The Thrill of the ATP World Tour Finals
The ATP World Tour Finals, held in the vibrant city of Turin, Italy, is a culmination of the year's best tennis talents. As fans eagerly anticipate tomorrow's matches, the excitement builds around who will emerge victorious in this prestigious event. The tournament is not just about showcasing exceptional skills but also about strategic plays and unexpected turns that keep spectators on the edge of their seats.
Among the notable participants is Jimmy Connors Group Italy, a team that has consistently demonstrated prowess and determination. Their presence adds an extra layer of intrigue to the matches, as they bring a unique style and strategy to the court.
Expert Betting Predictions
With tomorrow's matches drawing near, expert analysts have been busy providing betting predictions that offer insights into potential outcomes. These predictions are based on a combination of statistical analysis, player performance history, and current form.
Key Players to Watch
- Roger Federer: Known for his precision and agility, Federer remains a formidable opponent. His experience in high-stakes matches gives him an edge.
- Rafael Nadal: Nadal's resilience and powerful playstyle make him a favorite among fans. His ability to adapt to different surfaces is unmatched.
- Dominic Thiem: Thiem's recent performances have been impressive, showcasing his growth as a player capable of challenging top-ranked opponents.
Betting Trends and Insights
Analysts suggest that betting on underdogs might yield surprising results this year. Teams like Jimmy Connors Group Italy have shown potential to upset stronger teams through strategic gameplay and teamwork.
Strategic Plays and Match Dynamics
The dynamics of each match can shift dramatically based on several factors, including weather conditions, player fitness, and psychological readiness. Teams that can adapt quickly to these changes often have an advantage.
Tactics Employed by Top Teams
- Baseline Dominance: Many top players focus on controlling the baseline to dictate the pace of the game.
- Serving Strategy: Effective serving can disrupt an opponent's rhythm and create opportunities for winning points.
- Mental Fortitude: Maintaining composure under pressure is crucial for success in high-stakes matches.
The Role of Fan Support
The energy from fans can significantly influence player performance. In Turin, local support for teams like Jimmy Connors Group Italy adds an electrifying atmosphere that can boost players' morale.
Engaging with Fans
- Fans are encouraged to engage with social media platforms where live updates and interactive content are shared.
- Tourist activities around Turin provide additional entertainment options for those attending the matches.
Predictions for Tomorrow's Matches
<|repo_name|>Rajeev-rajesh/aiops<|file_sep|>/aiops/optimizer.py
from aiops.optimizer import Optimizer
from aiops.optimizer import AdamOptimizer
from aiops.optimizer import AdagradOptimizer
from aiops.optimizer import RMSPropOptimizer
__all__ = ['Optimizer', 'AdamOptimizer', 'AdagradOptimizer', 'RMSPropOptimizer']
<|repo_name|>Rajeev-rajesh/aiops<|file_sepodels/Autoencoder.py
import numpy as np
class AutoEncoder(object):
"""
A simple auto encoder implementation using tensorflow.
Parameters:
-----------
learning_rate: float (default=0.001)
The learning rate used during training.
n_epochs: int (default=100)
The number of epochs over which training should occur.
batch_size: int (default=100)
The batch size used during training.
display_step: int (default=1)
The frequency at which loss statistics are displayed.
save_path: str (default=None)
Specify path where trained model should be saved.
restore_path: str (default=None)
Specify path from which previously trained model should be restored.
dropout_keep_prob: float (default=0.8)
Dropout probability used during training
n_hidden_1: int (default=256)
The number of neurons in hidden layer one.
n_hidden_2: int (default=128)
The number of neurons in hidden layer two.
n_input: int (default=784)
The dimensionality of input data
init_stddev : float (default=None)
If None then use Xavier initialization otherwise use normal distribution with specified standard deviation
when initializing weights
output_dir : string default None
If not None then it specifies output directory where models are stored
log_dir : string default None
If not None then it specifies log directory where logs are stored
logger : logger object default None
A logger object if provided will be used instead internal logging mechanism
tf_logger : tflogger object default None
A tflogger object if provided will be used instead internal logging mechanism
Returns:
model : AutoEncoder()
An instance of AutoEncoder class with all parameters set according to arguments passed
TODO:
Add support for more than two hidden layers
Add support for validation set during training
Add support for saving model periodically during training
TODO:
Create separate methods train() , test() , predict()
Example:
import numpy as np
from sklearn.datasets import load_digits
digits = load_digits()
X_train = digits.data[:1500]
Y_train = digits.target[:1500]
X_test = digits.data[1500:]
Y_test = digits.target[1500:]
model = AutoEncoder(learning_rate=0.01,
n_epochs=10,
batch_size=100,
display_step=1,
output_dir='models',
log_dir='logs')
model.fit(X_train,X_train)
test_loss = model.evaluate(X_test,X_test)
prediction = model.predict(X_test)
This code snippet trains an autoencoder on MNIST dataset with three hidden layers having sizes [256 ,128 ,64]. It uses mean squared error as cost function.
It trains over ten epochs with batch size equaling one hundred samples per batch.
It displays loss statistics every epoch.
Code:
from sklearn.datasets import load_digits
digits = load_digits()
X_train = digits.data[:1500]
Y_train = digits.target[:1500]
X_test = digits.data[1500:]
Y_test = digits.target[1500:]
model = AutoEncoder(learning_rate=0.01,
n_epochs=10,
batch_size=100,
display_step=1,
output_dir='models',
log_dir='logs')
model.fit(X_train,X_train)
test_loss = model.evaluate(X_test,X_test)
prediction = model.predict(X_test)
"""
def __init__(self,n_input=None,n_hidden_1=None,n_hidden_2=None,
n_classes=None,
learning_rate=.001,n_epochs=100,batch_size=100,
display_step=1,output_dir=None,
log_dir=None,dropout_keep_prob=.8,
init_stddev=None,
restore_path=None,
save_path=None,
logger=None,tf_logger=None):
self.n_input=n_input
self.n_hidden_1=n_hidden_1
self.n_hidden_2=n_hidden_2
self.learning_rate=learning_rate
self.n_epochs=n_epochs
self.batch_size=batch_size
self.display_step=int(display_step) #display stats after every epoch
self.output_dir=output_dir #where models should be saved
self.log_dir=log_dir #where logs should be stored
self.dropout_keep_prob=float(dropout_keep_prob) #dropout probability during training
if init_stddev!=None:
assert isinstance(init_stddev,float), "init_stddev must be a float"
assert init_stddev>=0,"init_stddev must be non-negative"
self.init_stddev=float(init_stddev) #standard deviation used while initializing weights with normal distribution
else:
self.init_stddev=None #None means use xavier initialization
self.restore_path=self._validate_and_set_restore_path(restore_path) #path from which we want to restore previously trained model
self.save_path=self._validate_and_set_save_path(save_path) #path where we want to save our newly trained model
def _validate_and_set_restore_path(self,path):
"""Validates given restore path"""
if path==None:
return path
else:
assert isinstance(path,str),"restore path must be string"
return path
def _validate_and_set_save_path(self,path):
"""Validates given save path"""
if path==None:
return path
else:
assert isinstance(path,str),"save path must be string"
return path
def _get_weight_variable(self,name,stddev):
"""Returns weight variable initialized either using Xavier initialization or using normal distribution with specified standard deviation"""
if stddev==None:
return tf.get_variable(name=name,dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
else:
return tf.get_variable(name=name,dtype=tf.float32,
initializer=tf.random_normal(shape=[self.n_input,self.n_hidden_1],mean=.5,stddev=self.init_stddev))
def _get_bias_variable(self,name):
"""Returns bias variable initialized using zeros"""
return tf.get_variable(name=name,dtype=tf.float32,initializer=tf.zeros([self.n_input]))
def _create_placeholders(self):
"""Creates placeholders required by tensorflow graph"""
x=tf.placeholder(tf.float32,[None,self.n_input],name="input")
y_=tf.placeholder(tf.float32,[None,self.n_input],name="output")
keep_prob=tf.placeholder(tf.float32,name="keep_prob")
return x,y_,keep_prob
def _create_variables(self):
"""Creates variables required by tensorflow graph"""
weights={"encoder_h":self._get_weight_variable("encoder_h",stddev=self.init_stddev),
"decoder_h":self._get_weight_variable("decoder_h",stddev=self.init_stddev)}
biases={"encoder_b":self._get_bias_variable("encoder_b"),
"decoder_b":self._get_bias_variable("decoder_b")}
return weights,biases
def _create_model_graph(self,x,y_,keep_prob):
"""Creates tensorflow computation graph"""
weights,biases=self._create_variables()
encoder_op=tf.nn.sigmoid(tf.add(tf.matmul(x,weights["encoder_h"]),biases["encoder_b"]))
decoder_op=tf.nn.sigmoid(tf.add(tf.matmul(encoder_op,weights["decoder_h"]),biases["decoder_b"]))
prediction_op={x:x,y_:y_,keep_prob:keep_prob,"encoded":encoder_op,"decoded":decoder_op}
return prediction_op
def _create_loss_function_graph(self,prediction_op):
"""Creates loss function graph"""
mse_loss=tf.reduce_mean(.5*tf.pow(prediction_op["decoded"]-prediction_op["y_"],
power=2))
return mse_loss
def _create_training_optimizer_graph(self,mse_loss):
"""Creates optimizer graph which minimizes loss function"""
optimizer=tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(mse_loss)
return optimizer
def build_graph(self):
"""
Builds complete tensorflow computation graph
Returns :
A tuple containing following elements :
prediction_ops - dictionary containing placeholders created alongwith encoded values & decoded values returned by network
loss_function - tensor representing mean squared error between decoded values & actual output values
optimizer - optimizer operation minimizing mean squared error
"""
x,y_,keep_prob=self._create_placeholders()
prediction_ops=self._create_model_graph(x,y_,keep_prob)
mse_loss=self._create_loss_function_graph(prediction_ops=prediction_ops)
opt_operation=self._create_training_optimizer_graph(mse_loss=mse_loss)
return prediction_ops,mse_loss,opt_operation
def fit(model,x,y):
if not hasattr(model,'graph'):
raise ValueError('model.graph cannot be none')
x_data=np.array(x).astype(np.float32)
y_data=np.array(y).astype(np.float32)
print('Fitting Model ...')
with tf.Session(graph=model.graph) as sess:
sess.run(tf.global_variables_initializer())
saver=tf.train.Saver()
num_examples=x_data.shape[0]
total_batch=int(num_examples/model.batch_size)
print('Total batches:',total_batch)
print('Start Training...')
avg_cost_list=[]
for epoch in range(model.n_epochs):
avg_cost_val=.00000000000000
total_batch=int(num_examples/model.batch_size)
print('Total batches:',total_batch)
print('Epoch:',epoch+1)
for i in range(total_batch):
batch_x=batch_y=get_next_batch(x_data,y_data,model.batch_size,i,total_batch)
_,cost=sess.run([model.opt_operation,model.mse_loss],
feed_dict={model.prediction_ops['x']:batch_x,
model.prediction_ops['y_']:batch_y,
model.prediction_ops['keep_prob']:model.dropout_keep_prob})
avg_cost_val+=cost/float(total_batch)
avg_cost_list.append(avg_cost_val)
print("Cost:",avg_cost_val)
if epoch%model.display_step==0:
print('Epoch:',epoch+1,'completed out of',model.n_epochs,'with cost=',avg_cost_val)
print('Training Finished!')
if hasattr(model,'save_path'):
saver.save(sess,model.save_path)
print('Model Saved!')
return avg_cost_list
def evaluate(model,x,y):
if not hasattr(model,'graph'):
raise ValueError('model.graph cannot be none')
x_data=np.array(x).astype(np.float32)
y_data=np.array(y).astype(np.float32)
print('Evaluating Model ...')
with tf.Session(graph=model.graph) as sess:
sess.run(tf.global_variables_initializer())
saver=tf.train.Saver()
num_examples=x_data.shape[0]
total_batch=int(num_examples/model.batch_size)
print('Total batches:',total_batch)
saver.restore(sess,model.restore_path)
avg_cost_val=.00000000000000
total_batch=int(num_examples/model.batch_size)
print('Start Evaluating...')
avg_cost_list=[]
for i in range(total_batch):
batch_x=batch_y=get_next_batch(x_data,y_data,model.batch_size,i,total_batch)
cost=sess.run([model.mse_loss],
feed_dict={model.prediction_ops['x']:batch_x,
model.prediction_ops['y_']:batch_y})
avg_cost_val+=cost/float(total_batch)
avg_cost_list.append(avg_cost_val)
print("Cost:",avg_cost_val)
return avg_cost_list
def predict(model,x):
if not hasattr(model,'graph'):
raise ValueError('model.graph cannot be none')
x_data=np.array(x).astype(np.float32)
print('Predicting Model ...')
with tf.Session(graph=model.graph) as sess:
sess.run(tf.global_variables_initializer())
saver=tf.train.Saver()
num_examples=x_data.shape[0]
total_batch=int(num_examples/model.batch_size)
saver.restore(sess,model.restore_path)
pred_vals=[]
for i in range(total_batch):
batch_x=get_next_predictable(x_data,model.batch_size,i,total_batch)
pred=sess.run([model.prediction_ops['decoded']],
feed_dict={model.prediction_ops['x']:batch_x})
pred_vals.extend(pred.tolist()[0])
return pred_vals
def get_next_predictable(dataset,batch_size,current_index,total_batches):
num_samples=len(dataset)
if current_index==total_batches-1:
remaining=num_samples-(current_index*batch_size)
last_dataset_part=list(dataset[current_index*batch_size:])
padding=(batch_size-remaining)*[dataset[-1]]
padded_dataset_part=list(last_dataset_part)+list(padding)
next_dataset_part=padded_dataset_part[:batch_size]
else:
next_dataset_part=list(dataset[current_index*batch_size:(current_index+1)*batch_size])
return next_dataset_part
def get_next_batch(dataset_a,dataset_b,batch_size,current_index,total_batches):
num_samples=len(dataset_a)
assert len(dataset_a)==len(dataset_b), "datasets don't have same length"
if current_index==total_batches-1:
remaining=num_samples-(current_index*batch_size)
last_dataset_a_part=list(dataset_a[current_index*batch_size:])
last_dataset_b_part=list(dataset_b[current_index*batch_size:])
padding=(batch_size-remaining)*[dataset_a[-1]]
padded_last_dataset_a_part=list(last_dataset_a_part)+list(padding)
padded_last_dataset_b_part=list(last_dataset_b_part)+list(padding)
next_dataset_a_part=padded_last_dataset_a_part[:batch_size]
next_dataset_b_part=padded_last_dataset_b_part[:batch_size]
else:
next_dataset_a_part=list(dataset_a[current_index*batch_size:(current_index+1)*batch_SIZE])
next_datset_b part=list(datset b[current index*batchesize:(current index +batchesize)])
return next dataseta part,next datasetb part
if __name__=="__main__":
pass
#TODO :
#Create separate methods train(), test(), predict()
#TODO :
#Add support for more than two hidden layers
#TODO :
#Add support for validation set during training
#TODO :
#Add support saving models periodically during training
#TODO :
#Support dropout only at specific layers
<|repo_name|>Rajeev-rajesh/aiops<|file_sepodels/AutoEncoder.py<|repo_name|>Rajeev-rajesh/aiops<|file_sep
