Commit 8ed37647 authored by uoega's avatar uoega
Browse files

latex paper update

parent 906df2aa
......@@ -7,6 +7,37 @@ import torch.optim as optim
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR
"""
batch_size = 5
seq_len = 10
embed_dim = 2
value_dim = 12
num_heads = 1
query = torch.randn(seq_len, batch_size,embed_dim)
key = torch.randn(seq_len, batch_size,embed_dim)
value = torch.randn(seq_len, batch_size,embed_dim)
mha = nn.MultiheadAttention(embed_dim, num_heads)
attn_out,_ = mha(query, key, value)
attn_out = attn_out.permute(1,0,2)
m = nn.BatchNorm1d(3, affine=False)
input = torch.tensor([[1., 2., 3.], [1., 2., 3.], [1., 2., 3.], [1., 2., 3.]]) #batchsize,channels 4,3
print(input.shape)
print(input)
output = m(input)
print(output)
m = nn.BatchNorm1d(3, affine=False)
input = torch.tensor([[1., 1., 1.], [2., 2., 2.], [3., 3., 3.], [4., 4., 4.]]) #batchsize,channels 4,3
print(input.shape)
print(input)
output = m(input)
print(output)
exit()
"""
class Net(nn.Module):
......@@ -14,8 +45,10 @@ class Net(nn.Module):
super().__init__()
self.fc1 = nn.Linear(256, 256)
self.fc2 = nn.Linear(256, 40)
self.bn = nn.BatchNorm1d(256,affine=True)
def forward(self, x):
x = self.bn(x)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
......@@ -23,15 +56,71 @@ class Net(nn.Module):
class Net2(nn.Module):
def __init__(self):
super().__init__()
self.bn = nn.BatchNorm1d(256,affine=False)
self.fc1 = nn.Linear(256, 40)
def forward(self, x):
x = self.bn(x)
x = self.fc1(x)
return x
class Net_attention(nn.Module):
def __init__(self):
super().__init__()
self.self_attn = nn.MultiheadAttention(1, 1)
self.fc1 = nn.Linear(256, 40)
def forward(self, x):
x = x.view(-1, 256, 1) #batchsize, features, embedding_size
x = x.permute(1,0,2) #features, batchsize, embedding_size
x,_ = self.self_attn(x, x, x) #features, batchsize, embedding_size
x = x.permute(1,0,2) #batchsize, features, embedding_size
x = x.view(-1,256) #batchsize, features
x = self.fc1(x)
return x
class Net_attention2(nn.Module):
def __init__(self):
super().__init__()
self.self_attn = nn.MultiheadAttention(256, 1, dropout=0.5)
self.fc1 = nn.Linear(256, 40)
def forward(self, x):
x = x.view(-1, 1, 256) #batchsize, features, embedding_size
x = x.permute(1,0,2) #features, batchsize, embedding_size
x,_ = self.self_attn(x, x, x) #features, batchsize, embedding_size
x = x.permute(1,0,2) #batchsize, features, embedding_size
x = x.view(-1,256) #batchsize, features
x = self.fc1(x)
return x
class Net_attention3(nn.Module):
def __init__(self):
super().__init__()
self.self_attn = nn.MultiheadAttention(64, 1)
self.fc1 = nn.Linear(256, 40)
def forward(self, x):
x = x.view(-1, 4, 64) #batchsize, features, embedding_size
x = x.permute(1,0,2) #features, batchsize, embedding_size
x,_ = self.self_attn(x, x, x) #features, batchsize, embedding_size
x = x.permute(1,0,2) #batchsize, features, embedding_size
x = torch.flatten(x,start_dim=1) #batchsize, features
#print(x.shape)
x = self.fc1(x)
return x
def main():
# init weights with seed
print("init seed")
seed = 23
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(seed)
#init CUDA
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
......@@ -71,7 +160,7 @@ def main():
train_data = TensorDataset(train_features,train_label)
val_data = TensorDataset(val_features,val_label)
trainloader = DataLoader(train_data, batch_size=256,shuffle=True)
trainloader = DataLoader(train_data, batch_size=128,shuffle=True)
testloader = DataLoader(val_data, batch_size=32,shuffle=False)
#load net for valdiation
......@@ -83,22 +172,26 @@ def main():
net = Net().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(net.parameters(), lr=0.01)
#optimizer = optim.Adam(net.parameters(), lr=0.0001)
#scheduler = StepLR(optimizer,step_size=500,gamma=0.5)
#optimizer = torch.optim.Adagrad(net.parameters(), lr=0.01, lr_decay=0.0, weight_decay=0, initial_accumulator_value=0, eps=1e-10)
optimizer = torch.optim.Adagrad(net.parameters(), lr=0.01, lr_decay=0.0, weight_decay=0, initial_accumulator_value=0, eps=1e-10)
#optimizer = torch.optim.Adadelta(net.parameters(), lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)
class_accuracy = 0
print("Dimension of trainable parameters: ")
for parameter in net.parameters():
print(parameter.shape)
#print(parameter)
print("start training...")
for epoch in range(2000): # loop over the dataset multiple times
net.train()
running_loss = 0.0
for i, data in enumerate(trainloader):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
......@@ -112,9 +205,12 @@ def main():
# print statistics
if epoch % 10 == 9: # print every 10 epochs
print('[%d] loss: %.3f' % (epoch + 1, loss.item()))
#for parameter in net.parameters():
# print(parameter)
if epoch % 100 == 99:
if epoch % 10 == 9:
net.eval()
correct = 0
total = 0
# since we're not training, we don't need to calculate the gradients for our outputs
......@@ -130,7 +226,7 @@ def main():
print('Accuracy: %.2f %%' % (100 * correct / total))
if epoch % 500 == 499:
if epoch % 100 == 99:
# prepare to count predictions for each class
correct_pred = np.zeros(40)
total_pred = np.zeros(40)
......
......@@ -44,11 +44,34 @@
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers and Iryna Gurevych},
year={2019},
jornal={arXiv:1908.10084},
journal={arXiv:1908.10084},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{duan2021revisiting,
title={Revisiting Skeleton-based Action Recognition},
author={Haodong Duan and Yue Zhao and Kai Chen and Dian Shao and Dahua Lin and Bo Dai},
year={2021},
journal={arXiv:2104.13586},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@article{Liu_2020,
title={NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding},
volume={42},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2916873},
DOI={10.1109/tpami.2019.2916873},
number={10},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Liu, Jun and Shahroudy, Amir and Perez, Mauricio and Wang, Gang and Duan, Ling-Yu and Kot, Alex C.},
year={2020},
month={Oct},
pages={2684–2701}
}
@misc{Authors14,
author = {Authors},
......
......@@ -16,43 +16,54 @@
\gdef\HyperFirstAtBeginDocument#1{#1}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\citation{duan2021revisiting}
\citation{Liu_2020}
\citation{jasani2019skeleton}
\citation{reimers2019sentencebert}
\@writefile{toc}{\contentsline {section}{\numberline {1}\hskip -1em.\nobreakspace {}Introduction}{1}{section.1}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}\hskip -1em.\nobreakspace {}Zero-shot learning}{1}{subsection.1.1}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.2}\hskip -1em.\nobreakspace {}Skeleton-based visual recognition}{1}{subsection.1.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}\hskip -1em.\nobreakspace {}Data augmentation}{1}{subsection.1.3}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}\hskip -1em.\nobreakspace {}Related work}{1}{subsection.1.3}}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Architecture of the network.}}{2}{figure.1}}
\newlabel{fig:long}{{1}{2}{Architecture of the network}{figure.1}{}}
\newlabel{fig:onecol}{{1}{2}{Architecture of the network}{figure.1}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.4}\hskip -1em.\nobreakspace {}Data augmentation}{2}{subsection.1.4}}
\@writefile{toc}{\contentsline {section}{\numberline {2}\hskip -1em.\nobreakspace {}Method}{2}{section.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}\hskip -1em.\nobreakspace {}Architecture}{2}{subsection.2.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.1}Visual path}{2}{subsubsection.2.1.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.2}Semantic Path}{2}{subsubsection.2.1.2}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.3}Similarity-Learning-Part}{2}{subsubsection.2.1.3}}
\citation{liu2019roberta}
\citation{ma2019nlpaug}
\@writefile{toc}{\contentsline {section}{\numberline {2}\hskip -1em.\nobreakspace {}Method}{2}{section.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}\hskip -1em.\nobreakspace {}Augmentations}{2}{subsection.2.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.1}Automatic Augmentation}{2}{subsubsection.2.1.1}}
\citation{jasani2019skeleton}
\citation{sung2018learning}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Architecture or other needed for method}}{3}{figure.1}}
\newlabel{fig:long}{{1}{3}{Architecture or other needed for method}{figure.1}{}}
\newlabel{fig:onecol}{{1}{3}{Architecture or other needed for method}{figure.1}{}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Unseen top-1 and top-5 accuracies results in detail.}}{3}{table.1}}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces aug example1}}{3}{figure.2}}
\newlabel{fig:long}{{2}{3}{aug example1}{figure.2}{}}
\newlabel{fig:onecol}{{2}{3}{aug example1}{figure.2}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces aug example2}}{3}{figure.3}}
\newlabel{fig:long}{{3}{3}{aug example2}{figure.3}{}}
\newlabel{fig:onecol}{{3}{3}{aug example2}{figure.3}{}}
\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces ZSL and GZSL results for different approaches.}}{3}{table.2}}
\newlabel{tab:ZSL_GZSL}{{2}{3}{ZSL and GZSL results for different approaches}{table.2}{}}
\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Unseen top-1 and top-5 accuracies results in detail.}}{3}{table.3}}
\newlabel{tab:top1_top5}{{3}{3}{Unseen top-1 and top-5 accuracies results in detail}{table.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}\hskip -1em.\nobreakspace {}Experiments}{3}{subsection.2.2}}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Three descriptive labels for class "Squat down".}}{3}{table.1}}
\newlabel{tab:multi_label}{{1}{3}{Three descriptive labels for class "Squat down"}{table.1}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}\hskip -1em.\nobreakspace {}Augmentation}{3}{subsection.2.2}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.1}Descriptive labels}{3}{subsubsection.2.2.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.2}Multiple labels per class}{3}{subsubsection.2.2.2}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.3}Automatic augmentation}{3}{subsubsection.2.2.3}}
\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Descriptive label and two automatic augmentations for "Squat down".}}{3}{table.2}}
\newlabel{tab:auto_aug}{{2}{3}{Descriptive label and two automatic augmentations for "Squat down"}{table.2}{}}
\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces ZSL and GZSL results for different approaches.}}{3}{table.3}}
\newlabel{tab:ZSL_GZSL}{{3}{3}{ZSL and GZSL results for different approaches}{table.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}\hskip -1em.\nobreakspace {}Experiments}{3}{subsection.2.3}}
\citation{jasani2019skeleton}
\citation{ma2019nlpaug}
\bibstyle{ieee_fullname}
\bibdata{egbib}
\bibcite{jasani2019skeleton}{1}
\@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Unseen top-1 and top-5 accuracies in detail.}}{4}{table.4}}
\newlabel{tab:top1_top5}{{4}{4}{Unseen top-1 and top-5 accuracies in detail}{table.4}{}}
\@writefile{toc}{\contentsline {section}{\numberline {3}\hskip -1em.\nobreakspace {}Results}{4}{section.3}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}\hskip -1em.\nobreakspace {}Discussion}{4}{subsection.3.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.1}From default to descriptive labels}{4}{subsubsection.3.1.1}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2}Using multiple labels}{4}{subsubsection.3.1.2}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.3}Automatic augmentation}{4}{subsubsection.3.1.3}}
\@writefile{toc}{\contentsline {section}{\numberline {4}\hskip -1em.\nobreakspace {}Conclusion}{4}{section.4}}
\bibcite{liu2019roberta}{2}
\bibcite{ma2019nlpaug}{3}
\bibcite{sung2018learning}{4}
\bibstyle{ieee_fullname}
\bibdata{egbib}
\bibcite{duan2021revisiting}{1}
\bibcite{jasani2019skeleton}{2}
\bibcite{Liu_2020}{3}
\bibcite{liu2019roberta}{4}
\bibcite{ma2019nlpaug}{5}
\bibcite{reimers2019sentencebert}{6}
\bibcite{sung2018learning}{7}
\begin{thebibliography}{1}\itemsep=-1pt
\bibitem{duan2021revisiting}
Haodong Duan, Yue Zhao, Kai Chen, Dian Shao, Dahua Lin, and Bo Dai.
\newblock Revisiting skeleton-based action recognition.
\newblock {\em arXiv:2104.13586}, 2021.
\bibitem{jasani2019skeleton}
Bhavan Jasani and Afshaan Mazagonwalla.
\newblock Skeleton based zero shot action recognition in joint pose-language
semantic space.
\newblock {\em arXiv:1911.11344}, 2019.
\bibitem{Liu_2020}
Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex~C.
Kot.
\newblock Ntu rgb+d 120: A large-scale benchmark for 3d human activity
understanding.
\newblock {\em IEEE Transactions on Pattern Analysis and Machine Intelligence},
42(10):2684–2701, Oct 2020.
\bibitem{liu2019roberta}
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer
Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov.
......@@ -17,6 +30,11 @@ Edward Ma.
\newblock Nlp augmentation.
\newblock https://github.com/makcedward/nlpaug, 2019.
\bibitem{reimers2019sentencebert}
Nils Reimers and Iryna Gurevych.
\newblock Sentence-bert: Sentence embeddings using siamese bert-networks.
\newblock {\em arXiv:1908.10084}, 2019.
\bibitem{sung2018learning}
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.~S. Torr, and
Timothy~M. Hospedales.
......
This is pdfTeX, Version 3.14159265-2.6-1.40.19 (MiKTeX 2.9.6840 64-bit) (preloaded format=pdflatex 2018.10.16) 24 JUL 2021 22:08
This is pdfTeX, Version 3.14159265-2.6-1.40.19 (MiKTeX 2.9.6840 64-bit) (preloaded format=pdflatex 2018.10.16) 25 JUL 2021 15:41
entering extended mode
**./paper_working_design.tex
(paper_working_design.tex
......@@ -285,20 +285,7 @@ Package uniquecounter Info: New unique counter `rerunfilecheck' on input line 2
82.
)
\Hy@SectionHShift=\skip48
) (paper_working_design.aux
LaTeX Warning: Label `fig:long' multiply defined.
LaTeX Warning: Label `fig:onecol' multiply defined.
LaTeX Warning: Label `fig:long' multiply defined.
LaTeX Warning: Label `fig:onecol' multiply defined.
)
) (paper_working_design.aux)
\openout1 = `paper_working_design.aux'.
LaTeX Font Info: Checking defaults for OML/cmm/m/it on input line 25.
......@@ -316,7 +303,8 @@ LaTeX Font Info: ... okay on input line 25.
LaTeX Font Info: Checking defaults for PD1/pdf/m/n on input line 25.
LaTeX Font Info: ... okay on input line 25.
LaTeX Font Info: Try loading font information for OT1+ptm on input line 25.
("C:\Users\XPS15\AppData\Local\Programs\MiKTeX 2.9\tex\latex\psnfss\ot1ptm.fd"
("C:\Users\XPS15\AppData\Local\Programs\MiKTeX 2.9\tex\latex\psnfss\ot1ptm.fd"
File: ot1ptm.fd 2001/06/04 font definitions for OT1/ptm.
) ABD: EveryShipout initializing macros
("C:\Users\XPS15\AppData\Local\Programs\MiKTeX 2.9\tex\latex\graphics\color.sty
......@@ -393,136 +381,65 @@ File: ot1pcr.fd 2001/06/04 font definitions for OT1/pcr.
)
LaTeX Font Info: Font shape `OT1/ptm/bx/n' in size <12> not available
(Font) Font shape `OT1/ptm/b/n' tried instead on input line 52.
[1{C:/Users/XPS15/AppData/Local/MiKTeX/2.9/pdftex/config/pdftex.map}
]
Underfull \hbox (badness 4859) in paragraph at lines 78--79
[]\OT1/ptm/m/n/10 Die gew[]ahlte Ar-chitek-tur f[]ur un-sere Ex-per-i-mente
[]
Underfull \hbox (badness 10000) in paragraph at lines 72--75
Underfull \hbox (badness 1038) in paragraph at lines 78--79
\OT1/ptm/m/n/10 seinen einzel-nen Mod-ulen zusam-menge-baut. Einzelne
[]
[1{C:/Users/XPS15/AppData/Local/MiKTeX/2.9/pdftex/config/pdftex.map}
Underfull \hbox (badness 10000) in paragraph at lines 78--79
[]
]
<Architektur2.png, id=24, 885.6839pt x 440.77171pt>
File: Architektur2.png Graphic file (type png)
<use Architektur2.png>
Package pdftex.def Info: Architektur2.png used on input line 87.
(pdftex.def) Requested size: 213.4209pt x 106.21107pt.
LaTeX Font Info: Font shape `OT1/ptm/bx/n' in size <10> not available
(Font) Font shape `OT1/ptm/b/n' tried instead on input line 82.
Underfull \hbox (badness 10000) in paragraph at lines 80--85
[]
Underfull \hbox (badness 10000) in paragraph at lines 87--92
[]
Underfull \hbox (badness 1062) in paragraph at lines 94--100
\OT1/ptm/m/n/10 ab-u-lar, d.h. alle m[]oglichen Klassen-la-bels, in ein se-
[]
Underfull \hbox (badness 1127) in paragraph at lines 94--100
\OT1/ptm/m/n/10 die Ab-bil-dung der se-man-tis-chen Merk-male in den vi-
[]
Underfull \hbox (badness 1168) in paragraph at lines 94--100
\OT1/ptm/m/n/10 Net (RN), das im fol-gen-den Ab-schnitt n[]aher erl[]autert
[]
<Architektur.png, id=16, 817.527pt x 418.509pt>
File: Architektur.png Graphic file (type png)
<use Architektur.png>
Package pdftex.def Info: Architektur.png used on input line 104.
(pdftex.def) Requested size: 189.70947pt x 97.11714pt.
Underfull \hbox (badness 4181) in paragraph at lines 113--114
(Font) Font shape `OT1/ptm/b/n' tried instead on input line 98.
[2 <./Architektur2.png>]
Underfull \hbox (badness 4181) in paragraph at lines 137--139
\OT1/ptm/m/n/10 To re-duce the man-ual an-no-ta-tion ef-fort, we would
[]
Underfull \hbox (badness 6477) in paragraph at lines 113--114
Underfull \hbox (badness 6477) in paragraph at lines 137--139
\OT1/ptm/m/n/10 like to gen-er-ate ad-di-tional la-bels au-to-mat-i-cally for
[]
Underfull \hbox (badness 1888) in paragraph at lines 113--114
Underfull \hbox (badness 1888) in paragraph at lines 137--139
\OT1/ptm/m/n/10 the multi la-bel ap-proach. There-for we're us-ing the
[]
Underfull \hbox (badness 10000) in paragraph at lines 113--114
[]
[2]
Underfull \hbox (badness 10000) in paragraph at lines 115--119
[]
Underfull \hbox (badness 10000) in paragraph at lines 121--125
[]
Overfull \hbox (16.13214pt too wide) in paragraph at lines 129--139
[]
[]
<aug_example1.png, id=27, 1195.08984pt x 126.4725pt>
File: aug_example1.png Graphic file (type png)
<use aug_example1.png>
Package pdftex.def Info: aug_example1.png used on input line 146.
(pdftex.def) Requested size: 213.4209pt x 22.58458pt.
<aug_example2.png, id=28, 888.31876pt x 242.78203pt>
File: aug_example2.png Graphic file (type png)
<use aug_example2.png>
Package pdftex.def Info: aug_example2.png used on input line 156.
(pdftex.def) Requested size: 213.4209pt x 58.32814pt.
Underfull \vbox (badness 5711) has occurred while \output is active []
[3 <./Architektur.png> <./aug_example1.png> <./aug_example2.png>]
Underfull \vbox (badness 6412) has occurred while \output is active []
Underfull \vbox (badness 10000) has occurred while \output is active []
(paper_working_design.bbl [4]
Underfull \hbox (badness 10000) in paragraph at lines 16--19
[3]
[4] (paper_working_design.bbl
Underfull \hbox (badness 10000) in paragraph at lines 29--32
[]\OT1/ptm/m/n/9 Edward Ma. Nlp aug-men-ta-tion.
[]
)
Package atveryend Info: Empty hook `BeforeClearDocument' on input line 239.
Package atveryend Info: Empty hook `BeforeClearDocument' on input line 235.
[5
]
Package atveryend Info: Empty hook `AfterLastShipout' on input line 239.
Package atveryend Info: Empty hook `AfterLastShipout' on input line 235.
(paper_working_design.aux)
Package atveryend Info: Executing hook `AtVeryEndDocument' on input line 239.
Package atveryend Info: Empty hook `AtEndAfterFileList' on input line 239.
LaTeX Warning: There were multiply-defined labels.
Package atveryend Info: Empty hook `AtVeryVeryEnd' on input line 239.
Package atveryend Info: Executing hook `AtVeryEndDocument' on input line 235.
Package atveryend Info: Empty hook `AtEndAfterFileList' on input line 235.
Package atveryend Info: Empty hook `AtVeryVeryEnd' on input line 235.
)
Here is how much of TeX's memory you used:
6261 strings out of 492970
92074 string characters out of 3126593
189874 words of memory out of 3000000
10014 multiletter control sequences out of 15000+200000
92105 string characters out of 3126593
192127 words of memory out of 3000000
10007 multiletter control sequences out of 15000+200000
29095 words of font info for 69 fonts, out of 3000000 for 9000
1141 hyphenation exceptions out of 8191
32i,13n,27p,1165b,468s stack positions out of 5000i,500n,10000p,200000b,50000s
32i,13n,27p,1294b,324s stack positions out of 5000i,500n,10000p,200000b,50000s
{C:/Users/XPS15/AppData/Local/Programs/MiKTeX 2.9/fonts/enc/dvips/base/8r.enc
}<C:/Users/XPS15/AppData/Local/Programs/MiKTeX 2.9/fonts/type1/public/amsfonts/
cm/cmmi10.pfb><C:/Users/XPS15/AppData/Local/Programs/MiKTeX 2.9/fonts/type1/pub
......@@ -532,9 +449,9 @@ iKTeX 2.9/fonts/type1/urw/courier/ucrr8a.pfb><C:/Users/XPS15/AppData/Local/Prog
rams/MiKTeX 2.9/fonts/type1/urw/times/utmb8a.pfb><C:/Users/XPS15/AppData/Local/
Programs/MiKTeX 2.9/fonts/type1/urw/times/utmr8a.pfb><C:/Users/XPS15/AppData/Lo
cal/Programs/MiKTeX 2.9/fonts/type1/urw/times/utmri8a.pfb>
Output written on paper_working_design.pdf (5 pages, 557661 bytes).
Output written on paper_working_design.pdf (5 pages, 243662 bytes).
PDF statistics:
102 PDF objects out of 1000 (max. 8388607)
31 named destinations out of 1000 (max. 500000)
16 words of extra memory for PDF output out of 10000 (max. 10000000)
116 PDF objects out of 1000 (max. 8388607)
40 named destinations out of 1000 (max. 500000)
6 words of extra memory for PDF output out of 10000 (max. 10000000)
This diff is collapsed.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment