Upcoming Tennis Matches in Tolentino, Italy
Tomorrow promises to be an exciting day for tennis enthusiasts in Tolentino, Italy, as a series of thrilling matches are scheduled to take place. These matches are not just about showcasing local talent but also offer an opportunity for fans and bettors to engage with the sport on a deeper level. This article provides an in-depth look at the matches, including expert betting predictions and insights into the players who will be competing.
Match Schedule Overview
The tournament in Tolentino is set to feature a diverse lineup of players, both seasoned veterans and rising stars. The schedule for tomorrow includes several key matches that are expected to draw significant attention from both spectators and bettors alike. Here’s a detailed breakdown of the matches:
- Match 1: Player A vs. Player B - Scheduled for 10:00 AM
- Match 2: Player C vs. Player D - Scheduled for 12:00 PM
- Match 3: Player E vs. Player F - Scheduled for 2:00 PM
- Match 4: Player G vs. Player H - Scheduled for 4:00 PM
- Final Match: Winner of Match 1 vs. Winner of Match 3 - Scheduled for 6:00 PM
Detailed Match Analysis
Each match in the tournament brings its own unique dynamics and storylines. Let’s delve into the details of each upcoming match to understand what makes them particularly intriguing.
Match 1: Player A vs. Player B
This match features two highly competitive players known for their aggressive playstyles. Player A, with a strong baseline game, is expected to leverage his powerful groundstrokes to dominate rallies. On the other hand, Player B is renowned for his exceptional net play and quick reflexes, making him a formidable opponent on faster surfaces.
Expert betting predictions suggest that while Player A has been performing consistently well this season, Player B’s recent form gives him a slight edge. Bettors should consider placing their bets on Player B with an underdog payout.
- Betting Prediction: Bet on Player B to win with a potential payout of 1.8x.
- Key Factor: Weather conditions – if it’s windy, it might favor Player A’s powerful serves.
Match 2: Player C vs. Player D
Known for their strategic minds on the court, both players bring a tactical approach to their games. Player C excels in constructing points patiently and exploiting opponents’ weaknesses with precision. Meanwhile, Player D is known for his unpredictable shot-making ability and mental toughness under pressure.
Betting experts highlight that while both players have been evenly matched in previous encounters, Player D’s recent victory streak could give him the psychological advantage going into this match.
- Betting Prediction: Consider betting on a tight match with over/under games at around 22.5 games.
- Key Factor: Serve performance – both players have strong serves, but consistency will be crucial.
Match 3: Player E vs. Player F
This match is anticipated to be one of the highlights of the day, featuring two players known for their high-intensity playing styles. Player E’s agility and speed make him a threat on all surfaces, while Player F’s endurance and tactical acumen allow him to outlast opponents in long rallies.
Analysts predict that this match could go either way, but given Player F’s recent form and experience in high-stakes matches, he is slightly favored by bookmakers.
- Betting Prediction: Bet on Player F to win with odds of around 1.7x.
- Key Factor: Court surface – if it’s clay, expect longer rallies favoring Player F’s endurance.
Match 4: Player G vs. Player H
This encounter features two players who have been climbing the ranks rapidly this season. Player G is known for his explosive power from the baseline, while Player H excels in transitioning quickly from defense to offense.
Experts suggest that this match could be closely contested, but Player H’s ability to adapt his game plan mid-match might give him an edge.
- Betting Prediction: Consider a bet on sets played – over/under at around three sets.
- Key Factor: Mental resilience – both players have shown vulnerability under pressure.
The Final Match: Winner of Match 1 vs. Winner of Match 3
The final match is set to be a thrilling conclusion to the day’s events, featuring the winners of Match 1 and Match 3. Depending on the outcomes of these matches, we could see either a clash between baseline powerhouses or a battle of strategic minds.
If Player A and Player F advance, it would set up an intriguing matchup between power and endurance. Conversely, if Players B and E reach the final, expect a fast-paced contest filled with tactical shifts.
Bettors should keep an eye on these potential matchups as they plan their strategies.
- Betting Prediction: If possible, wait until after Match Day results before placing bets on the final.
- Key Factor: Momentum – winning players often carry their confidence into subsequent matches.
<|repo_name|>SahilBhasin007/Emotion-Recognition<|file_sep|>/train.py
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import argparse
from torchvision import transforms
from dataloader import Fer2013Dataset
from model import SimpleCNN
from utils import save_checkpoint
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Emotion recognition training')
parser.add_argument('--train_batch_size', type=int, default=64)
parser.add_argument('--val_batch_size', type=int, default=32)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--save_dir', type=str)
args = parser.parse_args()
transform_train = transforms.Compose([
transforms.Resize((48,48)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
transform_val = transforms.Compose([
transforms.Resize((48,48)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
trainset = Fer2013Dataset(transform=transform_train)
valset = Fer2013Dataset(transform=transform_val)
train_loader = DataLoader(trainset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
val_loader = DataLoader(valset,
batch_size=args.val_batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(args.epochs):
print('Epoch {}/{}'.format(epoch+1,args.epochs))
print('-'*10)
model.train()
for i,data in enumerate(train_loader):
inputs,target = data[0].to(device),data[1].to(device)
outputs = model(inputs)
loss = criterion(outputs,target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i %10 ==9:
print('Batch {}/{} - loss:{:.4f}'.format(i+1,len(train_loader),loss.item()))
model.eval()
with torch.no_grad():
correct,total =0 ,0
for data in val_loader:
inputs,target = data[0].to(device),data[1].to(device)
outputs = model(inputs)
pred = outputs.argmax(dim=1).cpu().numpy()
target = target.cpu().numpy()
correct += (pred==target).sum()
total += target.shape[0]
print('Val accuracy:{:.2f}%'.format(correct/total*100))
save_checkpoint({'state_dict':model.state_dict(),
'epoch':epoch+1},args.save_dir)<|file_sep|># Emotion-Recognition
## Model Architecture
## Dataset
[Fer2013](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)
## Results
Train Accuracy:98%
Val Accuracy:89%
<|repo_name|>SahilBhasin007/Emotion-Recognition<|file_sep|>/utils.py
import os
def save_checkpoint(state,directory='./checkpoints'):
os.makedirs(directory ,exist_ok=True)
filename=os.path.join(directory,'checkpoint.pth.tar')
torch.save(state,filename)<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Aug
@author: Sahil Bhasin
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
def get_labels():
labels_map={0:'Angry',1:'Disgust',2:'Fear',3:'Happy',4:'Sad',
5:'Surprise',6:'Neutral'}
return labels_map
def read_data(path):
if os.path.isfile(path):
data=pd.read_csv(path)
elif os.path.isdir(path):
data=pd.DataFrame(columns=['emotion','pixels'])
for root ,dirnames ,filenames in os.walk(path):
for file in filenames:
if file.endswith('.jpg') or file.endswith('.png'):
file_path=os.path.join(root,file)
img=cv2.imread(file_path)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img=cv2.resize(img,(48,48))
label=os.path.basename(root).split('_')[0]
image_string=np.array(img).flatten().astype('uint8')
image_string=image_string.tostring()
data=data.append({'emotion':label,'pixels':image_string},ignore_index=True)
return data
def get_data(data_path='./data'):
data=read_data(data_path)
labels_map=get_labels()
data['emotion']=data['emotion'].map(labels_map.get)
pixels=data['pixels']
pixels=pixels.apply(lambda x:np.fromstring(x,dtype='uint8').reshape(48,-1))
data['pixels']=pixels
emotions=data['emotion']
data=data.drop(['emotion'],axis=1)
return data.values.astype('float32'),emotions.values
def plot_images(images,nrows=5):
f,c=plt.subplots(nrows=nrows)
f.set_size_inches(12 ,7)
for i,image in enumerate(images):
c[i].imshow(image,cmap='gray')
def plot_distribution(emotions):
plt.figure(figsize=(7 ,7))
plt.pie(emotions.value_counts(),labels=emotions.unique(),autopct='%1.f%%')
plt.title('Emotion distribution')
def split_data(X,y,test_ratio=0.15):
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=test_ratio,stratify=y)
return X_train,X_test,y_train,y_test
if __name__=='__main__':
X,y=get_data()
X_train,X_test,y_train,y_test=split_data(X,y)
sample_images=X_train[:5]
sample_emotions=y_train[:5]
print('Sample images shape:',sample_images.shape)
print('Sample emotions shape:',sample_emotions.shape)
print('nSample emotions:n',sample_emotions,'n')
print('nSample images:n',sample_images[0])
print('nImage shape:',sample_images[0].shape,'n')
print('Image pixel values:n',sample_images[0][20,:])
sample_images=sample_images.reshape(-1 ,48 ,48 ,1)
print('nReshape sample images shape:',sample_images.shape,'n')
f,c=plt.subplots(nrows=5 ,ncols=5 ,sharex=True ,sharey=True)
f.set_size_inches(12 ,7)
for i in range(5):
r=np.random.randint(0,len(sample_images))
c[i][0].imshow(sample_images[r][:,:,0] ,cmap='gray')
label=y_train[r]
c[i][0].set_title(label)
f.tight_layout()
emotions=pd.Series(y_train,name='emotion')
emotions.value_counts().plot(kind='bar')
plt.title('Training dataset emotion distribution')
emotions=pd.Series(y_test,name='emotion')
emotions.value_counts().plot(kind='bar')
plt.title('Test dataset emotion distribution')<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Wed Aug
@author: Sahil Bhasin
"""
import os
import cv2
import numpy as np
def get_labels():
labels_map={0:'Angry',1:'Disgust',2:'Fear',3:'Happy',4:'Sad',
5:'Surprise',6:'Neutral'}
return labels_map
class Fer2013Dataset():
def __init__(self,data_dir='./data/Fer2013.csv',
transform=None):
self.data_dir=data_dir
self.transform=transform
def __len__(self):
if not os.path.isfile(self.data_dir):
raise ValueError('File {} not found'.format(self.data_dir))
def __getitem__(self,index):
if not os.path.isfile(self.data_dir):
raise ValueError('File {} not found'.format(self.data_dir))
data=pd.read_csv(self.data_dir,nrows=index+1)
image_string=data['pixels'].iloc[index]
img=np.fromstring(image_string,dtype='uint8').reshape(48,-1)
<|repo_name|>SahilBhasin007/Emotion-Recognition<|file_sep|>/model.py
import torch.nn as nn
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN,self).__init__()
def forward(self,x):
<|repo_name|>YoussefAldabagh/Baas<|file_sep|>/src/components/Comments/index.js
/* eslint-disable react/no-array-index-key */
/* eslint-disable no-nested-ternary */
/* eslint-disable no-plusplus */
/* eslint-disable react/jsx-no-target-blank */
/* eslint-disable jsx-a11y/click-events-have-key-events */
/* eslint-disable jsx-a11y/no-static-element-interactions */
/* eslint-disable react/destructuring-assignment */
/* eslint-disable no-unused-expressions */
/* eslint-disable no-return-assign */
/* eslint-disable react/no-unescaped-entities */
/* eslint-disable react/jsx-pascal-case */
/* eslint-disable no-nested-ternary */
/* eslint-disable react/no-unescaped-entities */
/* eslint-disable no-shadow */
/* eslint-disable react/no-array-index-key */
import React from 'react';
// import PropTypes from 'prop-types';
// import { connect } from 'react-redux';
// import { createStructuredSelector } from 'reselect';
// import { compose } from 'redux';
// import injectSaga from 'utils/injectSaga';
// import injectReducer from 'utils/injectReducer';
// import { makeSelectUser } from 'containers/App/selectors';
// import reducer from './reducer';
// import saga from './saga';
// const keyExtractor = item => item.id;
const Comments = ({ comments }) => {
const upVoteCommentHandler = (index) => {
let newCommentsList = [...comments];
let currentCommentVotes;
let newVotes;
if (newCommentsList[index].voteStatus === 'upVoted') {
currentCommentVotes = newCommentsList[index].voteCount;
newVotes = currentCommentVotes -1;
newCommentsList[index] = {
...newCommentsList[index],
voteCount : newVotes,
voteStatus : 'none'
}
} else if (newCommentsList[index].voteStatus === 'downVoted') {
currentCommentVotes = newCommentsList[index].voteCount;
newVotes = currentCommentVotes +2;
newCommentsList[index] = {
...newCommentsList[index],
voteCount : newVotes,
voteStatus : 'upVoted'
}
} else {
currentCommentVotes = newCommentsList[index].voteCount;
newVotes = currentCommentVotes +1;
newCommentsList[index] = {
...newCommentsList[index],
voteCount : newVotes,
voteStatus : 'upVoted'
}
}
console.log(newCommentsList);
// return (dispatch) => dispatch({
// type : "VOTE_UP_COMMENT",
// payload : {id : comment.id}
// })
return comments;
