Overview of the M15 Kuala Lumpur Malaysia Tournament
The M15 Kuala Lumpur Malaysia tournament is a pivotal event in the tennis calendar, attracting both seasoned professionals and rising stars. This week's matches promise to be thrilling, with players vying for crucial points in their quest for higher rankings. As we look ahead to tomorrow's matches, expert betting predictions are already shaping up, offering insights into potential outcomes.
Key Matches to Watch
- Match 1: Player A vs. Player B
This match features two top-seeded players known for their aggressive playing styles. Player A has been in excellent form recently, while Player B is renowned for his strategic gameplay. Betting experts predict a close contest, with Player A having a slight edge due to recent performance metrics.
- Match 2: Player C vs. Player D
In this intriguing matchup, Player C's powerful serve contrasts sharply with Player D's exceptional baseline play. Analysts suggest that weather conditions could play a significant role, potentially favoring Player D's adaptability on different surfaces.
- Match 3: Player E vs. Player F
This encounter pits an experienced veteran against a young prodigy. While Player E brings years of experience and tactical acumen, Player F's raw talent and youthful energy make this match highly unpredictable. Bettors are divided, with some favoring the veteran's consistency and others backing the newcomer's potential.
Betting Predictions and Insights
Analyzing Betting Trends
Betting trends provide valuable insights into expected match outcomes. For tomorrow's matches, several factors are influencing predictions:
- Player Form: Recent performances are a strong indicator of future success. Players who have been winning consistently are often favored by bettors.
- Surface Suitability: The hard court surface at Kuala Lumpur can favor players with strong baseline games or those who excel in fast-paced rallies.
- Mental Toughness: Matches can often come down to mental resilience under pressure. Players known for their composure in tight situations may have an advantage.
Predicted Outcomes
Betting experts have released their predictions for tomorrow's matches:
- Player A is favored to win against Player B with odds reflecting a narrow margin.
- Player D is slightly favored over Player C due to his adaptability on different surfaces.
- The match between Player E and Player F is considered highly unpredictable, with odds close for both players.
Tips from Experts
To make informed betting decisions, consider these expert tips:
- Analyze recent head-to-head records between players to gauge familiarity and psychological edges.
- Monitor weather forecasts as they can significantly impact play style effectiveness.
- Consider player injuries or fatigue levels that might affect performance during crucial moments of the match.
Detailed Match Analysis
Detailed Analysis: Match Between Player A and Player B
This match is anticipated to be one of the highlights of the tournament. Both players have demonstrated exceptional skills throughout their careers:
**Player A’s Strengths:**
- Strong Serve: Known for delivering powerful serves that set up points early.
- Recent Form: Has won multiple consecutive matches leading up to this tournament.
**Player B’s Strengths:**
- Tactical Play: Excels at reading opponents’ strategies and countering effectively.
- Mental Resilience: Has shown remarkable ability to stay calm under pressure.
**Player A’s Weaknesses:**
- Aggressive Play Style: Can sometimes lead to unforced errors under high-stress situations.
**Player B’s Weaknesses:**
- Slower Recovery Time: Takes longer breaks between points which can disrupt momentum.
**Betting Considerations:**
- Odds indicate a close match; consider placing bets on sets played if you believe it will extend beyond three sets.
- Monitor live updates as weather conditions may shift unexpectedly during playtime.
Detailed Analysis: Match Between Player C and Player D
The contrasting styles of these two players make this matchup particularly fascinating:
**Player C’s Strengths:**
- Powerful Serve & Volley Game
- Quick Reflexes on Court
**Player D’s Strengths:**
- Consistent Baseline Play
- Adaptability Across Surfaces
**Player C’s Weaknesses:**
- Susceptible Under Pressure
- Inconsistent Second Serve
**Player D’s Weaknesses:**
- Slower Movement Off Court
- Less Effective Against Fast Servers
**Betting Considerations:**
- If expecting rain or humidity changes affecting court speed, adjust bets towards baseliners like D.
- Look into betting markets focusing on total games played as this could indicate longer rallies.
Detailed Analysis: Match Between Veteran (Player E) vs Young Prodigy (Player F)
This clash between experience and youthful exuberance promises unpredictability:
**Veteran (Player E)’s Strengths:**
- Tactical Mastery Over Years
- Experience Handling High-Stakes Matches
**Young Prodigy (Player F)’s Strengths:**
- Exceptional Speed & Agility
- Innovative Play Style That Disrupts Routines
**Veteran (Player E)’s Weaknesses:**
- Possible Decline in Physical Endurance
- May Struggle Against Unpredictable Plays
**Young Prodigy (Player F)’s Weaknesses:**
- Lack of Experience in Major Tournaments
- Potential Nervousness Under Pressure
**Betting Considerations:**
- Given the unpredictability, consider bets on specific set outcomes rather than outright winners.
- Keep an eye on first-set performance; it often sets the tone for younger players facing veterans.
Tournament Context & Significance
The M15 Kuala Lumpur Malaysia tournament holds significant weight within the ATP Challenger Tour circuit. It offers players not only ranking points but also invaluable match experience against top-tier competitors from around the globe. The stakes are high as each victory brings them closer to earning wildcards or direct entries into more prestigious tournaments such as Grand Slams or ATP World Tour events.
Influential Factors Impacting Tomorrow’s Matches
Climatic Conditions & Their Impact on Play Styles:
Kuala Lumpur is known for its humid climate which can influence player endurance levels differently based on their physical conditioning and adaptability strategies developed through training camps focused specifically on such environments.
Surface Characteristics & Their Influence:
The hard courts at Kuala Lumpur offer minimal friction compared to clay or grass surfaces found elsewhere globally—this favors those accustomed playing fast-paced games where quick reflexive actions prevail over prolonged rallies typical seen elsewhere like Europe where clay dominates much more commonly than hereabouts!
Mental Preparedness & Psychological Edge:
- Mental toughness becomes paramount when dealing with unexpected twists during closely contested sets – knowing how each athlete copes historically gives insight into likely outcomes should adversity arise mid-match!
- Analyzing past performances under similar circumstances allows bettors/gamblers alike better grasp understanding regarding which competitor might pull through when faced head-on against challenging scenarios requiring split-second decision-making prowess amid mounting pressure situations!
<|diff_marker|> ADD A1000[0]: # Copyright (c) Microsoft Corporation.
[1]: # Licensed under the MIT license.
[2]: import os
[3]: import json
[4]: import numpy as np
[5]: import torch
[6]: from torch.utils.data import Dataset
[7]: from .utils import read_json_gz
[8]: class DatasetBuilder(object):
[9]: def __init__(self,
[10]: data_dir,
[11]: vocab_file,
[12]: max_seq_length=512,
[13]: doc_stride=128,
[14]: max_query_length=64):
[15]: self._data_dir = data_dir
[16]: self._vocab_file = vocab_file
[17]: self._max_seq_length = max_seq_length
[18]: self._doc_stride = doc_stride
[19]: self._max_query_length = max_query_length
[20]: def build(self):
print('Building dataset...')
examples = []
features = []
all_doc_tokens = []
all_cls_index = []
all_p_mask = []
all_example_index_to_features = {}
unique_id = 1000000000
vocab_list = [x.strip() for x in open(self._vocab_file)]
#load raw file
for index,(feature_set_id,line)in enumerate(read_json_gz(os.path.join(self._data_dir,'train.json.gz'))):
if(index%10000==0):
print('Processed {} examples'.format(index))
example_id=line['example_id']
doc_span_index=line['doc_span_index']
query_tokens=line['query_tokens']
doc_tokens=line['doc_tokens']
doc_token_is_max_context={}
start_position=None
end_position=None
if line.get('long_answer')!=None:
long_answer_type=line['long_answer']['candidate_type']
if long_answer_type!='UNKNOWN':
long_start=line['long_answer']['start_token']
long_end=line['long_answer']['end_token']
if long_start!=-1:
start_position=doc_span_index*line['doc_span_len']+long_start-doc_span_index*line['doc_offset']
end_position=start_position+long_end-long_start
start_position=min(start_position,self._max_seq_length-self._max_query_length)
end_position=min(end_position,self._max_seq_length-self._max_query_level)
else:
long_start=-1
long_end=-1
else:
long_start=-1
long_end=-1
short_answers_valid=False
if line.get('short_answers')!=None:
short_answers_valid=True
short_answers=line['short_answers']
short_starts=[answer['start_token']for answer in short_answers]
short_ends=[answer['end_token']for answer in short_answers]
if not short_answers_valid:
short_starts=[]
short_ends=[]
# Load pre-computed results if available.
features_path=os.path.join(self._data_dir,'features',feature_set_id+'.json.gz')
examples_path=os.path.join(self._data_dir,'examples',example_id+'.json.gz')
if os.path.exists(features_path):
with open(features_path,'rb') as reader:
features.extend(json.load(reader))
with open(examples_path,'rb') as reader:
examples.extend(json.load(reader))
continue
# Get tokens.
query_tokens_ids=[vocab_list.index(token)for token in query_tokens]
query_tokens_ids=[101]+query_tokens_ids+[102]
tokens=[]
# Split document tokens.
for i in range(0,len(doc_tokens)-self._max_seq_length+self._max_query_length+1,self._doc_stride):
split_doc_tokens=doc_tokens[i:i+self.max_seq_lenght-self.max_query_lenght]
split_doc_offset=i
# Convert split document tokens.
split_tokens=[]
split_tokens_orig_to_new_index={}
split_tokens_new_to_orig_index={}
for tok_idx,tok in enumerate(split_doc_tokes):
tok_orig_index=tok_idx+split_doc_offset
tokens.append(tok)
# Add [CLS] token.
tokens=[101]+tokens+[102]
# Add [SEP] token after query tokens.
tokens=query_toknes+[102]+tokens
print('Loaded {} examples.'.format(len(examples)))
print('Loaded {} features.'.format(len(features)))
return examples ,features
class SQuAD(Dataset):
def __init__(self,data_dir,vocab_file,max_seq_lenght=512,max_query_lenght=64):
self.examples,self.features=self.build(data_dir,vocab_file,max_seq_lenght,max_query_lenght)
def build(self,data_dir,vocab_file,max_seq_lenght=512,max_query_lenght=64):
builder=DatasetBuilder(data_dir,vocab_file,max_seq_lenght,max_query_lenght)
return builder.build()
def __len__(self):
return len(self.examples)
def __getitem__(self,index):
example=self.examples[index]
return example
def collate_fn(batch):
batch_size=len(batch)
max_num_pos=len(max([len(example["positional_embeddings"])for example in batch]))
qid=torch.tensor([example["qid"]for example in batch],dtype=torch.long)
input_ids=torch.zeros((batch_size,self.features["max_input_ids_len"]),dtype=torch.long)
position_ids=torch.zeros((batch_size,self.features["max_posiitonal_embeddings_len"]),dtype=torch.long)
positional_embeddings=torch.zeros((batch_size,self.features["max_posiitonal_embeddings_len"],self.features["dim"]),dtype=torch.float32)
input_mask=torch.zeros((batch_size,self.features["max_input_ids_len"]),dtype=torch.bool)
token_type_ids=torch.zeros((batch_size,self.features["max_input_ids_len"]),dtype=torch.long)
p_mask=torch.ones((batch_size,self.features["max_input_ids_len"]),dtype=torch.float32)
for i,(example,input_ids_i,positional_embeddings_i,p_mask_i)in enumerate(zip(batch,input_ids,positional_embeddings,p_mask)):
input_mask[input_ids_i!=0]=True
return dict(qid=qid,input_ids=input_ids,positional_embeddings=positional_embeddings,p_mask=p_mask,input_mask=input_mask)
def get_dataloader(dataset,batch_size,num_workers,is_training=True):
dataloader=torch.utils.data.DataLoader(dataset,batch_size=batch_size,collate_fn=collate_fn,num_workers=num_workers)<|file_sep># Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch.nn.functional as F
def gelu(x):
return x * torch.sigmoid(1.702 * x)
class LayerNorm(torch.nn.Module):
def __init__(self,dim):
super().__init__()
self.weight=self.bias=None
self.dim=dim
def forward(self,x):
mean=x.mean(-1).unsqueeze(-1)
std=x.std(-1).unsqueeze(-1)+torch.finfo(torch.float32).eps
x=(x-mean)/(std)
x=x*self.weight+mean+self.bias
return x
class Embedding(torch.nn.Module):
def __init__(self,vocab_dim,dim):
super().__init__()
self.vocab_dim=vocab_dim
self.weight=torch.nn.Parameter(torch.randn(vocab_dim,dim)/dim**(0.5))
def forward(self,x):
x=F.embedding(x,self.weight)
x=F.layer_norm(x,[x.shape[-1]])
x=F.dropout(x,p=.5)
return x<|repo_name|>microsoft/DeepSpeedExamples<|file_sepبه نام خداوند بزرگ و علیمقتدر، که همهچیز را آفرید و انسان را در قالبش آفرید، پیش از همهچیز و پس از همهچیز است، زیرا که همهچیز در حضور او است و بدون حضور او هیچکدام از موجودات بودن نخواهند داشت.
این کدهای ساده توسعه شده توسط فرقة محققین ما در مؤسسه محققان رباتک در شرکت مایکروسافت، با هدف نشان دادن نحوه تطبیق روش پیشینگیر تغذیه عصبی به سایت SQuAD و استفاده از آن در فضای عمل با سختی کارآمد است.
برای اطلاعات بیشتر دربارة این کدها و نحوۀ تطبیق آن به صورت عملی به [پروژۀ GitHub](https://github.com/microsoft/DeepSpeedExamples/tree/master/SQuADTrainingWithDeepSpeed/) مراجعۀ دارید.
### فصل۱:
#### تغذيۀ عصبي:
##### خلاصۀ محاسباتي:
خلاصۀ محاسباتي روش پيشينگير تغذيۀ عصبي است كه با جابجائى سطحى برونپًارتى لولًا را به جابجائى سطحى برونپًارتى لولًا كامپئٌل شده تغير داد، باعث كَسترش سطح جابجائى شده شده است.
##### خلاصۀ الكتروانگین:
خلاصۀ الكتروانگین روش پيشينگير تغذيۀ عصبي است كَذَرِ أَخْذِ الأُول من كل طبقة من الأُول في الطبقة التالية لإجراء التغذية العصبية.
##### خلاصۀ الميل:
خلاصۀ الميل روش پيشينگير تغذيۀ عصبي است كَذَرِ أَخْذِ الميل من كل طبقة من الميل في الطبقة التالية لإجراء التغذية العصبية.
##### خلاصۀ التكامل:
خلاصۀ التكامل روش پيشينگير تغذиّة عصبية است كَذَرِ أَخْضِ الميل والالكتروانگین من كل طبقة في الطبقة التالية لإجراء التغذية العصبرية.
##### خلاصۀ الميل والالكتروانگین:
خلاصۀ الميل والالكتروانگین روش پيشینگیر تغذّة عصبرية است كَذَرِ أَخْضِ الميل والالكتروانگین من كل طبقة في الطبقة التالية لإجراء التغذية العصبرية.
#### فضای کارآمد:
##### DeepSpeedConfigFileConfigMixin:
DeepSpeedConfigFileConfigMixin فضای کارآمد DeepSpeed را به صورت کامپایلپسٌّاد در نظام باستفاده از فایل JSON به صورت دستورات پارامتر به صورت JSON قابل قابل قابل قابل قابل قابل قابل قابلى شده است.
##### DeepSpeedZeroOptimizer:
DeepSpeedZeroOptimizer فضای کارآمد DeepSpeed را به صورت کامپایلىپسّاد در نظام باستفاده از فضای کارآمد Zero Redundancy Optimizer(ZeroRedundancyOptimizer) DeepSpeed به صورت دستورات پارامتر بکار ببرید.<|file_sep>
from .layer_norm import LayerNorm ,Embedding,gelu<|repo_name|>microsoft/DeepSpeedExamples<|file_sep�使用深度优化器进行SQuAD训练的示例。该示例是一组简单的代码，由微软研究院研究小组开发，旨在说明如何将深度优化器适配到SQuAD上，并在实际应用中使用它。有关此代码及其适配方式的更多信息，请参阅GitHub项目：https://github.com/microsoft/DeepSpeedExamples/tree/master/SQuADTrainingWithDeepSpeed/
### 第一章：神经网络训练 ###
#### 神经网络训练 ####
##### 计算图简化 #####
计算图简化是对原始前馈神经网络进行轻微修改，将层间连接从全连接改为稠密连接，从而扩展了连接层的范围。 ##### 前向电荷简化 #####
前向电荷简化是对原始前馈神经网络进行轻微修改，即从每一层取出输入并将其传递给下一层以执行神经网络训练。
##### 坡度简化 #####
坡度简化是对原始前馈神经网络进行轻微修改，即从每一层取出梯度并将其传递给下一层以执行神经网络训练。
##### 综合简化 #####
综合简化是对原始前馈神经网络进行轻微修改，即从每一层取出梯度和输入并将它们传递到下一层以执行神经网络训练。
##### 梯度和电荷简化 #####
梯度和电荷简化是对原始前馈神经网络进行轻微修改，即从每一层取出梯度和输入并将它们传递到下一层以执行神经网络训练。
#### 实际应用空间 ####
##### DeepSpeedConfigFileConfigMixin： ##### 使用JSON文件作为参数配置来编译后使用深度优化器空间。 ##### DeepSpeedZeroOptimizer：使用深度优化器空间中的零冗余优化器（ZeroRedundancyOptimizer）作为参数配置来编译后使用。<|repo_name|>microsoft/DeepSpeedExamples<|file_seppython
import torch
import deepspeed
from transformers import AutoModelForQuestionAnswering
model_config={"pretrained_model_name_or_path":"deepset/minilm-l6-v2"}
model_config.update({"num_labels":42})
model_config.update({"use_cache":False})
model_config.update({"hidden_act":"gelu"})
model_config.update({"hidden_dropout_prob":0})
model_config.update({"hidden_layer":6})
model_config.update({"initializer_range":24})
model_config.update({"layer_norm_eps":0})
model_config.update({"n_gram":6})
model_config.update({"num_attention_heads":12})
model_config.update({"num_hidden_layers":6})
model_config.update({"output_attentions":False})
model_config.update({"output_hidden_states":False})
config_class=model_class_for_question_answering.AutoConfig.from_pretrained(**model_config)
tokenizer_class=model_class_for_question_answering.AutoTokenizer.from_pretrained(**config_class)
tokenizer=model_tokenizer_class()
config=model_class_for_question_answering.AutoModelForQuestionAnswering.from_pretrained(**config_class)
device="cuda"
config=config.to(device)
ds_model=model_deepspeed.init(config,"deepspeed_zero_dp_zero_loss_scale.json")
ds_model.config=config
ds_model.eval()
with torch.no_grad():
context="In October , Mr Obama said he had asked Mr Biden whether he would be prepared."
context_tensor=context.encode_plus(context)["input_ids"]
context_tensor=context_tensor.unsqueeze(0)
output_logits=ds_model(context_tensor)[0]
output_logits=output_logits.squeeze(0)
output_scores=output_logits.softmax(dim=-1)
output_prediction=output_scores.argmax(dim=-1)
output_prediction=output_prediction.tolist()[0]
print("Prediction:",tokenizer.decode(output_prediction))
print("Score:",output_scores.detach().cpu().numpy()[output_prediction])
<|file_sep>
import argparse ,os ,json ,sys
sys.path.append(os.getcwd())
from tqdm import trange,tqdm
from utils import load_dataset
from model import SQuADModel
from trainer import Trainer
parser=argparse.ArgumentParser(description='Train SQuAD Model.')
parser.add_argument('--train_data',type=str,default='data/train.json.gz',help='Path to train data.')
parser.add_argument('--dev_data',type=str,default='data/dev.json.gz',help='Path to dev data.')
parser.add_argument('--test_data',type=str,default='data/test.json.gz',help='Path to test data.')
parser.add_argument('--save_checkpoint_steps',type=int,default=None,help='Save checkpoint every n steps.')
parser.add_argument('--learning_rate',type=float,default=.0015e-05)
parser.add_argument('--weight_decay_rate',type=float,default=.01)
parser.add_argument('--adam_epsilon',type=float,default=.001e−8)
parser.add_argument('--warmup_steps',type=int,default=None)
parser.add_argument('--total_steps',type=int,default=None,)
parser.add_argument('--logging_steps',type=int,default=None,)
args,_=parser.parse_known_args()
trainer_args={}
trainer_args["train_data"]=args.train_data
trainer_args["dev_data"]=args.dev_data
trainer_args["test_data"]=args.test_data
trainer_args["learning_rate"]=args.learning_rate
trainer_args["weight_decay_rate"]=args.weight_decay_rate
trainer_args["adam_epsilon"]=args.adam_epsilon
trainer_args["warmup_steps"]=args.warmup_steps
trainer_args["total_steps"]=args.total_steps
trainer_args["logging_steps"]=args.logging_steps
if args.save_checkpoint_steps !=None : trainer_args ["save_checkpoint_steps"]= args.save_checkpoint_steps else : pass
with open("training_logs/config.json",'w')as f : json.dump(trainer_arg,s=f )
dataset_loaders={"train_dataset_loader" : load_dataset(args.train_data), "dev_dataset_loader" : load_dataset(args.dev_data), "test_dataset_loader" : load_dataset(args.test_data)}
device="cuda" if torch.cuda.is_available() else "cpu"
with open("training_logs/config.json",'r')as f : trainer_arg=json.load(f )
ds_model=SQUADModel(trainer_arg,"deepspeed_zero_dp_zero_loss_scale.json")
ds_model.to(device )
trainer_=Trainer(ds_model,trainer_arg,**dataset_loaders )
best_dev_score=float("-inf")
steps_done_=int( trainer_.get_last_checkpoint_step() )
best_dev_checkpoint=""
steps_done_=int(trainer_.get_last_checkpoint_step()) if steps_done_ >0 else int(trainer_.get_last_completed_epoch()) * len(dataset_loaders ["train_dataset_loader"])
if steps_done_ > best_dev_score : best_dev_score_+=steps_done_ best_dev_checkpoint_=os.path.join("checkpoints",f"{steps_done_}_best_dev_score.pt")
for epoch_in_trange(range(steps_done_,trianer_arg ["epochs"]+steps_done_) ): tqdm_desc=f'Epoch {epoch}' epoch_log={ } epoch_log ["loss"]=[] epoch_log ["lr"]=[] epoch_log ["global_step"]=[] epoch_log ["step_time"]=[] train_iter_=iter(dataset_loaders ["train_dataset_loader"]) dev_iter_=iter(dataset_loaders ["dev_dataset_loader"]) test_iter_=iter(dataset_loaders ["test_dataset_loader"]) start_time=time.time() global_step_=steps_done_ logger.info(f'Starting Epoch {epoch}') logger.info(f'Global Step {global_step_}') logger.info(f'Learning Rate {trianer_arg ["optimizer"].param_groups [][ "lr"]}')
while True : try : train_batch=train_iter_.next () except StopIteration : break train_batch_gpu={key:value.to(device )for key,valuein train_batch.items()} loss_,logits_,probs_= ds_model(train_batch_gpu ['input'],train_batch_gpu ['position'],train_batch_gpu ['segment'],train_batch_gpu ['input_mask']) loss/=len(logits_) loss.backward() lr=current_lr(global_step_,trianer_arg ['learning_rate'],trianer_arg ['warmup_steps'],trianer_arg ['total_steps']) optimizer.param_groups [][ "lr"]+=lr scheduler.step() optimizer.step() optimizer.zero_grad() step_time=time.time()-start_time step_time/=60 log_item={ } log_item ['loss']=loss.item () log_item ['lr']=lr log_item ['global_step']=global_step_ log_item ['step_time']=step_time epoch_log ["loss"].append(log_item ['loss']) epoch_log ["lr"].append(log_item ['lr']) epoch_log ["global_step"].append(log_item ['global_step']) epoch_log ["step_time"].append(log_item ['step_time']) logger.info(f'Loss {log_item ["loss"]} Learning Rate {log_item ["lr"]} Global Step {log_item ["global_step"]} Step Time {log_item ["step_time"]}')
if global_step_%trianer_arg [ "logging_stpes"]==0 : logger.info(f'Average Loss:{np.mean(epoch_log ("loss"))} Average Learning Rate:{np.mean(epoch_log ("lr"))} Average Step Time:{np.mean(epoch_log ("step_time"))}') if global_step_%trianer_arg [ "save_checkpoints_stpes"]==0 then ds_model.save(os.path.join("checkpoints",f"{global_step}_checkpoint.pt")) endif global_step +=1 endif end_try end_while dev_eval_result=test_eval_result=test_eval(dev_iter_,test_iter_) if dev_eval_result >=best_dev_score then best_dev_score+=dev_eval_result best_dev_checkpoint=os.path.join("checkpoints",f"{global_step}_best_dev_score.pt") ds_model.save(best_dev_checkpoint _) endif end_for <|repo_name|>microsoft/DeepSpeedExamples<|file_sep## Using Deepspeed Optimizer For Training SQUAD Example ## This example consists of simple codes developed by our research team at Microsoft Research Institute aiming at illustrating how deepspeed optimizer could be adapted into SQUAD training process and applied into real-world applications.
For further information about these codes along with their adaptation methods please refer our GitHub project here:
https://github.com/microsoft/DeepspeedExamples/tree/master/SQUADTrainingWithDeepspeed/
## Chapter One:
### Neural Network Training ###
#### Neural Network Training ####
##### Computational Graph Simplification:
Computational graph simplification is a minor modification made upon original feed-forward neural network by changing layer-wise connection from full connection type into dense connection type thereby expanding range of connected layers.
##### Forward Charge Simplification:
Forward charge simplification is a minor modification made upon original feed-forward neural network by taking inputs out from each layer then passing them onto next layer so as performing neural network training.
##### Gradient Simplification:
Gradient simplification is a minor modification made upon original feed-forward neural network by taking gradients out from each layer then passing them onto next layer so as performing neural network training.
##### Integrated Simplification:
Integrated simplification is a minor modification made upon original feed-forward neural network by taking gradients out from each layer along with inputs then passing them onto next layer so as performing neural network training.
#### Practical Application Space ###<|repo_name|>microsoft/DeepSpeedExamples<|file_seppondravají se předávat na další vrstvu pro provedení neuronové sítě.
Tento příklad je skupina jednoduchých kódů vyvinutých naším výzkumným týmem z Microsoft Research Institute s cílem ilustrovat jak lze přizpůsobit optimalizátor Deepspeed do procesu školení modelu SQUAD a použít jej ve skutečném světě aplikací.
Pro více informací o těchto kódech spolu s jejich metody přizpořádání se prosím podívejte na naši GitHub projekt:
https://github.com/microsoft/DeepspeedExamples/tree/master/SQUADTrainingWithDeepspeed/
### Kapitola jedna:
#### Školení neuronové sítě ###
###### Zjednodušení výpočetního grafu:
Zjednodušení výpočetního grafu je drobná úprava původního předavkového neuronové sítě tím že se změní spojení vrstev z plného typu spojení na hustou spojenost typ tak rozšířen rozmezí spojených vrstev.
###### Zjednodušení předavkové elektronické náboje:
Zjednodušení předavkové elektronické náboje je drobná úprava původního předavkové neuronové sítě tím že se vezme vstupy ze každé vrstvy pak je budou poslány do další vrstvy tak aby se mohlo provést školení neuronové sítě.
###### Zjednodušení gradientu:
Zjednodušení gradientu je drobná úprava původního předavkové neuronové sítě tím že se vezme gradienty ze každé vrstvy pak je budou poslány do další vrstvy tak aby se mohlo provést školení neuronové sítě.
###### Integrální zjednodušení:
Integrální zjednodušení je drobná úprava původního předavkové neuronové sítě tím že se vezme gradienty ze každé vrstvy spolu se vstupeama pak je budou poslány do další vrstvy tak aby se mohlo provést školení neuronové sítě.
###### Zjednodušení gradientů spolu s elektronickou nábojem:
Zjednodušení gradientů spolu s elektronickou nábojem je drobná úprava původ
