National League Women stats & predictions

Upcoming Volleyball National League Women Matches in Kazakhstan

Tomorrow promises to be an exhilarating day for volleyball fans across Kazakhstan as the National League Women gears up for a series of thrilling matches. With teams battling fiercely for supremacy, each game is not just a test of skill but also a spectacle of strategy and athleticism. As the anticipation builds, let's delve into the details of these matches, exploring team dynamics, key players, and expert betting predictions that could guide enthusiasts in their viewing experience.

Match Highlights and Team Analysis

The league's top contenders are set to clash on the court, each bringing their unique strengths and strategies. The Almaty Eagles, known for their robust defense and strategic plays, face off against the Astana Aces, whose aggressive offense has been a highlight this season. Meanwhile, the Nur-Sultan Ninjas aim to leverage their experienced lineup to secure a victory against the Shymkent Sharks.

• Almaty Eagles vs Astana Aces: This match is expected to be a defensive showdown. The Eagles' ability to disrupt offensive plays will be tested against the Aces' powerful hitters.
• Nur-Sultan Ninjas vs Shymkent Sharks: With both teams having strong backcourt players, this match could turn into a high-scoring affair.

Key Players to Watch

Each team boasts players who have consistently delivered outstanding performances throughout the season. Keep an eye on these athletes who could tip the scales in favor of their teams:

• Anastasia Petrova (Almaty Eagles): Known for her impeccable serving skills and tactical acumen.
• Maria Ivanova (Astana Aces): A formidable spiker with an impressive record of successful attacks.
• Svetlana Kuznetsova (Nur-Sultan Ninjas): Renowned for her defensive prowess and ability to read the game.
• Darya Smirnova (Shymkent Sharks): A versatile player capable of making significant contributions both in attack and defense.

Betting Predictions: Expert Insights

As always, betting adds an extra layer of excitement to these matches. Based on current form and historical performance, here are some expert predictions:

• Total Points Over/Under: For Almaty Eagles vs Astana Aces, experts predict a tight contest with total points likely under par due to strong defensive play.
• Match Winner: The Nur-Sultan Ninjas are favored to win against the Shymkent Sharks based on recent victories and home-court advantage.
• Tie Bet: Given the evenly matched nature of some teams this season, consider tie bets as potential value picks.

Tactical Breakdowns

I am trying out different optimizers with my deep learning model using Keras/Tensorflow backend.
I have tried Adam optimizer which works fine but when I try RMSprop optimizer I get NaN loss values after some epochs.
My model is based on ResNet architecture.
Can anyone help me figure out why RMSprop optimizer gives NaN loss values?

I am attaching my code below:

#imports

from keras.models import Model
from keras.layers import Input
from keras.layers.core import Dense
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Dropout
from keras.optimizers import RMSprop

#ResNet Model

def residual_network(input_shape,
                     num_classes,
                     depth=20,
                     width=16):

     assert((depth - 2) %6 ==0)
     N = int((depth -2)/6)

     img_input = Input(shape=input_shape)

     x = Conv2D(width,(1 ,1),padding='same')(img_input)
     x = BatchNormalization()(x)
     x = Activation('relu')(x)

     x = _residual_block(x,N,width)

     x = _residual_block(x,N,width*2,strides=(1 ,1))

     x = _residual_block(x,N,width*4,strides=(1 ,1))

     x = GlobalAveragePooling2D()(x)
 
 
 
 
 
 
 
 

def _residual_block(_input,num_residual_blocks,filters,strides=(1 ,1)):
    
        
        x=_input
    
        
        for i in range(num_residual_blocks):
            
            x=_identity_block(x,filters,strides=strides)
            
        return x

def _identity_block(_input,filters,strides=(1 ,1)):

    

    
        x=Conv2D(filters,(3 ,3),strides=strides,padding='same')(_input)
    
        
        x=BatchNormalization()(x)
    
        
        x=Activation('relu')(x)

    
        
        x=Conv2D(filters,(3 ,3),strides=strides,padding='same')(x)
    
        
        x=BatchNormalization()(x)

    
    
        
        x=add([_input,x])
    
    
        
        x=Activation('relu')(x)

    
    
        
        return x


#data generator

train_datagen = ImageDataGenerator(
                             rescale=1./255.,
                             rotation_range=20,
                             width_shift_range=0.05,
                             height_shift_range=0.05,
                             shear_range=0.05,
                             zoom_range=0.05,
                             horizontal_flip=True)


valid_datagen = ImageDataGenerator(rescale=1./255.)

train_generator=train_datagen.flow_from_directory(
                                                train_dir,target_size=image_size,batch_size=batch_size,class_mode='categorical')

validation_generator=valid_datagen.flow_from_directory(
                                                validation_dir,target_size=image_size,batch_size=batch_size,class_mode='categorical')

#model compilation

model=residual_network(input_shape,image_size,num_classes=num_classes).build()

model.compile(optimizer=RMSprop(lr=.001),loss='categorical_crossentropy',metrics=['accuracy'])

#training 

history=model.fit_generator(train_generator,
                            steps_per_epoch=int(np.ceil(train_samples/batch_size)),
                            epochs=num_epochs,
                            validation_data=validation_generator,
                            validation_steps=int(np.ceil(validation_samples/batch_size)))