Overview of the Tennis Challenger Orleans France
The Tennis Challenger Orleans France is set to showcase some of the most promising talents in the tennis world. Scheduled for tomorrow, this event promises thrilling matches and strategic gameplay. With a lineup of seasoned players and rising stars, the tournament is a must-watch for tennis enthusiasts and bettors alike. In this guide, we delve into the matchups, provide expert betting predictions, and highlight key players to watch.
Match Schedule for Tomorrow
Tomorrow's schedule is packed with exciting matches. The tournament kicks off with early morning sessions and continues into the evening, offering fans ample opportunities to catch their favorite players in action.
Morning Matches
- Match 1: Player A vs. Player B
- Match 2: Player C vs. Player D
- Match 3: Player E vs. Player F
Afternoon Matches
- Match 4: Player G vs. Player H
- Match 5: Player I vs. Player J
- Match 6: Player K vs. Player L
Evening Matches
- Semifinal 1: Winner of Match 1 vs. Winner of Match 4
- Semifinal 2: Winner of Match 2 vs. Winner of Match 5
- Final: Winners of Semifinals
Key Players to Watch
The tournament features several standout players known for their exceptional skills and competitive spirit.
Rising Stars
- Player M: Known for powerful serves and aggressive baseline play.
- Player N: Aces the court with exceptional agility and quick reflexes.
Veteran Competitors
- Player O: Brings years of experience and strategic gameplay.
- Player P: Renowned for consistency and mental toughness.
Betting Predictions for Tomorrow's Matches
Betting enthusiasts can look forward to some exciting opportunities as experts weigh in on tomorrow's matches. Here are some predictions based on current form, head-to-head statistics, and player conditions.
Morning Matches Predictions
- Match 1: Player A vs. Player B
Prediction: Player A to win. Rationale: Strong performance on clay courts and recent winning streak.
- Match 2: Player C vs. Player D
Prediction: Close match, but Player D edges out with better serve accuracy.
- Match 3: Player E vs. Player F
Prediction: Player F to win. Rationale: Superior baseline rallies and recent form.
Afternoon Matches Predictions
- Match 4: Player G vs. Player H
Prediction: Player G to win. Rationale: Consistent performance under pressure.
- Match 5: Player I vs. Player J
Prediction: Upset alert! Player J predicted to win due to strong return game.
- Match 6: Player K vs. Player L
Prediction: Tight contest, but Player K's experience gives them the edge.
Semifinals and Final Predictions
- Semifinal 1: Winner of Match 1 vs. Winner of Match 4
Prediction: Winner of Match 1 expected to advance due to powerful serves.
- Semifinal 2: Winner of Match 2 vs. Winner of Match 5
Prediction: Winner of Match 5 predicted to advance with strong baseline play.
- Final: Winners of Semifinals
Prediction: Winner of Semifinal 1 favored due to consistent performance throughout the tournament.
Tournament Highlights and Insights
The Tennis Challenger Orleans France not only offers thrilling matches but also provides insights into the future stars of tennis. Here are some highlights and insights from today's sessions that could impact tomorrow's outcomes.
Tactical Analysis
Analyzing today's matches reveals several tactical trends that players might employ tomorrow:
- Serving Strategies: Players are focusing on first-serve accuracy to gain early advantage in rallies.
- Baseline Dominance: Consistent baseline play is proving crucial in controlling the tempo of matches.
- Mental Fortitude: Mental toughness is a deciding factor, especially in tight sets.
Injury Reports and Conditions
Injuries can significantly impact match outcomes. Here are updates on key players' conditions:
- Player A: Minor ankle issue but expected to compete fully tomorrow.
- Player D: Fully recovered from yesterday's muscle strain, ready for action.
Fan Engagement and Viewing Options
Fans have multiple ways to engage with tomorrow's matches, whether attending in person or watching remotely.
In-Person Viewing Tips
- Arrive early to secure good seats and explore the venue facilities.
- Come prepared with essentials like sunscreen, water, and comfortable seating options for long sessions.
Digital Viewing Options
- Tune into live streams through official tournament channels for high-quality coverage.
- Social media platforms will provide real-time updates and fan interactions during matches.
Daily Updates and News Coverage
To stay informed about last-minute changes or exciting developments during tomorrow's matches, follow these sources for daily updates:
- Tournament's official website for schedules, player interviews, and news releases.
- Sports news outlets providing expert commentary and analysis throughout the day.
- Twitter handles dedicated to live match updates and fan reactions using relevant hashtags like #TennisChallengerOrleans2024.
Affiliated Betting Platforms Recommendations
koji-kimura/CoTe<|file_sep|>/CoTe/Trainer.py
import os
import time
import torch
import numpy as np
from torch import nn
from tqdm import tqdm
from CoTe.utils import EarlyStopping
class Trainer:
def __init__(self, model,
train_loader,
val_loader,
criterion,
optimizer,
device,
scheduler=None,
model_path=None):
self.model = model
self.train_loader = train_loader
self.val_loader = val_loader
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.device = device
if model_path is None:
self.model_path = 'model.pt'
else:
self.model_path = model_path
def train(self,
epochs=10,
early_stop_patience=10):
best_loss = float('inf')
stop_step = -1
# Early stopping handler.
early_stopping_handler = EarlyStopping(patience=early_stop_patience)
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
train_loss = self._train()
print(f'Train loss {train_loss:.4f}')
val_loss = self._validate()
print(f'Validation loss {val_loss:.4f}')
if val_loss <= best_loss:
best_loss = val_loss
stop_step = -1
print('Best model found! Saving...')
torch.save(self.model.state_dict(), self.model_path)
else:
stop_step += 1
if self.scheduler is not None:
if isinstance(self.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
self.scheduler.step(val_loss)
else:
self.scheduler.step()
# Early stopping check.
early_stopping_handler(val_loss)
if early_stopping_handler.should_stop:
print(f'Stopped after {epoch + early_stop_patience} epochs!')
break
def _train(self):
total_loss = []
batch_size = len(self.train_loader.dataset) // len(self.train_loader)
# Train mode.
self.model.train()
# Progress bar.
pbar = tqdm(total=len(self.train_loader.dataset), desc='Train', unit='img')
start_time = time.time()
# Iterate over data.
for inputs, labels in self.train_loader:
inputs = inputs.to(self.device)
labels = labels.to(self.device)
# Forward pass.
outputs = self.model(inputs)
loss = self.criterion(outputs, labels)
# Backward pass.
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
total_loss.append(loss.item())
pbar.set_postfix(**{'loss (batch)': loss.item()})
pbar.update(inputs.shape[0])
pbar.close()
end_time = time.time()
epoch_time = end_time - start_time
epoch_loss = np.mean(total_loss)
print(f'Training time {epoch_time:.0f}s ({epoch_time * batch_size / (1024 * 1024): .0f} MB/s)')
return epoch_loss
def _validate(self):
total_loss = []
# Validation mode.
self.model.eval()
# Progress bar.
pbar = tqdm(total=len(self.val_loader.dataset), desc='Validate', unit='img')
with torch.no_grad():
# Iterate over data.
for inputs, labels in self.val_loader:
inputs = inputs.to(self.device)
labels = labels.to(self.device)
outputs = self.model(inputs)
loss = self.criterion(outputs, labels)
total_loss.append(loss.item())
pbar.set_postfix(**{'loss (batch)': loss.item()})
pbar.update(inputs.shape[0])
pbar.close()
epoch_loss = np.mean(total_loss)
return epoch_loss<|file_sep|># CoTe
This is a repository containing code used in our work on Convolutional Tensor Decomposition for Image Classification.
The code is written in Python using PyTorch.
## Prerequisites
* Python >= 3
* PyTorch >= 1.8
* Torchvision >=0.9
* Numpy >=1.19
* Scipy >=1.5
* tqdm >=4.59
## Usage
### Tensor decomposition
To perform tensor decomposition on a dataset stored at `DATASET_DIR`, use `tensor_decomposition.py` as follows:
python
python tensor_decomposition.py --dataset_dir DATASET_DIR
You can also specify other parameters such as number of components (`--components`), degree (`--degree`), etc.
The output will be stored at `DATASET_DIR/tensor_decomposition/`.
### Training
To train a convolutional neural network (CNN) on decomposed tensors stored at `DATASET_DIR/tensor_decomposition`, use `train.py` as follows:
python
python train.py --dataset_dir DATASET_DIR/tensor_decomposition
You can also specify other parameters such as number of epochs (`--epochs`), learning rate (`--learning_rate`), etc.
The trained model will be saved at `DATASET_DIR/tensor_decomposition/model.pt`.
### Testing
To test a trained model stored at `MODEL_PATH` on decomposed tensors stored at `DATASET_DIR/tensor_decomposition`, use `test.py` as follows:
python
python test.py --dataset_dir DATASET_DIR/tensor_decomposition --model_path MODEL_PATH
The output will be stored at `DATASET_DIR/tensor_decomposition/test_output/`.
## Citation
If you use this code in your research work, please cite our paper:
bibtex
@article{kimura2020cote,
title={Convolutional Tensor Decomposition for Image Classification},
author={Koji Kimura and Seong Jin Oh},
journal={arXiv preprint arXiv:2010.07014},
year={2020}
}
<|file_sep|># -*- coding:utf-8 -*-
import os
import sys
import argparse
import numpy as np
from scipy.sparse import coo_matrix
from CoTe.tensor import TTensor
def parse_args():
parser = argparse.ArgumentParser(description='Tensor decomposition')
parser.add_argument('--dataset_dir',
required=True,
type=str,
help='Dataset directory')
parser.add_argument('--components',
type=int,
default=32,
help='Number of components')
parser.add_argument('--degree',
type=int,
default=6,
help='Degree')
parser.add_argument('--tolerance',
type=float,
default=1e-8,
help='Tolerance')
parser.add_argument('--max_iter',
type=int,
default=10000,
help='Maximal number of iterations')
args = parser.parse_args()
return args
def main(args):
dataset_dir = args.dataset_dir
if not os.path.isdir(dataset_dir):
print(f'Dataset directory {dataset_dir} does not exist.')
sys.exit(1)
components = args.components
degree = args.degree
tolerance = args.tolerance
max_iter = args.max_iter
os.makedirs(os.path.join(dataset_dir,'tensor_decomposition'), exist_ok=True)
output_dir = os.path.join(dataset_dir,'tensor_decomposition')
train_path = os.path.join(dataset_dir,'train')
test_path = os.path.join(dataset_dir,'test')
t_tensor_train = TTensor.load(train_path)
t_tensor_test = TTensor.load(test_path)
tensor_train = t_tensor_train.to_sparse()
tensor_test = t_tensor_test.to_sparse()
tensor_train.fit(degree=degree)
tensor_test.fit(degree=degree)
tensor_train.decompose(components=components,tolerance=tolerance,max_iter=max_iter)
tensor_test.decompose(components=components,tolerance=tolerance,max_iter=max_iter)
train_components_path = os.path.join(output_dir,'train_components.npy')
test_components_path = os.path.join(output_dir,'test_components.npy')
np.save(train_components_path,tensor_train.components())
np.save(test_components_path,tensor_test.components())
if __name__ == '__main__':
main(parse_args())<|repo_name|>koji-kimura/CoTe<|file_sep|>/CoTe/model.py
# -*- coding:utf-8 -*-
import torch.nn as nn
class CoTeNet(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,stride,padding,dilation,bias=False):
super(CoTeNet,self).__init__()
self.in_channels=in_channels
self.out_channels=out_channels
self.kernel_size=kernel_size
self.stride=stride
self.padding=padding
self.dilation=dilation
self.bias=bias
self.weight=torch.Tensor(out_channels,in_channels,kernel_size,kernel_size).to(torch.float32).uniform_(-0.01,-0.01).requires_grad_()
if bias:
self.bias=torch.Tensor(out_channels).to(torch.float32).uniform_(-0.01,-0.01).requires_grad_()
def forward(self,x):
batch_size=x.shape[0]
x=x.view(batch_size,self.in_channels,self.kernel_size,self.kernel_size,self.kernel_size,-1)
x=torch.einsum('bikjlm,bkm->bijlm',x,self.weight)
if self.bias:
x=x+self.bias.view(1,-1,1,1,1).expand_as(x)
x=x.view(batch_size,x.shape[1],x.shape[2]*x.shape[3],x.shape[4]*x.shape[5])
return x
class CoTeBlock(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,stride,padding,dilation,bias=False):
super(CoTeBlock,self).__init__()
self.conv=CoteNet(in_channels,out_channels,kernel_size,stride,padding,dilation,bias=bias)
def forward(self,x):
x=self.conv(x)
return x
class CoTeNetModel(nn.Module):
def __init__(self,n_blocks,n_classes):
super(CoTeNetModel,self).__init__()
self.n_blocks=n_blocks
self.n_classes=n_classes
self.block_1=CoteBlock(in_channels=32,out_channels=32,kernel_size=(7,7),stride=(4,4),padding=(0,0),dilation=(1,1))
self.block_2=CoteBlock(in_channels=32,out_channels=64,kernel_size=(5,5),stride=(4,4),padding=(0,0),dilation=(1,1))
self.block_3=CoteBlock(in_channels=64,out_channels=128,kernel_size=(5,5),stride=(4,4),padding=(0,0),dilation=(1,1))
self.block_4=CoteBlock(in_channels=128,out_channels=256,kernel_size=(5,5),stride=(4,4),padding=(0,0),dilation=(1,1))
self.avgpool=nn.AdaptiveAvgPool2d((7*7))
self.classifier=nn.Linear(256*7
