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2. Division stats & predictions

Upcoming Football Thrills in Slovenia's 2nd Division

Tomorrow promises an exhilarating day of football action in Slovenia's 2nd Division, with several matches lined up that are sure to keep fans on the edge of their seats. Whether you're a die-hard supporter or a casual observer, the excitement is palpable as teams battle it out for supremacy on the pitch. This article provides an in-depth look at the matches scheduled for tomorrow, complete with expert betting predictions to enhance your viewing experience.

Match Highlights and Predictions

The Slovenian 2nd Division is known for its intense rivalries and unpredictable outcomes, making each match a thrilling spectacle. Let's dive into the key fixtures scheduled for tomorrow and explore what to expect from each encounter.

1. NK Rudar Velenje vs. NK Maribor B

The clash between NK Rudar Velenje and NK Maribor B is one of the most anticipated matches of the day. Rudar Velenje, known for their solid defense and strategic play, will be looking to maintain their unbeaten streak at home. On the other hand, Maribor B aims to prove their mettle and climb up the league table.

  • Betting Prediction: The odds favor Rudar Velenje due to their strong home advantage. A draw is also a viable outcome given Maribor B's recent form.
  • Key Players: Keep an eye on Rudar's star striker, who has been in exceptional form, and Maribor B's midfield maestro, known for his game-changing passes.

2. FC Domžale II vs. Olimpija Ljubljana II

This match features two teams with contrasting styles: FC Domžale II's aggressive attacking play versus Olimpija Ljubljana II's disciplined defensive tactics. It's set to be a tactical battle with potential for high-scoring moments.

  • Betting Prediction: Expect a closely contested match with both teams having equal chances of securing a win. A high-scoring draw could be on the cards.
  • Key Players: Watch out for Domžale II's winger, whose pace and dribbling skills could be crucial, and Olimpija Ljubljana II's goalkeeper, who has been instrumental in their recent clean sheets.

3. ND Gorica II vs. NK Celje B

ND Gorica II and NK Celje B are set to face off in what promises to be an exciting encounter. Gorica II will rely on their young talents and high-energy play, while Celje B will leverage their experience and tactical discipline.

  • Betting Prediction: A slight edge goes to ND Gorica II due to their impressive home record this season.
  • Key Players: Gorica II's dynamic forward is expected to shine, while Celje B's veteran defender will be key in thwarting their attacks.

4. NK Triglav Kranj vs. NK Krško

In this fixture, NK Triglav Kranj aims to continue their winning streak against a resilient NK Krško side. Both teams have shown great determination throughout the season, making this match a must-watch.

  • Betting Prediction: Triglav Kranj is favored to win, but don't count out Krško pulling off an upset.
  • Key Players: Triglav Kranj's captain is expected to lead from the front, while Krško's playmaker will be pivotal in creating scoring opportunities.

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Tactical Analysis and Team Form

Understanding the tactical nuances and current form of each team can provide valuable insights into how tomorrow's matches might unfold. Let's delve deeper into the strategies that could define these encounters.

NK Rudar Velenje

Under the guidance of their seasoned coach, Rudar Velenje has adopted a counter-attacking style that capitalizes on quick transitions from defense to attack. Their ability to absorb pressure and launch rapid counter-attacks makes them a formidable opponent.

NK Maribor B

Maribor B has been focusing on building a strong midfield presence this season. Their ability to control the tempo of the game through ball retention and precise passing has been a key factor in their recent performances.

FC Domžale II

Known for their high-intensity pressing game, FC Domžale II aims to disrupt their opponents' build-up play early on. Their aggressive approach often results in turnovers that lead to scoring opportunities.

Olimpija Ljubljana II

Olimpija Ljubljana II relies heavily on their solid defensive structure and quick counter-attacks. Their disciplined approach ensures they remain compact at the back while exploiting any gaps left by their opponents.

Betting Insights and Tips

For those interested in placing bets on tomorrow's matches, here are some expert insights and tips to consider:

  • Total Goals Over/Under: Given the attacking prowess of some teams like FC Domžale II and ND Gorica II, betting on 'Over' for total goals might be a wise choice.
  • Bet on Draw No Bet: This option can be particularly lucrative when facing teams with evenly matched records, such as NK Triglav Kranj vs. NK Krško.
  • Half-Time/Full-Time Betting: Analyzing teams' performance trends during different halves can offer an edge in predicting outcomes accurately.
  • Player Prop Bets: Focusing on key players who have been consistently performing well can increase your chances of winning player-specific bets.

Injury Updates and Squad Changes

Staying informed about injury updates and squad changes is crucial for making informed betting decisions. Here are the latest reports:

  • NK Rudar Velenje: Their star midfielder is doubtful due to a hamstring injury but might still make it into the squad.
  • NK Maribor B: They welcome back a key defender from suspension, bolstering their defensive options.
  • FC Domžale II: Their top scorer is fully fit after recovering from a minor ankle sprain.
  • Olimpija Ljubljana II: They face potential absences in midfield due to suspensions from previous matches.

Coupons and Promotions for Bettors

duducec/duducec.github.io<|file_sep|>/_posts/2019-06-19-test.md --- layout: post title: test author: duducec date: 2019-06-19 12:27 categories: [test] tags: [test] --- # test * test1 * test2 * test3<|repo_name|>duducec/duducec.github.io<|file_sep|>/_posts/2020-05-20-pytorch의-BatchNorm과-Inception-v1.md --- layout: post title: pytorch의 BatchNorm과 Inception-v1 author: duducec date: 2020-05-20 23:55 categories: [deep learning] tags: [pytorch] --- 이번 포스팅에서는 pytorch의 BatchNorm과 Inception-v1에 대해서 알아보도록 하겠습니다. ## BatchNorm ### BatchNorm은 무엇인가? Batch Normalization은 2015년 Google Brain의 Sergey Ioffe와 Christian Szegedy가 제안한 신경망을 학습할 때 사용하는 기술입니다. 이 기술은 입력 데이터를 정규화하는 방법으로 특정 레이어의 입력데이터가 특정 분포를 가지도록 변환합니다. ### 왜 BatchNorm을 쓰는가? Batch Normalization은 학습 속도를 높이고 일반화 성능을 높이며 초기화에 덜 민감하게 만들어주기 때문에 쓰입니다. Batch Normalization을 적용하면 학습 속도가 빨라지는 이유는 각 레이어의 입력값 분포를 일정하게 유지하기 때문입니다. 일반적으로 각 레이어는 다음 레이어에게 입력값을 전달하기 전에 입력값을 변형합니다. 변형된 입력값은 다음 레이어의 가중치로 곱해져서 출력값으로 변환됩니다. 만약 각 레이어가 학습 중에 입력값의 평균과 분산을 계속 변화시키면 학습 속도가 매우 느려집니다. 그러나 Batch Normalization은 이런 문제를 해결합니다. 입력값의 평균과 분산을 계산해서 정규화하여 일정하게 유지하기 때문입니다. 또한 일반화 성능을 높이는 이유는 Batch Normalization에서 추가적인 학습 파라미터가 있기 때문입니다. 학습 과정에서 추가적인 파라미터들을 학습하는 것은 모델의 복잡성을 증가시키고 일반화 성능을 높일 수 있습니다. 그리고 Batch Normalization은 초기화에 덜 민감하게 만들어줍니다. Batch Normalization을 사용하지 않으면 초기화 방식에 따라 네트워크 학습이 잘 되거나 안되기 때문입니다. 그러나 Batch Normalization을 사용하면 초기화 방식에 상관없이 잘 학습됩니다. ### Batch Norm의 원리 Batch Norm의 원리는 각 레이어마다 평균(mean)과 분산(variance)를 계산해서 정규화(normalize)합니다. 정규화란 입력값(x)에서 평균(mean)을 뺀 후 분산(variance)으로 나누는 것으로 표현할 수 있습니다. 여기서 주목해야 할 점은 정규화된 값에 스케일(scale)과 오프셋(offset)을 적용한다는 것입니다. 정규화된 값에 스케일(scale)과 오프셋(offset)을 적용하는 이유는 정규화된 값에서 스케일(scale)과 오프셋(offset) 값을 제거할 수 있기 때문입니다. 즉 정규화된 값에서 스케일(scale)과 오프셋(offset) 값을 제거하면 원래 값으로 돌아옵니다. ### Batch Norm 구현 Batch Norm은 pytorch에서 nn.BatchNorm1d(), nn.BatchNorm2d(), nn.BatchNorm3d() 함수로 구현할 수 있습니다. #### pytorch 코드 python import torch.nn as nn # batch norm 1d : 1차원 데이터를 위한 batch norm (편향 허용) bn = nn.BatchNorm1d(num_features=10) # batch norm 2d : 2차원 데이터를 위한 batch norm (편향 허용) bn = nn.BatchNorm2d(num_features=10) # batch norm 3d : 3차원 데이터를 위한 batch norm (편향 허용) bn = nn.BatchNorm3d(num_features=10) 위 코드에서 num_features는 각 배치(batch) 내 데이터 차원 중 가장 마지막 차원입니다. #### Pytorch 코드 설명 nn.BatchNorm1d() 함수로 구현한 batch norm 코드를 설명하겠습니다. python import torch.nn as nn bn = nn.BatchNorm1d(num_features=10) 위 코드에서 num_features=10은 각 배치(batch) 내 데이터 차원 중 가장 마지막 차원입니다. #### 모델 학습 시 batch norm 사용하기 batch norm을 사용하여 모델을 학습할 때는 forward 메서드에서 batch norm 객체를 호출하여 사용합니다. python import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.bn = nn.BatchNorm1d(num_features=10) def forward(self,x): x = self.bn(x) return x #### Pytorch 코드 설명 python import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net,self).__init__() self.bn = nn.BatchNorm1d(num_features=10) def forward(self,x): x = self.bn(x) return x 위 코드에서 num_features=10은 각 배치(batch) 내 데이터 차원 중 가장 마지막 차원입니다. ## Inception-v1 ### Inception-v1은 무엇인가? Inception-v1(초기 명칭 GoogLeNet)은 2014년 Google Brain의 Christian Szegedy 등이 제안한 심층 신경망 아키텍처 입니다. ### 왜 Inception-v1을 쓰는가? Inception-v1은 AlexNet보다 훨씬 많은 파라미터를 가지고 있으며 AlexNet보다 훨씬 복잡합니다. 그럼에도 불구하고 AlexNet보다 좋은 성능을 발휘하며 연산량이 AlexNet보다 적습니다. 그렇다면 왜 Inception-v1을 쓸까요? 이유는 다음과 같습니다. Inception-v1은 AlexNet보다 좋은 성능을 발휘하기 때문입니다. Inception-v1은 AlexNet보다 연산량이 적기 때문에 실시간 인식(real-time recognition)에 좋습니다. ### Inception-v1의 아키텍처 Inception-v1의 아키텍처는 다음과 같습니다. ![img](https://i.imgur.com/EbCtWVQ.png) ![img](https://i.imgur.com/czHlP5Z.png) Inception 모듈(A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z 모듈)의 구조와 연산량(Params), 연산 횟수(MACs; Multiply-Accumulate Operations), 필터(filter), 필터 크기(filter size), 출력 크기(output size), 출력 크기 × 필터 수(output size × number of filters), 출력 크기 × 필터 크기 × 필터 수(output size × filter size × number of filters), 필터 수(number of filters), 필터 크기(filter size), 패딩(padding), 스트라이드(stride)를 나타내고 있습니다. ![img](https://i.imgur.com/Dx8Lisw.png) Inception-v1의 전체 아키텍처를 나타내고 있습니다. ### Inception-v1 구현 Inception-v1은 pytorch에서 google_inception_v1.py 파일로 구현할 수 있습니다. #### google_inception_v1.py 파일 내용 python import torch.nn as nn import torch.nn.functional as F class BasicConv(nn.Module): def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super(BasicConv,self).__init__() self.conv = nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size,stride=stride,padding=padding) self.bn = nn.BatchNorm2d(num_features=out_channels) def forward(self,x): x = self.conv(x) x = self.bn(x) return F.relu(x) class InceptionA(nn.Module): def __init__(self,in_channels): super(InceptionA,self).__init__() self.branch0 = BasicConv(in_channels=in_channels,out_channels=64,kernel_size=1,stride=1,padding=0) self.branch