Super League 1 stats & predictions

Superliga Grega: A Antecipação dos Jogos de Amanhã

A Superliga Grega está prestes a nos entregar mais um dia emocionante de futebol, com partidas que prometem ser cheias de ação e emoção. Neste artigo, vamos explorar os jogos que estão programados para amanhã, além de oferecer algumas previsões de apostas para ajudar os fãs e apostadores a se prepararem para as partidas. Prepare-se para mergulhar no mundo do futebol grego e descobrir quais times podem surpreender a todos.

Calendário dos Jogos

Abaixo está o calendário dos jogos da Superliga Grega que serão disputados amanhã:

• Olympiacos vs PAOK
• Athletic Club Le Pirée vs AEK Athens
• Atromitos vs Panathinaikos
• Aris vs PAE Veria
• OFI Crete vs Apollon Smyrnis

Análise dos Jogos

Dicas dos Expertos para Apostas Especiais

Não se esqueça de considerar os seguintes aspectos ao fazer suas apostas:

Luz sobre os Jogadores Destacados da Rodada

A Superliga Grega conta com talentos individuais que podem decidir o rumo das partidas. Confira alguns dos jogadores mais aguardados desta rodada:

• Kostas Fortounis (Olympiacos): Conhecido por sua habilidade técnica e capacidade goleadora, Fortounis é sempre um jogador a ser observado em campo.
• Pedro Manzi (PAOK): Um atacante versátil que tem se destacado pela sua rapidez e capacidade de finalização.
• Pablo Sarabia (Athletic Club Le Pirée): Com passes precisos e visão excepcional do jogo, Sarabia é peça chave no meio-campo do seu time.
• Nikos Karelis (Panathinaikos): Um zagueiro sólido que lidera a defesa do Panathinaikos com autoridade e experiência.
• Ilias Anastasiou (Aris): Criativo e habilidoso nas costas da defesa adversária, Anastasiou é um jogador fundamental para o esquema tático do Aris.
• Ricardo Gomes (OFI Crete): Um meia-atacante que não hesita em criar oportunidades perigosas para seus companheiros.
• Sotiris Ninis (Apollon Smyrnis): Destaque na criação das jogadas ofensivas do Apollon Smyrnis, Ninis é conhecido por sua inteligência em campo.
• Kostas Galanopoulos (AEK Athens): Um jovem promissor que tem mostrado grande potencial como meia criativo no AEK Athens.
• Konstantinos Laifis (PAE Veria): Um lateral-direito combativo que contribui tanto defensivamente quanto ofensivamente pelo PAE Veria.
• Tomas Kalivoda (OFI Crete): Um atacante experiente cuja presença no ataque do OFI Crete inspira confiança aos companheiros de equipe.karlaguha/MasterThesis<|file_sep|>/src/models.py from torch import nn import torch from torchvision.models.resnet import BasicBlock from torchvision.models.vgg import make_layers import torchvision.models as models class ResNet18(nn.Module): def __init__(self): super(ResNet18,self).__init__() model = models.resnet18(pretrained=True) self.features = nn.Sequential(*list(model.children())[:-1]) for param in self.features.parameters(): param.requires_grad = False self.classifier = nn.Linear(512*7*7 , num_classes) for param in self.classifier.parameters(): param.requires_grad = True def forward(self,x): x = self.features(x) x = x.view(x.size(0),-1) x = self.classifier(x) return x class VGG19(nn.Module): def __init__(self): super(VGG19,self).__init__() features = make_layers(cfg['D']) self.features = nn.Sequential(*features).eval() for param in self.features.parameters(): param.requires_grad = False self.classifier = nn.Sequential( nn.Linear(512*7*7 , num_classes), ) for param in self.classifier.parameters(): param.requires_grad = True def forward(self,x): x = self.features(x) x = x.view(x.size(0),-1) x = self.classifier(x) return x class ResNet18_BN(nn.Module): def __init__(self): super(ResNet18_BN,self).__init__() model = models.resnet18(pretrained=True) # Remove last layer model.fc = nn.Sequential() # Add BN layer before fc layer # model.avgpool = nn.Sequential(nn.BatchNorm2d(512)) # # Add fc layer # model.fc = nn.Linear(512,num_classes) # for param in model.parameters(): # param.requires_grad = False # # Turn on gradients only for fc layer # for param in model.fc.parameters(): # param.requires_grad = True # # Freeze all layers except fc layer and BN layers # for name,param in model.named_parameters(): # if not ("fc" in name or "bn" in name): # param.requires_grad=False # # Define loss function and optimizer # criterion = nn.CrossEntropyLoss() # optimizer_ft= optim.SGD(model.fc.parameters(), lr=0.001,momentum=0.9) self.model=model def weights_init(m): # classname = m.__class__.__name__ # if classname.find('Conv') != -1: # n= m.kernel_size[0]*m.kernel_size[1]*m.out_channels # m.weight.data.normal_(0,std=math.sqrt(2. / n)) # elif classname.find('BatchNorm') != -1: # m.weight.data.fill_(1) # m.bias.data.zero_() if __name__ == "__main__": <|repo_name|>karlaguha/MasterThesis<|file_sep|>/src/data_preprocessing.py import pandas as pd import numpy as np def split_data(df_train): # Shuffle data df_train=df_train.sample(frac=1).reset_index(drop=True) df_train['train_id']=df_train['image_id'].str.split('_').str[0] df_train['label']=df_train['target'] # Split data into train and val set by train_id df_train_split=df_train.groupby(['train_id']).head(60).reset_index(drop=True) df_val_split=df_train.groupby(['train_id']).tail(20).reset_index(drop=True) # Shuffle data again df_train_split=df_train_split.sample(frac=1).reset_index(drop=True) df_val_split=df_val_split.sample(frac=1).reset_index(drop=True) # Drop label column from train set and keep it in val set df_train_split=df_train_split.drop(['target'],axis=1) df_val_split=df_val_split.drop(['label'],axis=1) return df_train_split ,df_val_split def get_fold_data(df_train,n_folds=5,fold=None): if fold is None: df_test=df_train[df_train['fold'].notnull()] else: df_test=df_train[df_train['fold']==fold] df_train=df_train[df_train['fold'].isnull()] df_test['label']=df_test['target'] df_test=df_test.drop(['target','fold'],axis=1) return df_train ,df_test def get_fold_data_old(df,n_folds=5,fold=None): if fold is None: df_test=df[df['fold'].notnull()] else: df_test=df[df['fold']==fold] df_train=df[df['fold'].isnull()] return df_train ,df_test def preprocess_data(df_train): if __name__ == "__main__": <|repo_name|>karlaguha/MasterThesis<|file_sep|>/src/README.md ## Master Thesis Project: ### Predicting genetic mutations from medical images using transfer learning with deep learning algorithms. #### Introduction: In this project we are going to study genetic mutations from medical images using transfer learning with deep learning algorithms. The data is provided by [Luna AI](https://luna-ai.com/challenge/), where we are given a dataset containing patients CT scan images along with their genetic mutations. We will be using this data to predict genetic mutations from CT scans using transfer learning with deep learning algorithms. The project will be done using PyTorch. #### Data: The data is available on [kaggle](https://www.kaggle.com/c/rsna-2019-cervical-spine-fracture-detection/data). It contains two files: * train.csv: Contains information about each patient's CT scan images such as image ID and genetic mutations. * sample_submission.csv: Contains sample submission format for the test set. #### Methodology: We will use transfer learning to fine-tune pre-trained models such as ResNet18 and VGG19 on our dataset to predict genetic mutations from CT scans. First we will preprocess the data by splitting it into train and validation sets based on patient IDs. Then we will create data loaders to load the images into PyTorch tensors. Next we will define our neural network architecture by adding additional layers to the pre-trained models. Finally we will train our model on the train set and evaluate its performance on the validation set. #### Results: After training our models on the dataset we achieved an accuracy of around **80%** on predicting genetic mutations from CT scans. #### Conclusion: In conclusion, transfer learning with deep learning algorithms can be used to predict genetic mutations from medical images such as CT scans with high accuracy. This approach can be applied to other medical imaging tasks such as cancer detection or organ segmentation. #### References: [Luna AI Challenge](https://luna-ai.com/challenge/) [Kaggle Competition](https://www.kaggle.com/c/rsna-2019-cervical-spine-fracture-detection/data) [PyTorch Documentation](https://pytorch.org/docs/stable/index.html) <|repo_name|>karlaguha/MasterThesis<|file_sep|>/src/main.py import argparse import torch.nn.functional as F import torch.nn as nn from torchvision import transforms from torch.utils.data import DataLoader from torch.optim import Adam from sklearn.metrics import accuracy_score import pandas as pd import cv2 from data_loader import CervicalDataset from models import ResNet18,VGG19 def train(model,criterion,dataloader, optimizer,scheduler=None,num_epochs=25,is_inception=False): for epoch in range(num_epochs): def validate(model,criterion,dataloader): if __name__ == "__main__": <|file_sep|># MasterThesis ## Introduction: In this project we are going to study genetic mutations from medical