847961 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.2395856856529e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0074375146068633 | |
| dG 1: -1.4328252291307e-06 | |
| dG 2: -2.915090478034e-12 | |
| --- | |
| Iteration 197 / 2500 | |
| Content 1 loss: 5121763.671875 | |
| Style 1 loss: 44.472545 | |
| Style 2 loss: 28618.406296 | |
| Style 3 loss: 167262.462616 | |
| Style 4 loss: 124748.107910 | |
| Style 5 loss: 16127.255917 | |
| Total loss: 5458564.377159 | |
| --- | |
| x1 value: -8.0731220245361 | |
| feval(x) grad value: 0.0044706403277814 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105540736 | |
| StyleLoss:updateOutput self.G 2: 6.7807312011719 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.8717921313491e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00044118278310634 | |
| dG 1: -1.572432120156e-05 | |
| dG 2: -1.0102488507205e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920251520 | |
| StyleLoss:updateOutput self.G 2: 246.22451782227 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.138286571897e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00028509000549093 | |
| dG 1: -5.170154327061e-05 | |
| dG 2: -6.6294370167086e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842705920 | |
| StyleLoss:updateOutput self.G 2: 216.11215209961 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.4347412025681e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00017770323029254 | |
| dG 1: -2.3206157493405e-06 | |
| dG 2: -5.951223671849e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94121336 | |
| StyleLoss:updateOutput self.G 2: 47.872577667236 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5882197601513e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0009171356796287 | |
| dG 1: 1.4661718523712e-06 | |
| dG 2: 7.4573366145442e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7053773.5 | |
| StyleLoss:updateOutput self.G 2: 14.350942611694 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1034627505069e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.006620776373893 | |
| dG 1: 1.2551594181787e-06 | |
| dG 2: 2.5536289662703e-12 | |
| --- | |
| Iteration 198 / 2500 | |
| Content 1 loss: 5167970.703125 | |
| Style 1 loss: 65.656828 | |
| Style 2 loss: 24023.872375 | |
| Style 3 loss: 166985.412598 | |
| Style 4 loss: 123140.876770 | |
| Style 5 loss: 15569.605350 | |
| Total loss: 5497756.127046 | |
| --- | |
| x1 value: -8.0738649368286 | |
| feval(x) grad value: 0.008011419326067 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105535336 | |
| StyleLoss:updateOutput self.G 2: 6.780385017395 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9398685203196e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011947795283049 | |
| dG 1: -1.5893430827418e-05 | |
| dG 2: -1.021113640691e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920055680 | |
| StyleLoss:updateOutput self.G 2: 246.19938659668 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.4989680121671e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00067364278947935 | |
| dG 1: -5.4770440328866e-05 | |
| dG 2: -7.0229460268079e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 841915840 | |
| StyleLoss:updateOutput self.G 2: 215.90956115723 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.4531826871626e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00051928375614807 | |
| dG 1: -8.5030560512678e-06 | |
| dG 2: -2.1806111603906e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93761104 | |
| StyleLoss:updateOutput self.G 2: 47.689361572266 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7808007513054e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -8.5058629338164e-05 | |
| dG 1: 6.8364457206371e-08 | |
| dG 2: 3.4771962869815e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6976498 | |
| StyleLoss:updateOutput self.G 2: 14.193720817566 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.0577678690806e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 5.4346746765077e-05 | |
| dG 1: 5.5662891185193e-08 | |
| dG 2: 1.1324639414157e-13 | |
| --- | |
| Iteration 199 / 2500 | |
| Content 1 loss: 5140223.437500 | |
| Style 1 loss: 62.373430 | |
| Style 2 loss: 23784.606457 | |
| Style 3 loss: 162364.688873 | |
| Style 4 loss: 122369.098663 | |
| Style 5 loss: 15156.962872 | |
| Total loss: 5463961.167794 | |
| --- | |
| x1 value: -8.0743856430054 | |
| feval(x) grad value: -0.0077376537956297 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105963344 | |
| StyleLoss:updateOutput self.G 2: 6.80788230896 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.6181400925605e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016004324425012 | |
| dG 1: -2.4668217974977e-06 | |
| dG 2: -1.58487209901e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928760832 | |
| StyleLoss:updateOutput self.G 2: 247.31559753418 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.103534046365e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00068574043689296 | |
| dG 1: 8.148552296916e-05 | |
| dG 2: 1.0448489295989e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 845709504 | |
| StyleLoss:updateOutput self.G 2: 216.88247680664 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.5895486171758e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00098218396306038 | |
| dG 1: 2.1188367099967e-05 | |
| dG 2: 5.4337619473377e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94045168 | |
| StyleLoss:updateOutput self.G 2: 47.833854675293 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1081416317893e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00057375279720873 | |
| dG 1: 1.1707547855622e-06 | |
| dG 2: 5.9547657603465e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6969849 | |
| StyleLoss:updateOutput self.G 2: 14.180193901062 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.7509520458061e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058505713241175 | |
| dG 1: -4.7541849568233e-08 | |
| dG 2: -9.6724176248692e-14 | |
| --- | |
| Iteration 200 / 2500 | |
| Content 1 loss: 5151866.796875 | |
| Style 1 loss: 45.212402 | |
| Style 2 loss: 28697.419167 | |
| Style 3 loss: 169796.070099 | |
| Style 4 loss: 122826.725006 | |
| Style 5 loss: 15311.415195 | |
| Total loss: 5488543.638744 | |
| --- | |
| x1 value: -8.0746240615845 | |
| feval(x) grad value: -0.004088825546205 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105924720 | |
| StyleLoss:updateOutput self.G 2: 6.805401802063 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.6389583547609e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00092724576825276 | |
| dG 1: -3.678261919049e-06 | |
| dG 2: -2.3631921072798e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927534592 | |
| StyleLoss:updateOutput self.G 2: 247.15837097168 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0217463142226e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0005040590185672 | |
| dG 1: 6.2295635871124e-05 | |
| dG 2: 7.9878647099574e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 843621376 | |
| StyleLoss:updateOutput self.G 2: 216.34693908691 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.6352590977203e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00039949643542059 | |
| dG 1: 4.8446513574163e-06 | |
| dG 2: 1.2424119892607e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93798576 | |
| StyleLoss:updateOutput self.G 2: 47.708427429199 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.7649835371003e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00011041675315937 | |
| dG 1: 2.1376666836659e-07 | |
| dG 2: 1.0872736494775e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6993779.5 | |
| StyleLoss:updateOutput self.G 2: 14.228880882263 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.9552211344708e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017731328262016 | |
| dG 1: 3.2391082527283e-07 | |
| dG 2: 6.5899840922742e-13 | |
| --- | |
| Iteration 201 / 2500 | |
| Content 1 loss: 5143191.015625 | |
| Style 1 loss: 45.186969 | |
| Style 2 loss: 26568.818092 | |
| Style 3 loss: 161174.102783 | |
| Style 4 loss: 121523.872375 | |
| Style 5 loss: 15059.324741 | |
| Total loss: 5467562.320586 | |
| --- | |
| x1 value: -8.0768184661865 | |
| feval(x) grad value: 0.0062804156914353 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105493896 | |
| StyleLoss:updateOutput self.G 2: 6.7777214050293 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.1801520467288e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0012417176039889 | |
| dG 1: -1.7193910025526e-05 | |
| dG 2: -1.1046662716507e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919334912 | |
| StyleLoss:updateOutput self.G 2: 246.10696411133 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0553615936715e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00058227765839547 | |
| dG 1: -6.6050277382601e-05 | |
| dG 2: -8.4693059856922e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842363328 | |
| StyleLoss:updateOutput self.G 2: 216.02432250977 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.4527235110745e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037107514799573 | |
| dG 1: -5.0002063289867e-06 | |
| dG 2: -1.2823043408355e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93921216 | |
| StyleLoss:updateOutput self.G 2: 47.770812988281 | |
| StyleLoss:updateGradInput self.gradInput 1: 5.7311240198032e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00030795380007476 | |
| dG 1: 6.8972747158114e-07 | |
| dG 2: 3.5081350801755e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7004795.5 | |
| StyleLoss:updateOutput self.G 2: 14.251293182373 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.389453067688e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.002633671509102 | |
| dG 1: 4.9490404308017e-07 | |
| dG 2: 1.0068853303208e-12 | |
| --- | |
| Iteration 202 / 2500 | |
| Content 1 loss: 5155573.437500 | |
| Style 1 loss: 66.163665 | |
| Style 2 loss: 24592.878342 | |
| Style 3 loss: 166165.260315 | |
| Style 4 loss: 122934.505463 | |
| Style 5 loss: 15257.934093 | |
| Total loss: 5484590.179377 | |
| --- | |
| x1 value: -8.0770969390869 | |
| feval(x) grad value: 0.0061358287930489 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105581968 | |
| StyleLoss:updateOutput self.G 2: 6.7833800315857 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6274929726687e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 6.9679066655226e-05 | |
| dG 1: -1.4431022464123e-05 | |
| dG 2: -9.2715743936489e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1921034752 | |
| StyleLoss:updateOutput self.G 2: 246.32489013672 | |
| StyleLoss:updateGradInput self.gradInput 1: -7.634672272161e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00037073728162795 | |
| dG 1: -3.9447710150853e-05 | |
| dG 2: -5.0581873758948e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842796032 | |
| StyleLoss:updateOutput self.G 2: 216.13525390625 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3197099413276e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -4.6364413719857e-05 | |
| dG 1: -1.6147022279256e-06 | |
| dG 2: -4.1409074520021e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93920936 | |
| StyleLoss:updateOutput self.G 2: 47.770656585693 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.125902439815e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00045048331958242 | |
| dG 1: 6.8860362034684e-07 | |
| dG 2: 3.5024197084232e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6973571 | |
| StyleLoss:updateOutput self.G 2: 14.187767028809 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.2777654186164e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 3.7666563002858e-05 | |
| dG 1: 1.0240713166354e-08 | |
| dG 2: 2.0834798052398e-14 | |
| --- | |
| Iteration 203 / 2500 | |
| Content 1 loss: 5145712.890625 | |
| Style 1 loss: 61.986012 | |
| Style 2 loss: 23297.138214 | |
| Style 3 loss: 162126.720428 | |
| Style 4 loss: 121388.385773 | |
| Style 5 loss: 15075.638294 | |
| Total loss: 5467662.759347 | |
| --- | |
| x1 value: -8.0778789520264 | |
| feval(x) grad value: -0.0061502261087298 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105987264 | |
| StyleLoss:updateOutput self.G 2: 6.8094186782837 | |
| StyleLoss:updateGradInput self.gradInput 1: 8.420978581114e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00020789823611267 | |
| dG 1: -1.716601445878e-06 | |
| dG 2: -1.1028740990121e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1929009920 | |
| StyleLoss:updateOutput self.G 2: 247.34756469727 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0729468158388e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00060481927357614 | |
| dG 1: 8.5386840510182e-05 | |
| dG 2: 1.094873844365e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844543040 | |
| StyleLoss:updateOutput self.G 2: 216.58326721191 | |
| StyleLoss:updateGradInput self.gradInput 1: 7.8314258189494e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00046488802763633 | |
| dG 1: 1.2057995263604e-05 | |
| dG 2: 3.0922757843915e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93892896 | |
| StyleLoss:updateOutput self.G 2: 47.756401062012 | |
| StyleLoss:updateGradInput self.gradInput 1: -5.3940996114221e-10 | |
| StyleLoss:updateGradInput self.gradInput 2: -3.5145474157616e-06 | |
| dG 1: 5.7977439382739e-07 | |
| dG 2: 2.94888489712e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6997406.5 | |
| StyleLoss:updateOutput self.G 2: 14.236261367798 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.7198453267374e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0016319074202329 | |
| dG 1: 3.8022076864763e-07 | |
| dG 2: 7.7356117388419e-13 | |
| --- | |
| Iteration 204 / 2500 | |
| Content 1 loss: 5158468.750000 | |
| Style 1 loss: 41.481206 | |
| Style 2 loss: 32384.279251 | |
| Style 3 loss: 182184.413910 | |
| Style 4 loss: 126051.509857 | |
| Style 5 loss: 15243.244171 | |
| Total loss: 5514373.678396 | |
| --- | |
| x1 value: -8.0788402557373 | |
| feval(x) grad value: -0.0063715241849422 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105965736 | |
| StyleLoss:updateOutput self.G 2: 6.8080358505249 | |
| StyleLoss:updateGradInput self.gradInput 1: 4.8907073946225e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0019798190332949 | |
| dG 1: -2.3917102680571e-06 | |
| dG 2: -1.5366150734643e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1928373888 | |
| StyleLoss:updateOutput self.G 2: 247.26596069336 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.0857625198923e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00040461952448823 | |
| dG 1: 7.5431540608406e-05 | |
| dG 2: 9.6722204898092e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 844863040 | |
| StyleLoss:updateOutput self.G 2: 216.66534423828 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3047340985395e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00075168674811721 | |
| dG 1: 1.4562829164788e-05 | |
| dG 2: 3.7346406349392e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93954344 | |
| StyleLoss:updateOutput self.G 2: 47.787647247314 | |
| StyleLoss:updateGradInput self.gradInput 1: 6.6167316958854e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00032886146800593 | |
| dG 1: 8.182075816876e-07 | |
| dG 2: 4.1616203213558e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6951393 | |
| StyleLoss:updateOutput self.G 2: 14.142645835876 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.6770745737158e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0022062442731112 | |
| dG 1: -3.340119008044e-07 | |
| dG 2: -6.7954902772602e-13 | |
| --- | |
| Iteration 205 / 2500 | |
| Content 1 loss: 5137704.687500 | |
| Style 1 loss: 44.939236 | |
| Style 2 loss: 28263.167381 | |
| Style 3 loss: 168614.215851 | |
| Style 4 loss: 123990.829468 | |
| Style 5 loss: 15248.291016 | |
| Total loss: 5473866.130451 | |
| --- | |
| x1 value: -8.080114364624 | |
| feval(x) grad value: 0.0058089923113585 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105522672 | |
| StyleLoss:updateOutput self.G 2: 6.779571056366 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.0639292819833e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0011821882799268 | |
| dG 1: -1.6290929124807e-05 | |
| dG 2: -1.0466518060234e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1919796096 | |
| StyleLoss:updateOutput self.G 2: 246.16612243652 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0010897710799e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00022336983238347 | |
| dG 1: -5.8832065406023e-05 | |
| dG 2: -7.5437486118934e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842574848 | |
| StyleLoss:updateOutput self.G 2: 216.07856750488 | |
| StyleLoss:updateGradInput self.gradInput 1: -6.1280438501399e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00025327160255983 | |
| dG 1: -3.3443818665546e-06 | |
| dG 2: -8.576678321541e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94006048 | |
| StyleLoss:updateOutput self.G 2: 47.813949584961 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.429763991875e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0005240291939117 | |
| dG 1: 1.0188991836912e-06 | |
| dG 2: 5.1823898904874e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7058188 | |
| StyleLoss:updateOutput self.G 2: 14.359922409058 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1150547152283e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0066903275437653 | |
| dG 1: 1.3236834774943e-06 | |
| dG 2: 2.6930411026194e-12 | |
| --- | |
| Iteration 206 / 2500 | |
| Content 1 loss: 5174927.734375 | |
| Style 1 loss: 64.086501 | |
| Style 2 loss: 24205.575943 | |
| Style 3 loss: 164342.834473 | |
| Style 4 loss: 123150.936127 | |
| Style 5 loss: 15648.263454 | |
| Total loss: 5502339.430872 | |
| --- | |
| x1 value: -8.0813417434692 | |
| feval(x) grad value: 0.010585675947368 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105541088 | |
| StyleLoss:updateOutput self.G 2: 6.7807540893555 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9580559274555e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0014299305621535 | |
| dG 1: -1.5713227185188e-05 | |
| dG 2: -1.0095360962123e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1920013696 | |
| StyleLoss:updateOutput self.G 2: 246.19400024414 | |
| StyleLoss:updateGradInput self.gradInput 1: -9.3155655633836e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00086975679732859 | |
| dG 1: -5.5427932238672e-05 | |
| dG 2: -7.1072540214212e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 840764416 | |
| StyleLoss:updateOutput self.G 2: 215.61427307129 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3029445256052e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00084216624964029 | |
| dG 1: -1.7513923012302e-05 | |
| dG 2: -4.4914497468462e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93541128 | |
| StyleLoss:updateOutput self.G 2: 47.577484130859 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.0427041985395e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00065578584326431 | |
| dG 1: -7.8514534607166e-07 | |
| dG 2: -3.9934562275946e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6914592.5 | |
| StyleLoss:updateOutput self.G 2: 14.067775726318 | |
| StyleLoss:updateGradInput self.gradInput 1: -8.713921602066e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0052283531986177 | |
| dG 1: -9.0522848950059e-07 | |
| dG 2: -1.8416923801973e-12 | |
| --- | |
| Iteration 207 / 2500 | |
| Content 1 loss: 5122337.109375 | |
| Style 1 loss: 60.429884 | |
| Style 2 loss: 24337.151527 | |
| Style 3 loss: 167951.683044 | |
| Style 4 loss: 124245.677948 | |
| Style 5 loss: 15468.980312 | |
| Total loss: 5454401.032091 | |
| --- | |
| x1 value: -8.0819625854492 | |
| feval(x) grad value: -0.014083371497691 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 106033480 | |
| StyleLoss:updateOutput self.G 2: 6.8123893737793 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.3468223869495e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0025091201532632 | |
| dG 1: -2.6616737613949e-07 | |
| dG 2: -1.7100592318055e-14 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1930189696 | |
| StyleLoss:updateOutput self.G 2: 247.49879455566 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.1655610165917e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0009294098126702 | |
| dG 1: 0.00010385099449195 | |
| dG 2: 1.3316303788813e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 847987264 | |
| StyleLoss:updateOutput self.G 2: 217.46656799316 | |
| StyleLoss:updateGradInput self.gradInput 1: 2.182374316817e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0012525249039754 | |
| dG 1: 3.9014179492369e-05 | |
| dG 2: 1.0005193722129e-11 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 94526632 | |
| StyleLoss:updateOutput self.G 2: 48.078731536865 | |
| StyleLoss:updateGradInput self.gradInput 1: 3.0516699212058e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0017989460611716 | |
| dG 1: 3.0390374377021e-06 | |
| dG 2: 1.5457346774078e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 7083080 | |
| StyleLoss:updateOutput self.G 2: 14.410564422607 | |
| StyleLoss:updateGradInput self.gradInput 1: 1.4031444379725e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.0084188692271709 | |
| dG 1: 1.7100536524595e-06 | |
| dG 2: 3.4791126143213e-12 | |
| --- | |
| Iteration 208 / 2500 | |
| Content 1 loss: 5187439.062500 | |
| Style 1 loss: 43.585164 | |
| Style 2 loss: 32304.834366 | |
| Style 3 loss: 181705.272675 | |
| Style 4 loss: 121760.513306 | |
| Style 5 loss: 15461.491585 | |
| Total loss: 5538714.759595 | |
| --- | |
| x1 value: -8.0818881988525 | |
| feval(x) grad value: 0.0046064304187894 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 105900288 | |
| StyleLoss:updateOutput self.G 2: 6.803831577301 | |
| StyleLoss:updateGradInput self.gradInput 1: -4.5262318337791e-09 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.00028055396978743 | |
| dG 1: -4.4446805986809e-06 | |
| dG 2: -2.8555977027445e-13 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 1927074560 | |
| StyleLoss:updateOutput self.G 2: 247.09931945801 | |
| StyleLoss:updateGradInput self.gradInput 1: 9.4405535833175e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: 0.00040060223545879 | |
| dG 1: 5.5087977671064e-05 | |
| dG 2: 7.0636625888743e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 842466688 | |
| StyleLoss:updateOutput self.G 2: 216.05081176758 | |
| StyleLoss:updateGradInput self.gradInput 1: -3.9515676064639e-08 | |
| StyleLoss:updateGradInput self.gradInput 2: -9.3220136477612e-05 | |
| dG 1: -4.1912899177987e-06 | |
| dG 2: -1.0748572171204e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 93236160 | |
| StyleLoss:updateOutput self.G 2: 47.422374725342 | |
| StyleLoss:updateGradInput self.gradInput 1: -2.4779703267086e-07 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0015637211035937 | |
| dG 1: -1.9686222003656e-06 | |
| dG 2: -1.0012930155151e-12 | |
| --- | |
| StyleLoss:updateOutput self.G 1: 6869286 | |
| StyleLoss:updateOutput self.G 2: 13.975598335266 | |
| StyleLoss:updateGradInput self.gradInput 1: -1.3384637895797e-06 | |
| StyleLoss:updateGradInput self.gradInput 2: -0.0080307805910707 | |
| dG 1: -1.6084813978523e-06 | |
| dG 2: -3.2724641139265e-12 | |
| --- | |
| Iteration 209 / 2500 | |
| Content 1 loss: 5119408.203125 | |
| Style 1 loss: 45.225525 | |
| Style 2 loss: 26348.264694 | |
| Style 3 loss: 168967.529297 | |
| Style 4 loss: 125227.558136 | |
| Style 5 loss: 16011.380196 | |
| Total loss: 5456008.160973 | |
| --- |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment