Introduction to Tennis W15 Lincoln, NE USA
The Tennis W15 Lincoln tournament in Nebraska is a premier event that attracts top talent from across the globe. This competition, part of the ATP Challenger Tour, offers players the opportunity to earn valuable points and gain exposure on the international stage. With matches updated daily, enthusiasts can stay engaged with the latest developments and expert betting predictions.
Daily Match Updates
Keeping up with the fast-paced world of tennis requires staying informed about the latest match outcomes. The Tennis W15 Lincoln tournament provides daily updates on match results, allowing fans to follow their favorite players' progress throughout the tournament. This continuous flow of information ensures that enthusiasts never miss a beat.
The tournament schedule is meticulously planned to maximize excitement and engagement. Matches are strategically timed to accommodate both local and international audiences, ensuring that fans can watch live or catch up with highlights at their convenience.
Expert Betting Predictions
Betting on tennis matches adds an extra layer of excitement for fans. Expert predictions offer insights into potential outcomes, helping bettors make informed decisions. These predictions are based on a variety of factors, including player form, head-to-head records, and surface preferences.
- Player Form: Current performance levels can significantly impact match outcomes. Experts analyze recent matches to gauge a player's momentum.
- Head-to-Head Records: Historical data on how players have fared against each other provides valuable context for predictions.
- Surface Preferences: Players often have surfaces where they perform better. Understanding these preferences can influence betting strategies.
By combining statistical analysis with expert intuition, predictions aim to offer the most accurate forecasts available. Bettors can use these insights to enhance their experience and potentially increase their chances of success.
Highlighting Key Players
The Tennis W15 Lincoln tournament features a diverse lineup of talented players. Highlighting key participants helps fans focus on those matches that promise the most excitement and skillful play.
- Rising Stars: The tournament is a platform for emerging talents who are looking to make their mark on the professional scene.
- Veterans: Experienced players bring a wealth of skill and strategy, often providing thrilling matches against younger opponents.
- Local Favorites: Homegrown talent often draws significant support from local fans, adding an extra layer of excitement to their matches.
Fans can follow these players closely throughout the tournament, gaining insights into their playing styles and strategies.
Tournament Structure and Format
The structure of the Tennis W15 Lincoln tournament is designed to maximize competitive play and audience engagement. The event typically follows a knockout format, ensuring that each match is crucial for advancing to the next round.
- Main Draw: The main draw consists of seeded players who have qualified based on their rankings and performance in previous tournaments.
- Qualifiers: A separate qualifying round determines which players will join the main draw, providing opportunities for lower-ranked players to compete at a high level.
- Doubles Matches: In addition to singles competitions, doubles matches offer fans a different dynamic and strategic approach to the game.
This format ensures a diverse range of matchups, keeping the tournament fresh and exciting from start to finish.
Engaging with Fans
Engagement is key to building a vibrant community around the Tennis W15 Lincoln tournament. Various platforms allow fans to interact with each other and share their passion for tennis.
- Social Media: Platforms like Twitter, Instagram, and Facebook provide real-time updates and opportunities for fans to discuss matches and share opinions.
- Fan Forums: Dedicated forums offer spaces for in-depth discussions about strategies, player performances, and tournament developments.
- Livestreams and Commentaries: Live broadcasts with expert commentary enhance the viewing experience, offering insights that might not be apparent from just watching the game.
These channels foster a sense of community among fans, making them feel more connected to the tournament and its participants.
The Role of Analytics in Tennis Betting
Analytical tools play a crucial role in modern tennis betting. By leveraging data analytics, experts can provide more accurate predictions and insights into match outcomes.
- Data Collection: Comprehensive data on player performances, match conditions, and historical trends form the basis of analytical models.
- Prediction Models: Advanced algorithms analyze this data to forecast potential match results, helping bettors make informed decisions.
- User Engagement: Analytics also track user behavior on betting platforms, allowing companies to tailor their offerings to better meet fan needs.
The integration of analytics into tennis betting enhances both the accuracy of predictions and the overall user experience.
Cultural Impact of Tennis in Lincoln
Tennis holds significant cultural value in Lincoln, Nebraska. The presence of tournaments like W15 Lincoln not only boosts local interest in the sport but also contributes to the community's cultural landscape.
- Economic Benefits: Hosting international tournaments brings economic advantages through tourism and local business patronage.
- Sporting Culture: The tournament fosters a sporting culture that encourages participation at all levels, from amateur clubs to professional circuits.
- Educational Opportunities: Local schools and universities often engage with the tournament through clinics and workshops, providing educational opportunities for young athletes.
This cultural impact underscores the importance of tennis as more than just a sport; it's a vital part of community identity in Lincoln.
Innovative Features of Tennis W15 Lincoln
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 25
@author: Nicolas Chatain
"""
import os
import time
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from tqdm import tqdm
from utils import AverageMeter
class Trainer:
"""Class used for training"""
def __init__(self,
model,
optimizer,
loss_fn,
scheduler,
criterion_reg,
train_loader,
valid_loader=None,
device='cuda',
log_dir='log',
save_dir='weights',
n_epochs=100,
save_every=10):
"""Initialization
Parameters:
model: network architecture.
optimizer: optimization method.
loss_fn: loss function.
scheduler: learning rate scheduler.
criterion_reg: regularization criterion.
train_loader: dataloader for training set.
valid_loader: dataloader for validation set.
device: device used (default 'cuda').
log_dir: directory used for saving logs (default 'log').
save_dir: directory used for saving weights (default 'weights').
n_epochs: number of epochs (default=100).
save_every: number of epochs between two saves (default=10).
"""
self.model = model
self.optimizer = optimizer
self.loss_fn = loss_fn
self.scheduler = scheduler
self.criterion_reg = criterion_reg
self.train_loader = train_loader
self.valid_loader = valid_loader
self.device = device
self.n_epochs = n_epochs
self.save_every = save_every
# Create directories if they don't exist yet.
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# Store paths.
self.log_path = os.path.join(log_dir,
'train.log')
self.save_path = os.path.join(save_dir,
'weights.pth')
def train(self):
"""Train network"""
print('Start training.')
# Store time at start.
start_time = time.time()
# Create log file.
with open(self.log_path, 'w') as f:
f.write('epochtlosstregttimen')
# Start training.
best_loss = float('inf')
epoch_loss_meter = AverageMeter()
reg_meter = AverageMeter()
reg_best_loss = float('inf')
for epoch in range(self.n_epochs):
print(f'Epoch {epoch +1}/{self.n_epochs}')
# Train epoch.
train_loss_epoch_meter = AverageMeter()
reg_epoch_meter = AverageMeter()
self.model.train()
# Loop over training set.
loop = tqdm(self.train_loader)
for i_batch, sample_batched in enumerate(loop):
inputs = sample_batched['input'].to(self.device)
labels = sample_batched['label'].to(self.device)
# Clear gradients before forward pass.
self.optimizer.zero_grad()
# Forward pass.
outputs = self.model(inputs)
# Compute loss value.
loss_train_value = self.loss_fn(outputs.squeeze(1), labels.squeeze(1))
reg_loss_value = self.criterion_reg(self.model)
loss_train_value += reg_loss_value
# Backward pass.
loss_train_value.backward()
# Update parameters using computed gradients.
self.optimizer.step()
# Compute mean loss value over batch.
train_loss_epoch_meter.update(loss_train_value.item(), inputs.size(0))
reg_epoch_meter.update(reg_loss_value.item(), inputs.size(0))
loop.set_postfix(
train_loss=train_loss_epoch_meter.avg,
reg=reg_epoch_meter.avg)
epoch_loss_meter.update(train_loss_epoch_meter.avg)
reg_meter.update(reg_epoch_meter.avg)
print(f'Train Loss: {epoch_loss_meter.avg:.4f} | Reg Loss : {reg_meter.avg:.4f}')
if epoch % self.save_every ==0:
torch.save({
'epoch': epoch,
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'loss': epoch_loss_meter.avg},
f'{self.save_path[:-4]}_{epoch}.pth')
# if epoch % (self.save_every//4) ==0:
#
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': self.model.state_dict(),
# 'optimizer_state_dict': self.optimizer.state_dict(),
# 'loss': epoch_loss_meter.avg},
# f'{self.save_path[:-4]}_{epoch}.pth')
# if epoch % (self.save_every//4) ==0:
#
# torch.save({
# 'epoch': epoch,
# 'model_state_dict': self.model.state_dict(),
# 'optimizer_state_dict': self.optimizer.state_dict(),
# 'loss': epoch_loss_meter.avg},
# f'{self.save_path[:-4]}_{epoch}.pth')
<|repo_name|>nchatain/PCV<|file_sep See paper https://arxiv.org/pdf/2107.12084.pdf
To download dataset :
https://drive.google.com/file/d/1lS3zqWuL2GjXjRtCZaF8d6nJnIg9xH9b/view?usp=sharing
Then run code :
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug False
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug True<|repo_name|>nchatain/PCV<|file_sep
## Step-by-step guide
### Step1 : Install PyTorch
Follow [PyTorch Installation Guide](https://pytorch.org/get-started/locally/)
### Step2 : Download Dataset
Go [here](https://drive.google.com/file/d/1lS3zqWuL2GjXjRtCZaF8d6nJnIg9xH9b/view?usp=sharing)
Download `dataset.zip` file.
Unzip it.
Move it in `./dataset` folder.
### Step3 : Run Code
Run code :
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug False
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug True
### Step4 : Launch tensorboard
tensorboard --logdir log/exp_1
You should see something like this :
<|repo_name|>nchatain/PCV<|file_sep/library/utils.py
import numpy as np
def compute_ssim(im1, im2):
"""Computes SSIM index between two images."""
#c1=6.5025; c2=58.5225;
c1=0.01**2; c2=0.03**2;
mu1=np.mean(im1); mu2=np.mean(im2);
sigma1=np.var(im1); sigma2=np.var(im2);
sigma12=np.cov(im1.flatten(),im2.flatten())[0][1];
ssim=(2*mu1*mu2+c1)*(2*sigma12+c2)/((mu1**2+mu2**2+c1)*(sigma1+sigma2+c2));
return ssim;
def compute_psnr(im1_imputed , im_ground_truth):
mse=np.mean((im_ground_truth-im1_imputed)**2);
psnr=10*np.log10(255**2/mse);
return psnr;<|repo_name|>nchatain/PCV<|file_sepלקוח ניתן להשתמש בגרסה הבאה של pytorch :
torch==1.7
torchvision==0.8
## מבנה קובץ הקוד:
### library
מארגנת את הפונקציות שיש בשימוש בכל הקוד.
### main.py
מארח את השירות של המודל ומבצע את האימון שלו.
### configs
מארח את קבצי ההגדרה של המודל.
### dataset
מארח את מחלקת המערכת הנתונים שלנו ואת מחלקת טעינת המערכת הנתונים שלנו.
### models
מארח את מחלקות המודל שלנו ואת מחלקת המשימה שלנו.
### trainer
מארח את מחלקת האימון שלנו.
### utils
מארח את פונקציות לעבדה עבור השירות והאימון.
## להתחלה:
### אימון:
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug False
python3 main.py --config config/dataset_3.yml --exp_id exp_1 --gpu_ids "0" --debug True
## להעריכה:
tensorboard --logdir log/exp_1
## עבור לשירות:
python3 service.py
## בעיית מדעי המחשב:
Reconstructing Images using Deep Generative Models with Prior Knowledge.
https://arxiv.org/pdf/2107.12084.pdf
## לעבר את בדיקת הדו-שבועי:
python3 test_eval.py
<|file_sepClassifier:
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
import torch.nn.functional as F
class Classifier:
def __init__(self,class_num,classifier_type='LR',device='cpu'):
if classifier_type=='LR':
self.classifier=LogisticRegression().to(device)
elif classifier_type=='SVM':
self.classifier=SVC().to(device)
else:
raise NotImplementedError
def fit(self,x,y):
self.classifier.fit(x,y)
def predict(self,x):
x=torch.from_numpy(x).float().to('cpu')
preds=self.classifier.predict_proba(x)
return preds[:,1]
def get_params(self):
params=self.classifier.get_params()
return params
class Classifier
