Upcoming Football Matches: Slovenian 2. Division
The Slovenian 2. Division is gearing up for an exciting day of football tomorrow, with several matches that promise to keep fans on the edge of their seats. As we look ahead to these clashes, expert betting predictions provide valuable insights into potential outcomes, helping enthusiasts make informed decisions. This comprehensive guide delves into the scheduled matches, offering detailed analysis and predictions for each game.
Matchday Schedule
Tomorrow's fixtures in the Slovenian 2. Division are set to feature intense competition across various stadiums. Here’s a breakdown of the matches:
- Team A vs Team B - Kickoff at 14:00
- Team C vs Team D - Kickoff at 16:00
- Team E vs Team F - Kickoff at 18:00
Detailed Match Analysis
Team A vs Team B
This match is anticipated to be a thrilling encounter between two evenly matched sides. Team A, known for its strong defensive tactics, will face off against Team B, which boasts an aggressive attacking lineup. The clash promises to be a tactical battle, with both teams aiming to capitalize on their strengths.
- Key Players:
- Team A’s star defender has been pivotal in their recent clean sheets.
- Team B’s top scorer is on form and could be the difference-maker.
- Betting Predictions:
- Draw: 3.10
- Team A Win: 2.20
- Team B Win: 2.80
Team C vs Team D
The upcoming match between Team C and Team D is expected to be a high-scoring affair. Both teams have shown impressive form this season, with Team C excelling in midfield control and Team D known for its quick counter-attacks.
- Key Players:
- Team C’s midfield maestro has been instrumental in dictating play.
- Team D’s pacey winger is a constant threat on the flanks.
- Betting Predictions:
- Total Over 2.5 Goals: 1.85
- Both Teams to Score: Yes at 1.90
- Draw: 3.25
Team E vs Team F
This fixture is set to be a classic encounter between two teams vying for promotion spots. Team E’s solid home record could give them an edge over Team F, which has struggled away from home this season.
- Key Players:
- Team E’s goalkeeper has been in outstanding form, keeping multiple clean sheets.
- Team F’s creative playmaker is crucial for breaking down defenses.
- Betting Predictions:
- Team E Win: 1.95
- Team F Win: 3.60
- No Goals: Yes at 2.30
Tactical Insights and Strategies
The Importance of Midfield Control
In football, controlling the midfield is often the key to success. Teams that dominate this area can dictate the tempo of the game and create more scoring opportunities. For tomorrow’s matches, both Team C and Team D will rely heavily on their midfielders to control possession and launch attacks.
- Midfield Battle: The clash between Team C’s creative midfielder and Team D’s defensive stalwart will be crucial in determining the outcome of their match.
- Possession Statistics: Teams with higher possession percentages tend to have better chances of winning or drawing games.
The Role of Defenders in Counter-Attacks
A strong defense is not just about preventing goals; it also plays a vital role in initiating counter-attacks. Teams like Team B and Team F excel in quickly transitioning from defense to attack, catching their opponents off guard.
- Pivotal Defenders: Key defenders who can intercept passes and launch swift counter-attacks are invaluable assets for their teams.
- Tactical Adjustments: Coaches often tweak their strategies based on the opponent’s weaknesses, focusing on exploiting gaps during counter-attacks.
Betting Strategies and Tips
Leveraging Statistical Data
Betting on football requires a blend of statistical analysis and intuition. By examining past performances, head-to-head records, and current form, bettors can make more informed decisions.
- Data Analysis: Utilize data from previous matches to identify trends and patterns that could influence tomorrow’s outcomes.
- Injury Reports: Keep an eye on injury updates, as they can significantly impact team performance and betting odds.
Making Informed Betting Decisions
To maximize your chances of winning bets, consider factors such as team morale, weather conditions, and recent managerial changes. These elements can all play a role in determining match results.
- Morale Boosts: Teams with high morale often perform better than expected, especially after significant victories or managerial changes.
- Wealth of Experience: Experienced players can make a difference in tight matches by making crucial plays under pressure.
Predictions and Expert Opinions
Analyzing Expert Forecasts
Betting experts provide valuable insights based on years of experience and deep knowledge of the sport. Their predictions often include detailed analyses of team strategies, player performances, and potential game-changing moments.
- Credible Sources: Rely on reputable betting experts who have a track record of accurate predictions.
- Diverse Perspectives: Consider multiple expert opinions to gain a well-rounded view of potential outcomes.
Incorporating Public Sentiment
In addition to expert forecasts, public sentiment can also influence betting markets. Popular teams or players might sway public opinion, affecting odds and betting behavior.
- Social Media Trends: Monitor social media platforms for trending topics related to upcoming matches.
- Fan Forums: Engage with fan communities to gauge general sentiment and gather insights into team dynamics.
Tips for Successful Betting Tomorrow
Finding Value in Betting Markets
To achieve success in betting, it’s essential to identify value bets where the odds offered by bookmakers do not accurately reflect the true probability of an outcome occurring.
- Odds Comparison: Compare odds across different bookmakers to find the best value for your bets.
- Risk Management: Set a budget for your bets and stick to it to avoid overspending and minimize losses.
Diversifying Your Betting Portfolio
Diversification is key in betting strategy; spreading your bets across different matches or markets can help mitigate risks and increase chances of winning overall returns.
jzhang-git/DeepLearning<|file_sep|>/LeNet/LeNet.py
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
class LeNet:
def __init__(self):
self.x = tf.placeholder(tf.float32,[None,784])
self.y_ = tf.placeholder(tf.float32,[None,10])
self.keep_prob = tf.placeholder(tf.float32)
self.W_conv1 = self.weight_variable([5,5,1,6])
self.b_conv1 = self.bias_variable([6])
x_image = tf.reshape(self.x,[self.x.get_shape().as_list()[0],28,-1])
h_conv1 = tf.nn.relu(self.conv2d(x_image,self.W_conv1) + self.b_conv1)
h_pool1 = self.max_pool_2x2(h_conv1)
self.W_conv2 = self.weight_variable([5,5,6,16])
self.b_conv2 = self.bias_variable([16])
h_conv2 = tf.nn.relu(self.conv2d(h_pool1,self.W_conv2) + self.b_conv2)
h_pool2 = self.max_pool_2x2(h_conv2)
self.W_fc1 = self.weight_variable([400,120])
self.b_fc1 = self.bias_variable([120])
h_pool2_flat = tf.reshape(h_pool2,[self.x.get_shape().as_list()[0],-1])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,self.W_fc1) + self.b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,self.keep_prob)
self.W_fc2 = self.weight_variable([120,84])
self.b_fc2 = self.bias_variable([84])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop,self.W_fc2) + self.b_fc2)
h_fc2_drop = tf.nn.dropout(h_fc2,self.keep_prob)
self.W_fc3 = self.weight_variable([84,10])
self.b_fc3 = self.bias_variable([10])
y_conv=tf.matmul(h_fc2_drop,self.W_fc3) + self.b_fc3
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.y_,logits=y_conv))
training_step=tf.train.AdamOptimizer(1e-5).minimize(cross_entropy)
correct_prediction=tf.equal(tf.argmax(y_conv,axis=1),tf.argmax(self.y_,axis=1))
self.accuracy=tf.reduce_mean(tf.cast(correct_prediction,dtype=tf.float32))
def weight_variable(self,dims):
initial=tf.truncated_normal(dims,stddev=0.01)
return tf.Variable(initial)
def bias_variable(self,dims):
initial=tf.constant(0.01,dtype=tf.float32)
return tf.Variable(initial)
def conv2d(self,x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(self,x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
if __name__ == '__main__':
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
lenet=LeNet()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch_xs,batch_ys=mnist.train.next_batch(50)
sess.run(lenet.training_step,{lenet.x:batch_xs,lenet.y_:batch_ys,lenet.keep_prob:0.5})
if i%100==0:
train_accuracy=sess.run(lenet.accuracy,{lenet.x:batch_xs,lenet.y_:batch_ys,lenet.keep_prob:0.5})
print("step %d ,train accuracy %g"%(i+1,float(train_accuracy)))
test_accuracy=sess.run(lenet.accuracy,{lenet.x:mnist.test.images,lenet.y_:mnist.test.labels,lenet.keep_prob:0.5})
print("test accuracy %g"%float(test_accuracy))
<|repo_name|>jzhang-git/DeepLearning<|file_sep|>/RNN/RNN.py
# -*- coding:utf-8 -*-
import numpy as np
import tensorflow as tf
class RNN:
def __init__(self,num_classes,input_size,num_hidden):
# number of classes
self.num_classes=num_classes
# input size
self.input_size=input_size
# hidden layer size
self.num_hidden=num_hidden
# parameters
# weights
# shape:(num_hidden,input_size+num_hidden+num_classes)
self.Wh=np.random.randn(num_hidden,input_size+num_hidden+num_classes)*0.01
# bias vector
# shape:(num_hidden,)
self.bh=np.zeros(num_hidden)
# output weight matrix
# shape:(num_classes,num_hidden)
self.Wo=np.random.randn(num_classes,num_hidden)*0.01
# output bias vector
# shape:(num_classes,)
self.bo=np.zeros(num_classes)
def forward_propagation(self,X,Y,hprev):
"""
X shape:(n_in,time_step,in_size)
Y shape:(n_out,time_step,out_size)
hprev shape:(n_batch,num_hidden)
output shape:(n_batch,time_step,out_size)
final_h shape:(n_batch,num_hidden)
loss value(float type)
X,Y,hprev->output,hfinal,l
X is input sequence matrix.
Y is label sequence matrix.
hprev is initial hidden state matrix.
output is output sequence matrix.
hfinal is final hidden state matrix.
l is loss value.
"""
n_in=X.shape[0]
time_step=X.shape[1]
out_size=Y.shape[2]
inputs=np.zeros((time_step,n_in,input_size+num_hidden+out_size))
outputs=np.zeros((n_in,time_step,out_size))
loss=0
for t in range(time_step):
inputs[t,:,input_size:-out_size]=X[:,t,:]
if t!=0:
inputs[t,:,input_size:-out_size]=np.hstack((inputs[t,:,input_size:-out_size],outputs[:,t-1,:]))
if t!=time_step-1:
inputs[t,:,-out_size:]=Y[:,t+1,:]
ht=np.tanh(np.dot(inputs[t,:,:],self.Wh)+self.bh+hprev)
ot=np.dot(ht,self.Wo)+self.bo
pt=softmax(ot)
outputs[:,t,:]=pt
loss+=cross_entropy(pt,Y[:,t,:])
hprev=ht
<|file_sep|># DeepLearning
TensorFlow implement some deep learning model.
## LeNet ##
A simple Convolutional Neural Network(CNN) implement by TensorFlow.
## RNN ##
A simple Recurrent Neural Network(RNN) implement by TensorFlow.
## CNN ##
A simple Convolutional Neural Network(CNN) implement by TensorFlow.
## VGGNet ##
A simple VGGNet implement by TensorFlow.
## ResNet ##
A simple ResNet implement by TensorFlow.
<|repo_name|>jzhang-git/DeepLearning<|file_sep|>/CNN/CNN.py
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.01)
return tf.Variable(initial)
def bias_variable(shape):
initial=tf.constant(0.,shape=shape)
return tf.Variable(initial)
def conv_layer(input_tensor,W,b,stride):
return tf.nn.convolution(input_tensor,W,strides=stride,padding="SAME")+b
def max_pooling(input_tensor,ksize,stride):
return tf.nn.max_pool(input_tensor,ksize=ksize,strides=stride,padding="SAME")
def dropout_layer(input_tensor):
return tf.nn.dropout(input_tensor,.8)
def CNN(x,y_,is_training):
with tf.variable_scope("conv_layer"):
W_conv11=weight_variable([5*5*6])
b_conv11=bias_variable([6])
if __name__ == '__main__':
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
x=tf.placeholder(tf.float32,[None,None,None])
y_=tf.placeholder(tf.float32,[None,None,None])
is_training=tf.placeholder(tf.bool)
cnn=CNN(x,y_,is_training)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch_xs,batch_ys=mnist.train.next_batch(50)
<|repo_name|>huangchenqiu/smarthome<|file_sep|>/src/smarthome/smarthome.py
# -*- coding:utf-8 -*-
"""
File Name : smarthome.py
Author : [email protected]
Date : Oct/21/2017
"""
from __future__ import print_function
import json
import requests
class Smarthome():
def __init__(self,
access_key_id=None,
access_key_secret=None,
region_id=None,
domain=None,
protocol=None,
port=None,
timeout=None):
""" Initialize Smarthome object.
Args:
access_key_id (str): Access Key ID provided by aliyun.
access_key_secret (str): Access Key Secret provided by aliyun.
region_id (str): Region ID provided by aliyun.
domain (str): Domain name provided by aliyun.
protocol (str): Protocol used by aliyun service.
port (int): Port used by aliyun service.
timeout (int): Timeout used when calling aliyun service.
"""
if not access_key_id or not access_key_secret or not region_id
or not domain or not protocol or not port:
raise Exception('Please provide valid parameter.')
if not isinstance(timeout,int):
raise TypeError('Timeout must be int.')
if timeout >0:
timeout=int(timeout/1000)
protocol_str='http'
if protocol == 'https':
protocol_str='https'
url='{protocol}://{domain
