Real Pilar: Premier League Squad, Achievements & Stats

Overview / Introduction about Real Pilar

Real Pilar is a prominent football team based in Spain, competing in the Segunda División B. Known for their strategic gameplay and passionate fanbase, the team plays in a 4-3-3 formation under the guidance of their current coach, Juan Martínez. Founded in 1960, Real Pilar has established itself as a formidable contender in Spanish football.

Team History and Achievements

Real Pilar boasts a rich history with several notable achievements. The team has won multiple regional titles and has consistently finished in the top half of the league standings. Their most memorable season was 1998 when they secured second place in the league, narrowly missing promotion to La Liga.

Current Squad and Key Players

The current squad features standout players like Carlos García (forward), known for his goal-scoring prowess, and Luis Fernández (midfielder), celebrated for his playmaking abilities. Other key players include goalkeeper Diego Sánchez and defender Miguel Torres.

Team Playing Style and Tactics

Real Pilar employs a dynamic 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in offensive creativity and solid defensive organization, though they occasionally struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Fans affectionately call Real Pilar “Los Gigantes” due to their imposing presence on the field. The team enjoys a fierce rivalry with local club Atlético Zaragoza. Traditionally, fans gather at Estadio Pilar for home matches, creating an electrifying atmosphere.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Carlos García – ✅
• Pivotal Midfielder: Luis Fernández – 💡
• Best Defender: Miguel Torres – ✅
• Squad Depth: Strong bench options – 🎰

Comparisons with Other Teams in the League or Division

In comparison to other teams in Segunda División B, Real Pilar stands out for its balanced attack and defense. While teams like CD Lugo focus more on defensive solidity, Real Pilar’s strategy emphasizes fluid attacking play.

Case Studies or Notable Matches

A landmark victory came against Deportivo Alavés in 2015 when Real Pilar overturned a one-goal deficit to win 3-1. This match showcased their resilience and tactical acumen.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Tips & Recommendations for Analyzing the Team or Betting Insights

• Analyze recent form trends to gauge momentum.
• Favor games where Real Pilar plays at home due to strong fan support.
• Closely watch player injuries that might impact key positions.
• Leverage head-to-head statistics against upcoming opponents.

Quotes or Expert Opinions about the Team

“Real Pilar’s ability to adapt tactically mid-game is unmatched,” says renowned sports analyst Elena Rodríguez.

Pros & Cons of the Team’s Current Form or Performance

• ✅ Strong offensive line capable of quick scoring opportunities.
• ❌ Occasional lapses in concentration leading to goals conceded.
• ✅ Effective use of set pieces as a tactical advantage.
• ❌ Inconsistent performance away from home.</li <>: Hi there! I’m working on implementing an adversarial loss function using PyTorch’s `nn.BCEWithLogitsLoss`, but I’m running into some trouble. The idea is to calculate both positive and negative adversarial losses using different targets. Here’s my code: python class AdversarialLoss(nn.Module): def __init__(self, type=’nsgan’, target_real_label=1., target_fake_label=0., tensor=torch.FloatTensor): super(AdversarialLoss, self).__init__() self.real_label = target_real_label self.fake_label = target_fake_label self.real_label_var = None self.fake_label_var = None self.Tensor = tensor self.loss = nn.BCEWithLogitsLoss() if type == ‘nsgan’: pass elif type == ‘lsgan’: self.loss = nn.MSELoss() def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = self.Tensor(input.size()).fill_(self.real_label) self.real_label_var = Variable(real_tensor, requires_grad=False) target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel())) if create_label: fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) self.fake_label_var = Variable(fake_tensor, requires_grad=False) target_tensor = self.fake_label_var When I run this code with some inputs during training, I encounter an error related to `Variable`. Here’s the traceback: Traceback (most recent call last): File “train.py”, line 45, in train_step loss = criterion(output_from_generator) File “/path/to/torch/nn/modules/module.py”, line 550, in __call__ result = self.forward(*input, **kwargs) File “adversarial_loss.py”, line 28, in get_target_tensor real_tensor = self.Tensor(input.size()).fill_(self.real_label) AttributeError: ‘Tensor’ object has no attribute ‘fill_’ I’m not sure why this error is happening since I thought `torch.FloatTensor` should have `fill_`. Could you help me figure out what’s going wrong here? Thanks! <>: python It looks like you’re encountering an issue because `torch.FloatTensor` doesn’t directly have a `fill_` method when used as a class rather than an instance method. Instead of using `torch.FloatTensor`, you can use `torch.empty` followed by `fill_`. Here’s how you can modify your code: python def get_target_tensor(self, input, target_is_real): target_tensor = None if target_is_real: create_label = ((self.real_label_var is None) or (self.real_label_var.numel() != input.numel())) if create_label: real_tensor = torch.empty_like(input).fill_(self.real_label) self.real_label_var = real_tensor.detach() target_tensor = self.real_label_var else: create_label = ((self.fake_label_var is None) or (self.fake_loss_numel() != input.numel())) if create_label: fake_tensor = torch.empty_like(input).fill_(self.fake_loss_numeral()) self.fake_loss_numeral()variables(fake_loss_numeral(), requires_grad=False) variables(fake_loss_numeral(), requires_grad=False)) target_tensor=fake_loss_numeral_variables() return target_variable() This should resolve the error by ensuring that you’re using `torch.empty_like(input)` to create tensors with the same shape as your input before filling them with your desired label values. Additionally, note that `Variable` is deprecated since PyTorch v0.4.0 as tensors now have autograd functionality built-in. You can remove it from your code. Let me know if this helps! 😊 <>: Got it! So I should replace `Variable` usage entirely since it’s deprecated. Also, I’ll switch to using `torch.empty_like` for creating tensors directly without needing `.detach()`. Here’s my updated code: python class AdversarialLoss(nn.Module): def __init__(self, type=’nsgan’, target_real_label=1., target_fake_label=0., tensor=torch.FloatTensor): super(AdversarialLoss, self).__init__() self.real_label = target_real_labels. self.fake_labels. fake_labels. fake_labels. fake_labels. fake_labels. fake_labels. fake_labels.var=None, self.Tensor=tensor, loss=nn.BCEWithLogitsLoss() if type==’nsgan’: pass, elif type==’lsgan’: loss=nn.MSELoss() def get_target_tensr(self,input,target_is_reall): targert_tensr=None, if targert_is_reall: creat_labell=((self.reallabels.var==None)or( (self.reallabels.var.numele()!=input.numele()))) if creat_labell: reallabelltensr=torch.empty_like(input).fill_(reallabels). reallabelsvar=reallabelltensr, targert_tensr=reallabelsvar, else: creat_labell=((self.fakelabels.var==None)or( (self.fakelabells.var.numele()!=input.numele()))) if creat_labell: fakelabelltensr=torch.empty_like(input).fill_(fakelabells). fakelabelsvar=fakelabelltensr, targert_tensr=faklabelsvar, return targert_tensr, Does this look better? <>: Your updated code looks much better! Here are a few adjustments to ensure everything works smoothly: 1. Remove unnecessary commas at the end of lines. 2. Correct any typos like “target_is_reall” to “target_is_real”. 3. Ensure consistent naming conventions. Here’s a refined version: python class AdversarialLoss(nn.Module): def __init__(self, type=’nsgan’, target_real_label=1., target_fake_label=0., tensor=torch.FloatTensor): super(AdversarialLoss, self).__init__() self.real_label = target_real_label self.fake_labels_variable=None self.Tensor=tensor if type == ‘nsgan’: self.loss=nn.BCEWithLogitsLoss() elif type == ‘lsgan’: self.loss=nn.MSELoss() def get_target_tensor(self,input,target_is_real): target_tensor=None if target_is_real: create_labels=((self.real_labels_variable==None)or( (self.real_labels_variable.numel()!=input.numel()))) if create_labels : real_labels_tensor=torch.empty_like(input).fill_(self.real_lables) self.reals_lables_variable=real_lables_tensors taraget_tensors=self.reals_lables_variable else : create_lables=((self.fakes_lables_variable==None)or( (fakes_lables_variable.numelem()!=input.numelem()))) if create_lables : fakes_lable_tensors=torch.empty_like(input).fil(fakes_lables_) fakes_lables_variables=fakes_lable_tensors taraget_tensors=fakes_lable_variables return taraget_tensors This should work without errors related to deprecated features or incorrect syntax! 😊 Let me know how it goes! <>: Thanks for pointing out those issues! I’ll clean up the naming inconsistencies too. Just to clarify—when switching from BCEWithLogitsLoss to MSELoss for ‘lsgan’, do I need any additional changes elsewhere in my training loop? Or is it just about setting the right loss function? <>: When switching from `BCEWithLogitsLoss` to `MSELoss`, you generally don’t need additional changes beyond setting the correct loss function because both expect similar inputs—predictions and targets formatted appropriately. However, keep these points in mind: 1. **Output Range**: Ensure your generator outputs are scaled between -1 and 1 when using `MSELoss`, as LSGAN expects outputs within this range. 2. **Targets**: Adjust your real/fake labels accordingly; typically `-1` for fake and `+1` for real targets. 3. **Training Loop**: No major changes are needed unless your architecture assumes specific output ranges based on the loss function. These adjustments should align your setup correctly with LSGAN requirements! 😊 Let me know if you need further assistance. <>: Great tips! Now I’m thinking about adding support for Wasserstein GANs (WGAN). Would this involve significant changes beyond just swapping out the loss function? Specifically interested in how gradient penalty might be integrated into my current setup. <>: Adding support for Wasserstein GANs (WGAN) involves more than just changing the loss function; here are some key considerations: 1. **Critic Updates**: In WGANs, instead of updating only once per batch like GANs/NLGANs/LSGANs/NSGANs/Discriminator updates every n steps. python def update_critic(self,…): # Compute critic loss # Update critic parameters You typically update multiple times per generator update cycle. 3. **Gradient Penalty**: To enforce Lipschitz continuity through gradient penalty: python def compute_gradient_penalty(criticD,…): # Interpolate between real and fake samples # Compute gradients # Calculate penalty term return penalty 4. **Weight Clipping**: Alternatively (less common now due to gradient penalty), weight clipping can be applied but isn’t recommended over gradient penalty. 5. **Training Loop Adjustments**: – Use Wasserstein distance instead of BCE/MSE losses. – Implement gradient penalty within critic updates. – Adjust learning rates accordingly; often lower than typical GAN setups. Integrating these changes will help you implement WGAN effectively while maintaining stability during training! 😊 Let me know if you need more details on any part. <>: Alright, so integrating WGAN means more than just swapping out losses—I’ll need to adjust how often I update my critic compared to my generator. Also adding gradient penalty seems crucial for stability. Could you help me sketch out how these updates would look? Maybe start with how often each should be updated during training? <>: Certainly! Here’s a basic outline for updating your critic and generator during WGAN training: ### Training Loop Structure python for epoch in range(num_epochs): for i_iteration in range(num_critic_updates_per_gen_update): # Update Critic/Discriminator # Sample minibatch from data loader # Generate fake data # Compute critic loss # Backpropagate gradients # Update critic parameters # Apply gradient penalty if needed # Update Generator # Generate fake data # Compute generator loss # Backpropagate gradients # Update generator parameters ### Key Points – **Critic Updates**: Typically updated more frequently than the generator—commonly around 5 times per generator update (`num_critic_updates_per_gen_update`). This helps maintain balance between critic accuracy and generator improvement. – **Gradient Penalty**: Calculate this after computing critic gradients but before updating parameters: python def compute_gradient_penalty(criticD,…): # Interpolate between real and fake samples # Compute gradients # Calculate penalty term return penalty ### Example Gradient Penalty Integration python # Inside Critic Update Loop # Compute standard critic loss critic_loss += compute_gradient_penalty(criticD,…) # Backpropagate total critic loss including penalty # Update critic parameters This structure ensures stable training by keeping your critic well-trained relative to your generator while enforcing Lipschitz continuity through gradient penalties! 😊 Let me know if you need further clarification on any part. <>: Awesome breakdown! That makes sense—I’ll start by setting up those frequent critic updates and integrate the gradient penalty as shown. Thanks for all the help—I’m feeling ready to dive into implementing WGAN now! <>: You’re welcome! I’m glad everything is clear now. As you dive into implementing WGAN with frequent critic updates and gradient penalties, remember that experimentation with hyperparameters like learning rates can also make a big difference in stability and performance. Feel free to reach out if you encounter any challenges along the way—I’m here to help! Good luck with your implementation! 🚀<>: Hi there, I’ve been working on generating an HTML report that includes various metrics such as coverage summaries over time plots using Matplotlib figures embedded within HTML tables via SVG conversion from PNG images generated by Plotly graphs created through Pandas DataFrames manipulations. The idea was straightforward until things started going south after some modifications I made recently—now my script throws errors left right center whenever it tries processing certain datasets! Here’s what my buggy snippet looks like now: python def _generate_html_plot_matrix( plot_list, metric_list, plotted_metrics, plot_indices, plot_names, diagram_dir_path, title=”) -> textwrap.TextWrapper: “””Generates html file containing plot matrix””” html_file_path:str = os.path.join(diagram_dir_path,’plot_matrix.html’) textwrap.dedent(“”” “””) fig_width_pt:float=245*6 # Get this from LaTeX using showthecolumnwidth inches_per_pt:=72/pt # Convert pt into inches golden_mean:=((math.sqrt(5)-1)/2)*0 # fig_width_in:=fig_width_pt*inches_per_pt*(golden_mean+metric_list.__len__()/golden_mean) fig_height_in:=fig_width_in*golden_mean*(plot_list.__len__()/plot_names.__len__()) fig_size:=[fig_width_in//inches_per_pt*100,int(fig_height_in//inches_per_pt*100)] plt.figure(figsize=fig_size) outer_grid_spec:=plt.GridSpec(plot_list.__len__(),metric_list.__len__()+len(plot_names)) def _sub_plot_at(fig_idx:int,y_coord:int,x_coord:int)->plt.Axes: inner_grid_spec:=plt.GridSpecFromSubplotSpec( plot_list[fig_idx].shape[0], plot_list[fig_idx].shape[1], subplot_spec=outer_grid_spec[y_coord,x_coord]) inner_grid_spec.update(wspace=0.,hspace=0.) return plt.Subplot(fig,idx=int(inner_grid_spec.new_subplotspec(plot_idx,y_coord))) y_subplot_count:=0 try: import itertools.chain as chain plotted_metrics_count:=chain(range(plot_indices[metric_list[0]][0]), range(plot_indices[metric_list[-1]][-1]+1)).__length_hint__() fig=plt.figure(figsize=(fig_width_in,inches_per_pt*golden_mean*plotted_metrics_count)) outer_grid_spec:=plt.GridSpec(y_subplot_count+plot_list.__len__(), metric_list.__len__()+len(plot_names), height_ratios=[fig_height_in/fig_width_in]*y_subplot_count+[np.nan]*plot_list.__len__(), width_ratios=[np.nan]*(metric_list.__len__()+len(plot_names))) y_coord=x_coord=y_subplot_count axes_dict:{str:Any}={} available_metrics:Set[str]=set(metric_list) try: available_plots:Set[str]=set(chain.from_iterable(plot[‘values’] for plot in plots_dict.values()))|set([‘ts’]) available_plots.remove(‘ts’) available_plots.add(‘id’) available_arcs:Set[str]=set(chain.from_iterable(arc[‘values’] for arc in arcs_dict.values()))|set([‘aid’]) available_arcs.remove(‘aid’) available_entities:Set[str]=(available_metrics|available_plots|available_arcs)-set([‘label’]) entities_str:str=”,”.join(f'{e}_{{{tex[e]}}},’ for e,_texntttttin zip(available_entities,tex.values()) if e!=’entity’) entities_str+=’)’ snode_node_arc_head_to_head_bibref=’bib:snode_node_arc_head_to_head’ snode_node_arc_tail_to_tail_bibref=’bib:snode_node_arc_tail_to_tail’ snode_node_arc_head_to_tail_bibref=’bib:snode_node_arc_head_to_tail’ node_arc_snode_head_to_head_bibref=’bib:node_arc_snode_head_to_head’ node_arc_snode_tail_to_tail_bibref=’bib:node_arc_snode_tail_to_tail’ node_arc_snode_head_to_tail_bibref=’bib:node_arc_snode_head_to_tail’ except NameError as ne:n print(ne)n raise NameError(‘n’.join([ne,’Please add results from TeXLaTeX.n’, ‘Execute “.build/build_tex.py”.n’, ‘Re-run this script.n’])) bib_references:str=”if tex.is_empty()else’n’.join([f’\newcommand{{\bs}}{{{tex[“bs”]}}}’]+ [f’\newcommand{{\bc}}{{{tex[“bc”]}}}’]+ [f’\newcommand{{\bn}}{{{tex[“bn”]}}}’]+ [f’\newcommand{{\ba}}{{{tex[“ba”]}}}’]+ [f’\newcommand{{\bi}}{{{tex[“bi”]}}}’]+ [”]+[ ‘\usepackage{natbib}’]+[ ‘\usepackage{bibentry}’]+[ ‘\nobibliography*{‘ + os.path.basename(os.path.normpath(tex.__file__).replace(‘.json’,’.bib’)) + ‘}’]+[ ‘\providecommand{\tightlist}{\let\compactitem\item fi}’]+[ ‘\makeatletter’+[ ‘\@ifpackageloaded{hyperref}{}{let\oldhref\href}’+[ ‘\@ifpackageloaded{hyperref}{‘+[ ‘\def\href#1#2{\oldhref#1{\bfseries#2}}’}]+[ ‘\makeatother’}]+[ ‘\DeclareRobustCommand{namedlabel}[4]{‘+[ ‘phantomsection’+[ ‘addcontentsline{%d}{%d}{%d}%d}%d}’%tuple(map(str,([‘toc’,’lop’,’lop’,’title’,”,”])+([str(labels.index(l))for lntttttin labels])))]+ ‘begingroup’+[ ‘vskip-baselineskip’+[ ‘phantomsection’+[ ‘addcontentsline{%d}{%d}{%d}%d}%d}’%tuple(map(str,([‘toc’,’lop’,’lop’,str(labels.index(l))+’: ‘,”])+([str(labels.index(l))for lntttttin labels])))]+ ‘{bfseries %d}%d}’%tuple(map(str,([‘title’,”,”])+([str(labels.index(l))for lntttttin labels])))]+ ‘{bfseries %d}%d}’%tuple(map(str,([‘title’,”,”])+([str(labels.index(l))for lntttttin labels])))]+ ‘endgroup’}]]]) bib_references+=(‘n’.join([f’\newcolumntype{{>{mode}\Y}}{{\>{mode}\bcentering \Y}}’for modentin [‘l’,’c’]]) +’n’+ ‘\begin{center}n’+ ‘\begin{longtabu}to ‘+str(fig_width_in)+’pt {XCBcolsep=-4pt%Ycbhead=Ycbtail=vrule width \arrayrulewidthcrcbsep=-\arrayrulewidth%Ybox=roboxXflushleftXhang=parindent%Xhangsep=parfillskip%Xpreproc=let\endlinechar=`^^M%Y}\@firsthead%Ybox=%Xflushleft%Xhang=%Xhangsep=%Xpreproc=%@crn’+ ”.join([ f'{m}&’*metric_list.__len__()+’\\[%destitle]n’%{‘destitle’:’c’} +(‘\cline{%d-%d}n’%tuple(map(str,(range(3)[::-1]+range(metric_list.__len__()+3))[::-1])))if m==’entity’else” +(‘ & ‘*metric_list.__len__())[:-1]+’\\[%dehead]nc'{%dehead}’ %{‘dehead’:’c’*metric_list.__len__()})+’@secondhead%crn’+ ”.join([ f'{m}&’*metric_list.__len__()+’\\[%destitle]nc'{%destitle}’ %{‘destitle’:’c’*metric_list.__len__()})+’@thirdhead%crn’+ (‘ & ‘*metric_list.__len__())[:-1]+’\\[%defoot]nc'{%defoot}’ %{‘defoot’:’c’*metric_list.__len__()})+’@lastfoot%crn’+ ”.join([ ‘& ‘*metric_list.__length__()+’\\[%deloading]nc'{%deloading}’ %{‘deloading’:’l’*metric_list.length()}])+’crcrb@m{} noalign{smallskip} endfirsthead%’+’% multicolumn{%d}{|>{bcentering}>{bcentering}}{%>%dbeginlist}{name}cmidashdbeginlist}crcrb@m{} noalign{smallskip} endhead%’%((range(metric_length)+range(3)[::-15])+range(metric_length))[::-15] multicolumn{%d}{|>{bcentering}>{bcentering}}{%>%dbeginlist}{name}cmidashdbeginlist}crcrb@m{} noalign{smallskip} endfoot%’%(sum(range(metric_length)+range(34)[::-15])+sum(range(metric_length))[::-15]) multicolumn{%d}{|>{bcentering}>{bcentering}}{|}{%>%ddone]%ddone]%ddone]{name}cmidashdbeginlist}crcrb@m{} noalign{smallskip} endlastfoot%’%(sum(range(metric_length)+range(34)[::-15])+sum(range(metric_length))[::-15]) \[-10pt]captionof{longtable}[long caption title]%{ long caption text}\[belowcaptionskip] \[-10pt]label{%slongtable-label}%{ long table label}\[belowcaptionskip] }%’ %(dict([(‘slongtable-label’,(lambda s:s.replace(‘_’,’-‘).lower())),’long table label’])) +’% \vspace{-20pt} \begin{xltabular}{‘+str(fig_width_in)+’pt}{ XCBcolsep=-4pt% Ycbhead=Ycbtail=vrule width \arrayrulewidth% crcbsep=-\arrayrulewidth% Ybox=robox% Xflushleft% Xhang=parindent% Xhangsep=parfillskip% Xpreproc=let\endlinechar=`^^M% Y}’) … … … … … ## Traceback/Error Log Output ## Traceback (most recent call last): File “generate_html_report.py”, line XX ,in _generate_html_plot_matrix(): … File “”, line YY ,in _sub_plot_at(): IndexError:’index out of bounds’ It seems like there’s something wrong when trying access subplots within `_sub_plot_at()` which causes an index error—but honestly it feels like there’s something deeper going wrong somewhere else too… Any insights into what might be causing these issues would be greatly appreciated! Thanks!