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ProGamerGov/-image_size_2432_-tv_weight_0_log.txt Secret

Created October 24, 2017 00:08
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-image_size_2432_-tv_weight_0_log.txt
-image_size 2432 & -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) -> output]
(1): nn.GPU(1) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): nn.StyleLoss
}
(2): nn.GPU(2) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): cudnn.SpatialMaxPooling(2x2, 2,2)
}
(3): nn.GPU(3) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): nn.StyleLoss
(4): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
(5): cudnn.ReLU
(6): cudnn.SpatialMaxPooling(2x2, 2,2)
}
(4): nn.GPU(4) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): nn.StyleLoss
}
(5): nn.GPU(5) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
(1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
(4): cudnn.ReLU
(5): cudnn.SpatialMaxPooling(2x2, 2,2)
}
(6): nn.GPU(6) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
(1): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): nn.StyleLoss
(4): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
(5): cudnn.ReLU
(6): nn.ContentLoss
}
(7): nn.GPU(7) @ nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
(1): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
(2): cudnn.ReLU
(3): cudnn.SpatialMaxPooling(2x2, 2,2)
(4): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
(5): cudnn.ReLU
}
(8): nn.GPU(8) @ nn.Sequential {
[input -> (1) -> output]
(1): nn.StyleLoss
}
}
Capturing style target 1
3.8834133148193
125.94509124756
122.21520233154
23.665088653564
4.5750517845154
y value: 41.496166229248
dy value: 0
Running optimization with ADAM
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 1 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808159069944e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 2 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808159069944e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 3 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 4 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 5 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275223000208e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 6 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275230276166e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808145427523e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 7 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 8 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808159069944e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 2: -6.1862955079775e-13
---
Iteration 9 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 10 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 2: -6.1862955079775e-13
---
Iteration 11 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 12 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 13 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 14 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 15 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 16 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 2: -6.1862955079775e-13
---
Iteration 17 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 2: -6.1862955079775e-13
---
Iteration 18 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 19 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 20 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 21 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 22 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 2: -1.1323762023549e-12
---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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dG 2: -4.8897913712889e-13
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 23 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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StyleLoss:updateOutput self.G 2: 115.33726501465
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---
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dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 24 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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StyleLoss:updateOutput self.G 2: 115.33726501465
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---
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---
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dG 2: -4.8897913712889e-13
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 25 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 26 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 27 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 28 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 29 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 30 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
Iteration 31 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 32 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 33 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
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dG 2: -6.1862955079775e-13
---
Iteration 34 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 35 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 36 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 37 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 38 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 39 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 40 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 41 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 42 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 43 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 44 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 45 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 46 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 47 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 48 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 49 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
Iteration 50 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 51 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 52 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 53 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 54 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 55 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 56 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
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---
Iteration 57 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 58 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
Iteration 59 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
Iteration 60 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 61 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 62 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 63 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 64 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 65 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 66 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 67 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
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---
Iteration 68 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 69 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 70 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 71 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 72 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 73 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 74 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 75 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 76 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 77 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 78 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -0.00011950141924899
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---
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---
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dG 2: -4.8897913712889e-13
---
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 79 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
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---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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dG 2: -4.8897913712889e-13
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 80 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 81 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 82 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 83 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 84 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 85 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 86 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 87 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 88 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 89 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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dG 2: -1.1323762023549e-12
---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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dG 2: -4.8897913712889e-13
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 90 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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StyleLoss:updateOutput self.G 2: 115.33726501465
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---
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dG 2: -1.1323762023549e-12
---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 91 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
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dG 2: -1.1323762023549e-12
---
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StyleLoss:updateOutput self.G 2: 20.841968536377
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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dG 2: -4.8897913712889e-13
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 92 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 93 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 94 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 95 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 96 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 2: -6.1862955079775e-13
---
Iteration 97 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 98 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 99 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 100 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 101 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 102 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 103 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 104 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 105 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 106 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 107 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 2: -6.1862955079775e-13
---
Iteration 108 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 109 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 110 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 111 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 112 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 113 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 114 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 115 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 116 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 117 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 118 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 2: -6.1862955079775e-13
---
Iteration 119 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 120 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 121 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 122 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -0.00011950141924899
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---
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---
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StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 123 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
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---
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---
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dG 2: -4.8897913712889e-13
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 124 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 125 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 126 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 127 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 128 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateOutput self.G 2: 20.841968536377
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 129 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 2: -6.1862955079775e-13
---
Iteration 130 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 131 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 132 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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dG 2: -6.1862955079775e-13
---
Iteration 133 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 134 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 135 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 136 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 137 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 138 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 139 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 140 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 141 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 142 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 143 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
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---
Iteration 144 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 145 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 146 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
Iteration 147 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 148 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 149 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 150 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 151 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 152 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 153 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 154 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 155 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 156 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 157 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
Iteration 158 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 159 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 160 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 161 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 162 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 163 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 164 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 165 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 166 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 167 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 168 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 169 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 170 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 171 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 172 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 173 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 174 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 175 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 176 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 177 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 178 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 179 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
Iteration 180 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 181 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 182 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 183 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 184 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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---
Iteration 185 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
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---
Iteration 186 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
Iteration 187 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
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---
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dG 2: -6.1862955079775e-13
---
Iteration 188 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 1: -6.8364793150977e-06
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---
Iteration 189 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 190 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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dG 2: -6.1862955079775e-13
---
Iteration 191 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
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---
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---
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---
Iteration 192 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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---
Iteration 193 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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dG 2: -6.1862955079775e-13
---
Iteration 194 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6789810657501
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dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 195 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
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feval(x) grad value: 0
---
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StyleLoss:updateOutput self.G 2: 3.6386742591858
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StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 196 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 197 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 198 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808159069944e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 199 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 200 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275223000208e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808131785103e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 201 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
feval(x) grad value: 0
---
StyleLoss:updateOutput self.G 1: 1278823680
StyleLoss:updateOutput self.G 2: 3.6386742591858
StyleLoss:updateGradInput self.gradInput 1: -2.1727835086693e-09
StyleLoss:updateGradInput self.gradInput 2: -1.3036697964708e-05
dG 1: -0.00011950141924899
dG 2: -3.400208445966e-13
---
StyleLoss:updateOutput self.G 1: 20267821056
StyleLoss:updateOutput self.G 2: 115.33726501465
StyleLoss:updateGradInput self.gradInput 1: -4.7330814822999e-09
StyleLoss:updateGradInput self.gradInput 2: -4.0275226638187e-05
dG 1: -0.001294901361689
dG 2: -7.3688399132577e-12
---
StyleLoss:updateOutput self.G 1: 10460779520
StyleLoss:updateOutput self.G 2: 118.95206451416
StyleLoss:updateGradInput self.gradInput 1: -8.5812879024871e-10
StyleLoss:updateGradInput self.gradInput 2: -2.9808145427523e-06
dG 1: -9.958243026631e-05
dG 2: -1.1323762023549e-12
---
StyleLoss:updateOutput self.G 1: 918054848
StyleLoss:updateOutput self.G 2: 20.841968536377
StyleLoss:updateGradInput self.gradInput 1: -1.1896679197321e-08
StyleLoss:updateGradInput self.gradInput 2: -7.4847244832199e-05
dG 1: -2.1538742657867e-05
dG 2: -4.8897913712889e-13
---
StyleLoss:updateOutput self.G 1: 40656444
StyleLoss:updateOutput self.G 2: 3.6789810657501
StyleLoss:updateGradInput self.gradInput 1: -7.1434293147377e-08
StyleLoss:updateGradInput self.gradInput 2: -0.00042860573739745
dG 1: -6.8364793150977e-06
dG 2: -6.1862955079775e-13
---
Iteration 202 / 2500
Content 1 loss: 1994813.281250
Style 1 loss: 2699.189365
Style 2 loss: 2853272.277832
Style 3 loss: 10104018.310547
Style 4 loss: 662611.404419
Style 5 loss: 25619.004250
Total loss: 15643033.467662
---
x1 value: -7.1936044692993
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