Ardal South West stats & predictions

Why Choose Ardal for Your Football Fix?

Ardal is not just any location; it's a vibrant hub for football fans in South West Wales. With a rich history and a strong community spirit, Ardal offers an unparalleled experience for anyone looking to immerse themselves in the world of football. Our platform brings you the freshest matches from local leagues and beyond, ensuring you never miss a beat.

• Local Leagues: Stay updated with matches from regional leagues that showcase local talent and passion.
• National and International Matches: Access comprehensive coverage of major football events from around the world.
• Expert Predictions: Benefit from insights and predictions from seasoned analysts who understand the nuances of the game.

Expert Betting Predictions: Your Guide to Success

Betting on football can be both exhilarating and challenging. To help you navigate this landscape, we offer expert betting predictions that are meticulously crafted by professionals with years of experience. Our predictions take into account various factors such as team form, player injuries, historical performance, and more.

Here's how our expert predictions can enhance your betting experience:

• Data-Driven Insights: Our analysts use advanced data analytics to provide accurate predictions.
• Daily Updates: Get fresh predictions every day to keep up with the latest developments in the football world.
• Comprehensive Analysis: Dive deep into each match with detailed analysis and commentary.

Match Highlights: Fresh Content Every Day

Our commitment to providing fresh content every day ensures that you stay informed about all the latest happenings in football. From pre-match build-ups to post-match analyses, we cover every aspect of the game. Here's what you can expect:

• Pre-Match Build-Up: Get ready for each match with expert opinions and key talking points.
• Live Match Updates: Follow live updates as the action unfolds on the pitch.
• Post-Match Analysis: Understand what happened during the match with in-depth analysis and expert commentary.

In-Depth Player and Team Analysis

To truly appreciate the beauty of football, it's essential to understand the players and teams involved. Our platform offers comprehensive analysis of key players and teams, providing insights into their strengths, weaknesses, and potential impact on upcoming matches.

• Player Profiles: Learn about the top players in each league with detailed profiles and statistics.
• Team Strategies: Discover how different teams approach the game with unique strategies and tactics.
• Performance Metrics: Analyze performance metrics to gauge team form and potential outcomes.

Betting Strategies: Maximizing Your Potential

Betting on football requires not just luck but also a well-thought-out strategy. Our platform provides you with tips and strategies to help you make informed decisions. Here are some key strategies to consider:

• Betting on Underdogs: Sometimes, betting on underdogs can yield surprising results. Learn when it might be advantageous to back the less favored team.
• Hedging Bets: Understand how hedging can minimize risks and protect your bankroll.
• Diversifying Bets: Spread your bets across different matches to increase your chances of winning.

The Community Aspect: Connect with Other Fans

Ardal is not just about watching matches; it's about being part of a community. Our platform encourages interaction among fans, allowing you to connect with like-minded individuals who share your passion for football. Engage in discussions, share your thoughts, and become part of a vibrant community that celebrates the sport together.

• Forums and Discussions: Participate in forums where fans discuss matches, share predictions, and exchange views.
• Social Media Integration: Stay connected through our social media channels for real-time updates and interactions.
• User-Generated Content: Contribute your own insights and analyses to enrich the community experience.

The Future of Football Betting in Ardal

The future looks bright for football betting in Ardal. With advancements in technology and increasing interest in sports betting, our platform is poised to lead the way in providing exceptional content and services. Here's what we envision for the future:

• Innovative Technologies: Embrace new technologies such as AI-driven predictions and virtual reality experiences.
• Sustainable Practices:gongweizhe/Deep-learning<|file_sep|>/LeNet5.py import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import torchvision.datasets as dsets from torch.autograd import Variable import matplotlib.pyplot as plt # Hyper Parameters num_epochs = 5 batch_size = 100 learning_rate =0.001 # MNIST Dataset train_dataset = dsets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = dsets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) # Data Loader (Input Pipeline) train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) class LeNet5(nn.Module): def __init__(self): super(LeNet5,self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1,6,kernel_size=5,padding=2), nn.ReLU(), nn.MaxPool2d(kernel_size=2)) self.layer2 = nn.Sequential( nn.Conv2d(6,16,kernel_size=5), nn.ReLU(), nn.MaxPool2d(kernel_size=2)) self.fc1 = nn.Linear(16*7*7,120) self.relu = nn.ReLU() self.fc2 = nn.Linear(120,84) self.fc3 = nn.Linear(84,10) def forward(self,x): out = self.layer1(x) out = self.layer2(out) out = out.view(out.size(0),-1) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) out = self.relu(out) out = self.fc3(out) return out # Set device device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') print(device) # Initialize model model = LeNet5().to(device) # Loss function & Optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(),lr=learning_rate) # Train the Model total_step = len(train_loader) for epoch in range(num_epochs): for i,(images,labels) in enumerate(train_loader): images = images.to(device) labels = labels.to(device) outputs=model(images) loss=criterion(outputs,labels) optimizer.zero_grad() loss.backward() optimizer.step() if (i+1) %100 ==0: print('Epoch [{}/{}], Step [{}/{}], Loss:{:.4f}'.format(epoch+1,num_epochs,i+1,total_step, loss.item())) # Test model accuracy model.eval() with torch.no_grad(): correct=0 total=0 for images , labels in test_loader: labels=labels.to(device) outputs=model(images.to(device)) prediction=torch.max(outputs.data ,1)[1] correct += (prediction==labels).sum().item() print('Accuracy of test images : {} %'.format(100*correct/len(test_dataset))) classes=['T-shirt/top','Trouser','Pullover','Dress','Coat', 'Sandal','Shirt','Sneaker','Bag','Ankle boot'] class_correct=[] class_total=[] for i in range(10): class_correct.append(torch.zeros(1).to(device)) class_total.append(torch.zeros(1).to(device)) with torch.no_grad(): for images , labels in test_loader: labels=labels.to(device) outputs=model(images.to(device)) prediction=torch.max(outputs.data ,1)[1] c=cuda.FloatTensor(prediction==labels) for i in range(batch_size): label=labels[i] class_correct[label]+=c[i] class_total[label]+=1 for i in range(10): print('Accuracy of {} : {:.2f}%'.format(classes[i], class_correct[i]/class_total[i]*100)) plt.bar(classes,class_correct/class_total*100,color='blue') plt.show()<|repo_name|>gongweizhe/Deep-learning<|file_sep|>/README.md # Deep-learning This repository contains my deep learning projects. ### Project list: #### **LeNet5** ##### CNNs - Convolutional Neural Networks #### **AlexNet** ##### CNNs - Convolutional Neural Networks #### **VGG16** ##### CNNs - Convolutional Neural Networks #### **ResNet18** ##### CNNs - Convolutional Neural Networks #### **GAN** ##### GANs - Generative Adversarial Networks #### **BiLSTM** ##### RNNs - Recurrent Neural Networks #### **Transformer** ##### NLP - Natural Language Processing <|repo_name|>gongweizhe/Deep-learning<|file_sep|>/GAN.py import numpy as np import torch import torchvision.datasets as dset import torchvision.transforms as transforms from torch.utils.data import DataLoader from torchvision.utils import save_image from torch.autograd import Variable import matplotlib.pyplot as plt # Hyper Parameters EPOCHS=100 BATCH_SIZE=64 Z_DIM=100 LR_G=0.0002 LR_D=0.0002 BETA1=0.5 BETA2=0.999 # Load Dataset dataset=dset.ImageFolder(root='./data', transform=transforms.Compose([ transforms.Resize((64,64)), transforms.ToTensor(), transforms.Normalize([0.5],[0.5]) ])) dataloader=DataLoader(dataset,batch_size=BATCH_SIZE, shuffle=True,num_workers=int(8)) def weights_init(m): if isinstance(m , nn.ConvTranspose2d) or isinstance(m , nn.Conv2d): m.weight.data.normal_(0.0 , std=.02) class Generator(nn.Module): def __init__(self,z_dim): super().__init__() self.gen=nn.Sequential( # Input : N x Z_DIM x Z_DIM x Z_DIM # Output : N x CH x IMG_DIM/4 x IMG_DIM/4 nn.ConvTranspose2d(Z_DIM , CH*8 , kernel_size=4 , stride=1 , padding=0 , bias=False), nn.BatchNorm2d(CH*8), nn.ReLU(True), # Output : N x CH*8 x IMG_DIM/4 + 3 x IMG_DIM/4 + 3 nn.ConvTranspose2d(CH*8 , CH*4 , kernel_size=4 , stride=2 , padding=1 , bias=False), nn.BatchNorm2d(CH*4), nn.ReLU(True), # Output : N x CH*4 x IMG_DIM/2 + 6 x IMG_DIM/2 +6 nn.ConvTranspose2d(CH*4 , CH*2 , kernel_size=4 , stride=2 , padding=1 , bias=False), nn.BatchNorm2d(CH*2), nn.ReLU(True), # Output : N x CH*2 x IMG_DIM +12 x IMG_DIM +12 nn.ConvTranspose2d(CH*2 , CH , kernel_size=4 , stride=2 , padding=1 , bias=False), nn.BatchNorm2d(CH), nn.ReLU(True), # Output : N x CH x IMG_DIM * 2 +24 x IMG_DIM * 2 +24 nn.ConvTranspose2d(CH , OUT_CH , kernel_size=4 , stride=2 , padding=1 ), # Output : N X OUT_CH X IMG_DIM X IMG_DIM # Sigmoid activation function used because pixel values are normalized between [0-1] nn.Tanh() ) def forward(self,x): return self.gen(x) class Discriminator(nn.Module): def __init__(self): super().__init__() self.dis=torch.nn.Sequential( # Input : N X OUT_CH X IMG_DIM X IMG DIM # Output : N X CH X IMG_DIM / 2 X IMG_DIM / 2 nn.Conv2d(OUT_CH , CH , kernel_size=(4) , stride=(2) , padding=(1)), # Input : N X CH X IMG_DIM / 2 X IMG_DIM / 2 # Output : N X CH X (IMG_DIM / 4) X (IMG DIM /4) nn.LeakyReLU(negative_slope=.02,inplace=True), # Input : N X CH X (IMG DIM /4) X (IMG DIM /4) # Output : N X CH * 2 X (IMG DIM /4) +6 X (IMG DIM /4) +6 nn.Conv2d(CH , CH * (CH//CH) , kernel_size=(4) , stride=(2) , padding=(1)), # Input : N X CH * (CH//CH) X (IMG DIM /4) +6 X (IMG DIM /4)+6 # Output : N X CH * (CH//CH) * (CH//CH) X (IMG DIM /8)+12X(IMG DIM /8)+12 nn.LeakyReLU(negative_slope=.02,inplace=True), # Input : N x CH * (CH//CH) * (CH//CH) x (IMG DIM /8)+12x(IMG DIM /8)+12 # Output : N x CH * ((CH//CH)*CH//CH)* ((CH//CH)*CH//CH) #x ((IMG DIM /16)+24x((IMG DIM /16)+24) nn.Conv2d((CH*(CH//CH)) ,(CH*(CH//CH))*(CH//CH), kernel_size=(4),stride=(2),padding=(1)), # Input :N x CH*(CH//CH)*(CH//CH)x((IMG DIM /16)+24x((IMG DIM /16)+24) #Output: N x ((OUT_CH//(OUT_CH//OUT_CH)))*(OUT_CH//OUT_CH)*((OUT_CH // OUT_CH)) #x ((IMG DIM /32)+48x((IMG DIM /32)+48) nn.LeakyReLU(negative_slope=.02,inplace=True), # Input :Nx ((OUT_CH//(OUT_CH//OUT_CH)))*(OUT_CH//OUT_CH)*((OUT_CH // OUT_CH)) #(x ((IMG DIM /32)+48x((IMG DIM /32)+48) #Output: Nx OUT_CH #x ((IMG DIM /32)+48x((IMG DIM /32)+48) nn.Conv((OUT_CH//(OUT_CH//OUT_CH)) ,(OUT_CH // OUT_CH), kernel_size=(4),stride=(1),padding=(0)), # Output : N X OUT_CH #X ((IMG DIM //32)+48X((IMG DIM //32)+48) ) def forward(self,x): return self.dis(x) G=torch.nn.Sequential( Generator(Z_DIM)).apply(weights_init) D=torch.nn.Sequential( Discriminator()).apply(weights_init) optimG=torch.optim.Adam(G.parameters(),lr=LR_G,betas=[BETA1,BETA2]) optimD=torch.optim.Adam(D.parameters(),lr=LR_D,betas=[BETA1,BETA2]) def weights_init(m): classname=m.__class__.__name__ if classname.find('Conv') !=-1: m.weight.data.normal_(mean=.0,std=.02) def train(): G.train() D.train() for epoch in range(EPOCHS): for batch_idx,data in enumerate(dataloader): real_images=data[0] batch_size_real_images=len(real_images) real_labels=torch.full(size=(batch_size_real_images,) , fill_value=.9,dtype=torch.float32) fake_labels=torch.full(size=(batch_size_real_images,) , fill_value=.01,dtype=torch.float32) real_images_variable=torch.autograd.Variable(real_images).to(device) z=torch.randn(batch_size_real_images,Z_DIM,dtype=torch.float32).to(device) z_variable=torch.autograd.Variable(z).to(device) fake_images=G(z