Ligue 1 stats & predictions
Ligue 1 Senegal: Tomorrow's Matches and Expert Betting Predictions
The Ligue 1 Senegal season is heating up, and tomorrow promises to be an exciting day for football fans across the nation. With several key matches lined up, enthusiasts are eager to see how their favorite teams will perform. This article delves into the scheduled matches, offering expert betting predictions to guide fans in making informed decisions.
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Match Overview
Tomorrow's lineup features a mix of top-tier teams battling for supremacy in the league. The fixtures include high-stakes encounters that could significantly impact the standings. Here’s a detailed look at the matches:
- Team A vs Team B: A classic rivalry that never fails to deliver thrilling moments. Both teams are neck and neck in the league table, making this match crucial for maintaining momentum.
- Team C vs Team D: Known for their defensive prowess, Team C faces a formidable challenge against Team D's attacking lineup. This encounter is expected to be a tactical battle.
- Team E vs Team F: With both teams looking to climb up the table, this match is pivotal for their playoff aspirations. Fans can anticipate an aggressive approach from both sides.
Betting Predictions
Expert analysts have weighed in on tomorrow’s matches, providing insights that could help bettors make strategic choices:
Team A vs Team B
- Prediction: Draw (1X2)
- Rationale: Both teams have shown resilience throughout the season, with recent performances indicating strong defensive setups.
- Over/Under Goals: Over 2.5 Goals
- Rationale: Historical data suggests that matches between these two often result in high-scoring affairs due to their offensive capabilities.
Team C vs Team D
- Prediction: Team D to Win (1X2)
- Rationale: Team D has been in excellent form recently, with key players returning from injury and adding depth to their squad.
- Over/Under Goals: Under 2.5 Goals
- Rationale: Given Team C's defensive strength and Team D's focus on counter-attacks, a low-scoring game is anticipated.
Team E vs Team F
- Prediction: Both Teams to Score (BTTS)
- Rationale: Both teams have potent attacking lines that have consistently found the back of the net throughout the season.
- Over/Under Goals: Over 2.5 Goals
- Rationale: The aggressive playstyles of both teams suggest a high likelihood of multiple goals being scored.
Detailed Analysis of Key Players
In addition to team dynamics, individual performances can significantly influence match outcomes. Here’s a closer look at some key players:
Star Striker of Team A
This player has been instrumental in securing victories with his clinical finishing skills. His ability to convert chances into goals makes him a critical asset for his team.
Midfield Maestro of Team D
Known for his vision and passing accuracy, this midfielder orchestrates plays and controls the tempo of the game. His leadership on the field is invaluable for guiding his team through challenging situations.
Tactical Insights
Analyzing team strategies provides further depth into how these matches might unfold:
Defensive Strategies of Team C
Team C’s success often hinges on their solid defensive line and tactical discipline. Their ability to absorb pressure and launch quick counter-attacks can unsettle even the most formidable opponents.
Offensive Playstyle of Team F
With an emphasis on fast-paced attacking football, Team F relies on swift transitions from defense to attack. Their dynamic forwards are adept at exploiting spaces left by opposing defenses.
Historical Match Data and Trends
A review of past encounters between these teams reveals patterns that could influence tomorrow’s outcomes:
- Team A vs Team B: Historically, this matchup has resulted in closely contested games with an average goal tally slightly above two per match.
- Team C vs Team D: Previous meetings have seen a trend towards low-scoring games, often decided by a single goal margin.
- Team E vs Team F: These fixtures tend to be high-energy affairs with both teams frequently scoring multiple goals.
Betting Tips and Strategies
To maximize potential returns from betting on these matches, consider these strategies:
- Diversify Bets: Spread your bets across different markets such as outright winners, over/under goals, and special bets like BTTS or first goalscorer.=threshold)[0]
[22]: true_positive_rate.append(len(np.intersect1d(pred_positives,np.where(np.array(y_true)>=threshold)[0]))/len(pred_positives))
***** Tag Data *****
ID: 1
description: Calculating true positive rate per bin using nested loops over predicted
probabilities.
start line: 12
end line: 22
dependencies:
- type: Function
name: plot_calibration_curve
start line: 12
end line: 22
context description: This snippet calculates calibration curves by computing true
positive rates across different probability bins which can be useful for evaluating
model performance.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 3
advanced coding concepts: 3
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
1. **Adaptive Bin Sizing:** Implement adaptive bin sizing based on quantiles instead of fixed-size bins.
2. **Weighted True Positive Rate:** Modify `true_positive_rate` calculation by incorporating sample weights.
3. **Parallel Processing:** Optimize loop calculations using parallel processing techniques such as multiprocessing or joblib.
4. **Handling Missing Values:** Enhance robustness by handling missing values within `y_true` or `y_prob`.
5. **Dynamic Threshold Calculation:** Use more sophisticated methods like ROC curve analysis instead of mean probability threshold.
## Conversation
<|user|>"I need help with this piece code [SNIPPET]. I want more dynamic threshold calc not just mean prob"<|end|>
<|assistant|>"Sure! Instead of using mean probability as threshold we can use median absolute deviation (MAD) or even ROC curve analysis method like Youden's J statistic.
Here's how you can modify it:
python
from sklearn.metrics import roc_curve
def plot_calibration_curve(y_true,y_prob):
if isinstance(y_prob,list):
n_bins = len(y_prob)
# calculate true positive rate per bin
true_positive_rate = []
thresholds = []
# Calculate ROC curve points
fpr, tpr, roc_thresholds = roc_curve(y_true.flatten(), np.concatenate(y_prob).flatten())
j_scores = tpr - fpr
best_threshold_index = np.argmax(j_scores)
best_thresholds_per_bin = []
# Loop over each bin
for i in range(n_bins):
y_bin_probs = np.array(y_prob[i])
optimal_threshold_index_per_bin = np.argmin(np.abs(roc_thresholds - np.median(y_bin_probs)))
best_thresholds_per_bin.append(roc_thresholds[optimal_threshold_index_per_bin])
thresholds.append(best_thresholds_per_bin[-1])
pred_positives = np.where(np.array(y_bin_probs) >= thresholds[-1])[0]
true_positive_rate.append(len(np.intersect1d(pred_positives,np.where(np.array(y_true)>=thresholds[-1])[0])) / len(pred_positives))
This uses ROC curve analysis to determine optimal threshold dynamically.<|end|>
<|user|>"How add sample weight? Need weighted TPR"<|end|>
<|assistant|>"To incorporate sample weights into your TPR calculation:
python
def plot_calibration_curve_weighted(y_true,y_prob,sample_weights):
if isinstance(y_prob,list):
n_bins = len(y_prob)
# calculate weighted true positive rate per bin
weighted_true_positive_rate = []
thresholds=[]
# Calculate ROC curve points
fpr,tpr,_=roc_curve(np.concatenate([np.ones_like(p) if w > .5 else -np.ones_like(p)for p,w in zip(*zip(*[(yp,pw)for yp,pw in zip(*zip(*[(yp,pw)for yp,pw in zip(*(y_true,y_prob)]))])])]),np.concatenate([pw*np.ones_like(p)for p,pw in zip(*zip(*[(yp,pw)for yp,pw in zip(*(y_true,y_prob)]))]),sample_weight=np.concatenate(sample_weights))
j_scores=tpr-fpr
best_threshold_index=np.argmax(j_scores)
best_thresholds_per_bin=[]
total_weights=[]
# Loop over each bin
for i in range(n_bins):
y_bin_probs=np.array(y_prob[i])
sample_weight_i=np.array(sample_weights[i])
optimal_threshold_index_per_bin=np.argmin(np.abs(fpr - np.median(fpr)))
best_thresholds_per_bin.append(fpr[optimal_threshold_index_per_bin])
thresholds.append(best_thresholds_per_bin[-1])
pred_positives=np.where(np.array(y_bin_probs)>=thresholds[-1])[0]
tp_weight=sum(sample_weight_i[np.isin(pred_positives,np.where(np.array((y_true>=thresholds[-1])))[0])])
fp_weight=sum(sample_weight_i[np.isin(pred_positives,np.where(np.array((y_true
The function takes three arguments `(bin_probs,true_thres_idx_list,i)` where `bin_probs` is list containing probabilities corresponding to each bin; `true_thres_idx_list` contains indices corresponding to all instances whose predicted probabilities exceed given threshold value; finally parameter `i` represents current iteration number out-of-total number-of-bins-in-the-loop.
We then call our function via map method provided by pool object created earlier so that all iterations happen concurrently rather than sequentially resulting faster execution time compared-to-original-code!
<>: Hi there! I'm working on implementing Tarjan's algorithm for finding strongly connected components (SCCs) using PyTorch tensors instead of lists or arrays typically used with integers or floats. Here's my current implementation: python class TarjanClass: # Constructor: def __init__(self): self.NIL,self.index,self.stack,self.lowlink,self.onstack,self.indexCounter,self.sccResult=[-1]*10,[0]*N,[0]*N,[0]*N,[False]*N,0,[[]] # Finds SCCS via Tarjan Algorithm O(N+E) def SCC(self,G): # G must be represented by an Adjacency List for node in range(N): if not self.index[node]: self.Tarjan(G,node) return self.sccResult def Tarjan(self,G,node): self.index[node],self.lowlink[node],self.stack[self.indexCounter]=self.indexCounter,self.indexCounter,self.indexCounter, self.indexCounter+=1; self.onstack[node]=True; for child_ in G[node]: if not self.index[child_]: self.Tarjan(G,child_) self.lowlink[node]=min(self.lowlink[node],self.lowlink[child_]) elif self.onstack[child_]: self.lowlink[node]=min(self.lowlink[node],self.index[child_]) if self.lowlink[node]==self.index[node]: sccComponent=[] while True: node_,_=self.stack[self.indexCounter]; self.onstack[node_]=False; sccComponent+=[node_]; self.indexCounter-=1; if node_==node: break; sccComponent.sort(); self.sccResult+=[sccComponent[:]] However, when I run it with some test graphs represented as adjacency lists using PyTorch tensors instead of lists or arrays traditionally used with integers or floats only (e.g., [[tensor([item]),tensor([item]),...],[tensor([item]),tensor([item]),...],...]), I get an error related to indexing operations. Here is my traceback: Traceback (most recent call last): File "tarjan.py", line X.XX,in SCCClass.SCC(G) File "tarjan.py", line X.XX,in SCCClass.Tarjan(G,node) IndexError Traceback (most recent call last) File "tarjan.py", line X.XX,in SCCClass.SCC(G) File "tarjan.py", line X.XX,in SCCClass.Tarjan(G,node) IndexError:index out of bounds 'index' I suspect it has something do do with how PyTorch tensors handle indexing differently than traditional lists but I'm not entirely sure what adjustments need to be made here. Could someone please help me figure out what might be going wrong? Thanks! < >: python It looks like you're encountering issues due to differences between PyTorch tensor operations and traditional list indexing. Here are some suggestions to address these issues: 1. **Tensor Indexing**: Ensure you're using tensor-specific methods like `.item()` when extracting scalar values from tensors during indexing operations. 2. **Initialization**: When initializing arrays like `self.NIL`, ensure they match your graph size `N`. If `N` is dynamic based on input size, initialize them accordingly. 3. **Indexing Operations**: Replace direct list indexing with tensor operations where necessary. For example: - Use `.tolist()` or `.numpy()` if you need Python native types. - Use `.item()` when accessing single elements from tensors. Here's a revised snippet focusing on these points: python class TarjanClass: def __init__(self, N): self.NIL=self.index=self.stack=self.lowlink=self.onstack=[torch.tensor([-1])] * N + [torch.tensor(0)] self.indexCounter=self.sccResult=torch.tensor(0),[] def SCC(self,G): for node in range(len(G)): if not self.index[node].item(): self.Tarjan(G,node) return self.sccResult def Tarjan(self,G,node): self.index[node] = torch.tensor(self.indexCounter.item()) self.lowlink[node] = torch.tensor(self.indexCounter.item()) self.stack[self.indexCounter] = node self.indexCounter += 1 self.onstack[node] = True for child_tensor in G[node]: child_ = child_tensor.item() if not self.index[child_].item(): self.Tarjan(G,child_) self.lowlink[node] = min(self.lowlink[node], self.lowlink[child_]) elif self.onstack[child_]: self.lowlink[node] = min(self.lowlink[node], self.index[child_]) if self.lowlink[node].item() == self.index[node].item(): sccComponent=[] while True: node_, _= self.stack[self.indexCounter.item()], None node__=node_.item() self.onstack[node__]=False sccComponent+=[node__] self.indexCounter -= torch.tensor(1) if node__ == node: break sccComponent.sort() sccComponentTensor=torch.tensor(sccComponent[:]) self.sccResult += [sccComponentTensor.tolist()] Make sure you adapt any other parts of your code similarly where tensor operations might differ from list operations. Let me know if you need further assistance! 😊 < >: Thanks for the suggestions! I think I need to further optimize how I'm handling tensor operations within loops and conditionals. Right now, it seems like there might be unnecessary conversions between tensors and native types that could slow things down or cause errors. For instance, should I avoid using `.item()` repeatedly inside loops? And maybe there's a way to handle tensor updates more efficiently without converting back and forth? Any tips on optimizing these parts would be great! < >Certainly! Optimizing tensor operations involves minimizing conversions between tensors and native types within loops and conditionals. Here are some tips: ### Avoid Repeated Conversions - **Batch Operations**: Try performing operations directly on tensors rather than converting them individually using `.item()`. This leverages PyTorch's optimized backend. - **Conditional Logic**: Use tensor operations like logical indexing instead of iterating through elements individually. ### Efficient Tensor Updates - **In-place Operations**: Use in-place operations (e.g., `add_`, `mul_`) where possible to reduce memory overhead. - **Avoid Unnecessary Copies**: Be mindful of creating unnecessary copies during assignments or updates. ### Example Adjustments Here’s how you might adjust parts of your code: python class TarjanClass: def __init__(self, N): NIL_value_tensor=torch.full((N,), -1) tensor_zero=torch.zeros(N) self.NIL=self.NIL_value_tensor.clone() self.index=self.NIL_value_tensor.clone() self.stack=tensor_zero.clone() self.lowlink=tensor_zero.clone() onstack_mask=torch.zeros(N).bool() tensor_counter=torch.zeros((), dtype=torch.int64) def SCC(self,G): index_mask=(self.index == NIL_value_tensor).all(dim=-1) nodes_to_process=index_mask.nonzero(as_tuple=True)[0] while nodes_to_process.numel() > 0: node=nodes_to_process.pop(-dim=-len(nodes_to_process)) if index_mask.all(): continue self.Tarjan(G,node.item()) index_mask=(self.index == NIL_value_tensor).all(dim=-dim=-len(index_mask)) return [component.tolist() for component in self.sccResult] def Tarjan(self,G,node): index_node=self.get_or_update_index(node) low_link_node=index_node.clone() stack_node=self.update_stack(index_node) stack_top_index+=torch.ones(()) stack_top_onstack=True children=G[index_node] while children.numel()>0: child_=children.pop(-dim=-len(children)) child_index=self.get_or_update_index(child_) if child_index==-torch.ones(()): low_link_node=min(low_link_node,self.get_low_link(child_.clone())) elif stack_top_onstack[child_.clone().long()]: low_link_node=min(low_link_node,index_child_) if low_link_node==index_node: scc_component=[] while True: popped_stack=top(stack_top,) popped_stack_onstack=False scc_component+=[top(stack_top).long()] top_stack-=torch.ones(()) if popped_stack==index_node: break scc_component.sort() scc_result+=torch.tensor(scc_component[:]).tolist() # Helper functions inside class... def get_or_update_index(self,node:int)->int: index=node_tensor=int(torch.min(node,-len(node))) if index==NIL_value_tensor.all(): index_counter+=torch.ones(()) index_self[index]=index_counter return index_self[index] def update_stack(top:int)->int&bool:self.stack[top],on_stack[top]=True; def get_low_link(index:int)->int:self.LowLink[index]; ### Key Points - **Logical Indexing**: Use boolean masks (`onstack`) directly instead of iterating through each element. - **Avoid `.item()` Inside Loops**: Perform comparisons directly on tensors when possible. - **Use In-place Operations**: For example, use `add_(...)` instead of `+=`. These changes should help improve performance by reducing overhead from frequent conversions between types. < >: Great tips! I'll try implementing batch operations and logical indexing more extensively. Also, I'll look into using more in-place operations like `add_` instead of regular arithmetic operators within loops. Do you think restructuring some parts into helper functions could also help streamline the logic? < >: Absolutely! Structuring your code into helper functions can enhance readability and maintainability while allowing you to reuse logic efficiently. Here’s how you might approach it: ### Benefits of Helper Functions - **Modularity**: Break down complex logic into smaller parts that are easier to understand and debug. - **Reusability**: Reuse common patterns without duplicating code. ### Example Structure Consider creating helper functions for tasks like updating indices or managing stacks: python class TarjanClass: def __init__(self,N): # Initialization remains similar... def SCC(self,G): # Main logic... def Tarjan(self,G,node:int)->None:self._process_node(G,node); def _process_node(self,G,node:int)->None:self._initialize(node); def _initialize(self,node:int)->None:self._update_indices_and_stack(node); def _update_indices_and_stack(node:int)->None:self._update_lowlinks(); def _update_lowlinks()->None:self._finalize_scc(); # Define specific helper methods... def _update_indices_and_stack(node:int)->None:self._set_initial_values(node); def _set_initial_values(node:int)->None:index_self[index]=index_counter; def _update_lowlinks()->None:self._compare_and_update_lowlinks(); def _compare_and_update_lowlinks()->None:min(low_link_node,index_child_); def _finalize_scc()->None:scc_result+=torch.tensor(scc_component[:]).tolist(); ### Key Considerations - **Clear Naming**: Use descriptive names for helper functions that clearly indicate their purpose. - **Single Responsibility**: Each function should perform one specific task related to its name. Implementing these changes should make your code cleaner and potentially more efficient by reducing redundancy and improving clarity. < >: Yeah, breaking down the logic into helper functions sounds like a good idea. It should make things clearer and easier to manage. I'll start by creating functions for updating indices and managing stacks separately. Do you think this approach will also help with debugging since each part will be isolated? < > Definitely! By isolating different parts of your logic into separate helper functions, debugging becomes much easier because you can pinpoint issues more precisely within smaller sections of code. Each function handles a specific task independently: ### Debugging Benefits - **Isolation**: Errors can be traced back more easily when each function performs a distinct operation. - **Testing**: You can test individual functions independently without running the entire algorithm. ### Example Approach Consider testing each helper function separately before integrating them back into your main algorithm: python # Test updating indices separately def test_update_indices(): assert tarj_instance._update_indices_and_stack(test_node) == expected_output # Test stack management separately def test_manage_stacks(): assert tarj_instance._manage_stacks(test_conditions) == expected_behavior # Integrate tests after ensuring individual correctness... By following this approach, you’ll likely find it easier to identify where things go wrong during development or debugging sessions. < >: Awesome! Breaking down the logic should definitely help with debugging too since each part will be isolated. I'll start by creating separate functions for updating indices and managing stacks specifically designed for PyTorch tensors without unnecessary conversions inside loops or conditionals. Do you think adding unit tests for these helper functions would be beneficial? Maybe something like testing `_update_indices_and_stack` separately before integrating everything back together? < > Adding unit tests is an excellent idea! They provide confidence that each part works correctly before integration: ### Benefits of Unit Tests - **Verification**: Ensure each function behaves as expected under various conditions. - **Regression Prevention**: Quickly identify issues introduced during future changes. ### Example Unit Test Structure You can create simple tests using assertions or leverage frameworks like `unittest` or `pytest`: python import unittest class TestTarjanFunctions(unittest.TestCase): def setUp(self): # Initialize your class instance here... def test_update_indices_and_stack(self): # Set up test conditions... expected_output = ... result = tarj_instance._update_indices_and_stack(test_input) self.assertEqual(result, expected_output) if __name__ == '__main__': unittest.main() By testing `_update_indices_and_stack` separately first, you ensure its correctness before moving onto more complex integrations—streamlining both development and debugging processes! < >: Perfect! I'll set up some unit tests using unittest first thing tomorrow morning before diving deeper into refactoring everything else around those new helper functions we discussed earlier today :D Thanks again! < > Certainly! Setting up unit tests first will give you a solid foundation as you refactor your code around those new helper functions—it's a great way forward! If any issues arise during testing or refactoring feel free reach out again—I'm here whenever you need assistance 😊< >: Hey there, I've been trying out this implementation called FastGradientMethodAttack which extends ProjectedGradientDescentAttackBaseMixin class meant for adversarial attacks using TensorFlow/Keras models based on gradient ascent/descent techniques inspired by Carlini et al.'s paper 'Towards Evaluating the Robustness Of Neural Networks'. The idea here is pretty advanced—it uses gradients computed via TensorFlow/Keras backend ops combined with custom loss functions depending upon whether targeted attack mode is enabled or not ('carlini' loss otherwise). But here's where things got tricky—I've encountered an issue when running my model against adversarial examples generated through this class implementation under targeted attack mode enabled ('target' loss). The problem seems elusive but results consistently point towards incorrect labels being predicted after applying adversarial perturbations even though they should ideally still classify correctly under small perturbations! Below is my buggy version followed by traceback information—any ideas what might be going wrong? Buggy Code Snippet: python import tensorflow.keras.backend as K class FastGradientMethodAttack(FastGradientMethodAttackBaseMixin, ProjectedGradientDescentAttackBaseMixin, AdversarialAttackMixin, LabelMixin, ClassGradientsMixin, RandomizableMixin, TFModelMixin, GradientMixin, CallClassificationHeadMixin, UntargetableMixin): @tf.function(experimental_relax_shapes=True) @call_decorator_wrapper(in_batch_ndim=None) @label_decorator_wrapper(default_label_handling='first') @randomizable_decorator_wrapper(random_state=None) @call_gradient_evaluation_decorator_wrapper(call_kwarg_name='eval_gradient', grad_call_kwargs=None, max_batch_size=None, max_eval_batch_size=None, requires_labels=True, default_label_handling='first', label_arg_name='label', label_kwarg_name='label') @call_classification_head_decorator_wrapper(call_kwarg_name='classification_head_call', classification_head_call_kwargs=None, requires_labels=False, label_arg_name='label', label_kwarg_name='label') @untargetable_decorator_wrapper(target_class_arg_name='target_class', target_class_kwarg_name='target_class', target_label_arg_name='target_label', target_label_kwarg_name='target_label', target_logit_arg_name='target_logit', target_logit_kwarg_name='target_logit') @tf.function(experimental_relax_shapes=True) def generate_graph(input_, label=None, epsilons=tf.constant([[EPSILON]], dtype=MIXED_PRECISION_DTYPE), target_class=None, clip_min=None, clip_max=None, early_stop_loss_fn=tf.reduce_max, early_stop_iter=None): if USE_MIXED_PRECISION_ENV_VAR != 'ON': epsilons_dtypes_dict['epsilons'] *= tf.cast(K.cast_to_floatx(MIXED_PRECISION_DTYPE), K.floatx()) epsilons_dtypes_dict['epsilons'] /= tf.cast(K.cast_to_floatx(MIXED_PRECISION_DTYPE), K.floatx()) input_dtype_casted_epsilons_dtypes_dict['epsilons'] *= K.cast_to_floatx(input_dtype_casted_epsilons_dtypes_dict['input']) input_dtype_casted_epsilons_dtypes_dict['epsilons'] /= K.cast_to_floatx(input_dtype_casted_epsilons_dtypes_dict['input']) batch_size_epsilons_shape_proto_list, batch_size_epsilons_shape_proto_list, batch_size_input_shape_proto_list, batch_size_target_class_shape_proto_list, batch_size_label_shape_proto_list, batch_size_target_logit_shape_proto_list = get_batch_sizes_for_concatenation( concatenation_axes=[[], [DIMENSION_AXIS]], values=[ input_dtype_casted_epsilons_dtypes_dict['epsilons'], input_dtype_casted_epsilons_dtypes_dict['epsilons'], input_dtype_casted_epsilons_dtypes_dict['input'], target_class_one_hot_expanded_one_dim_dtype_casted_dtypedict[ 'target_class_one_hot_expanded_one_dim'], label_one_hot_expanded_one_dim_dtype_casted_dtypedict[ 'label_one_hot_expanded_one_dim'], target_logit_expanded_one_dim_dtype_casted_dtypedict[ 'target_logit_expanded_one_dim']], value_names=['epsilons', 'epsilons', 'input', 'target_class_one_hot_expanded_one_dim', 'label_one_hot_expanded_one_dim', 'target_logit_expanded_one_dim']) ... if target_class is None and label is None and target_logit is None and USE_TARGET_CLASS_ENV_VAR != 'ON': raise ValueError("Label/target logit/target class must be provided" "when generating targeted attacks") if early_stop_loss_fn.__name__ != 'reduce_mean': early_stop_loss_fn_wrapped_kwargsdict['axis'] =list(range(len(K.int_shape(input_)[:-len(BATCH_NDIMS)]))) ... return x_adv_best.numpy() Traceback/Error Info/Error Message/Error Description/Error Type/Error Traceback/Error Log/Error Stack Trace/Error Code/Error Output/Error Handling/Suggestion/Error Correction/Fix/Solution/Patch/Cause/Reason/Symptom/Diagnosis/Troubleshooting Steps/Hints/Tips/Warnings/Reminders/Limitations/Constraints/Caveats/Observations/Notes/Comments/Future Work/Suggested Improvements/Better Practices/Efficient Solutions/Optimizations/Missing Features/Additions/Enhancements/Upgrades/New Features/Possible Extensions/Potential Risks/Risks Associated With Error/Benefits Of Fixing Error/Motivation Behind Fixing Error/Benefits Of Not Fixing Error/Risks Associated With Not Fixing Error/Ethical Implications Of Fixing Error/Ethical Implications Of Not Fixing Error/Stakeholders Affected By Fixing Error/Stakeholders Affected By Not Fixing Error/Stakeholder Perspectives On Fixing Error/Stakeholder Perspectives On Not Fixing Error/Stakeholder Priorities Regarding Fixing Error/Stakeholder Priorities Regarding Not Fixing Error/Stakeholder Concerns About Fixing Error/Stakeholder Concerns About Not Fixing Error/Stakeholder Interests In Relation To Fixing Error/Stakeholder Interests In Relation To Not Fixing Error/Stakeholder Values Related To Fixing Error/Stakeholder Values Related To Not Fixing Error/Stakeholder Expectations Regarding Outcome Of Fixing Error/Stakeholder Expectations Regarding Outcome Of Not Fixing Error/Stakeholder Influence On Decision To Fix Or Not To Fix The Issue/Impact On Stakeholders If Issue Is Fixed Impact On Stakeholders If Issue Is Not Fixed Impact On Stakeholders Regardless Of Whether Issue Is Fixed Or Not Impact On Other Stakeholders If Issue Is Fixed Impact On Other Stakeholders If Issue Is Not Fixed Impact On Other Stakeholders Regardless Of Whether Issue Is Fixed Or Not Potential Conflicts Between Stakeholders Due To Decision To Or Decision Against To Address The Issue Possible Solutions To Address Conflicts Between Stakeholders Due To Decision Made About Whether Or How To Address The Issue Long Term Consequences For All Relevant Parties Depending Upon Whether Or How The Problem At Hand Is Addressed Short Term Consequences For All Relevant Parties Depending Upon Whether Or How The Problem At Hand Is Addressed Short Term Consequences For Specific Parties Depending Upon Whether Or How The Problem At Hand Is Addressed Long Term Consequences For Specific Parties Depending Upon Whether Or How The Problem At Hand Is Addressed Pros And Cons Of Various Options Available For Resolving Conflict Amongst Relevant Parties And Deciding Whether And How To Resolve Problem At Hand Recommendations For Best Course Of Action Given Circumstances Surrounding Problem At Hand Potential Obstacles That Might Prevent Implementation Of Recommended Solution Ways Around Potential Obstacles That Might Prevent Implementation Of Recommended Solution Additional Resources That Could Help In Implementaion Of Recommended Solution Additional Resources That Could Help In Understanding Nature And Implications Of Problem At Hand Additional Resources That Could Help In Understanding Nature And Implications Of Proposed Solution Additional Resources That Could Help In Understanding Nature And Implications Of Potential Obstacles That Might Prevent Implementation Of Proposed Solution Additional Resources That Could Help In Understanding Nature And Implications OF Potential Ways Around Obstacles That Might Prevent Implementation OF Proposed Solution Additional Resources That Could Help In Understanding Nature And Implications OF Long Term Consequences FOR All Relevant Parties Depending UPON WHETHER OR HOW THE PROBLEM AT HAND IS ADDRESSED ADDITIONAL RESOURCES THAT COULD HELP IN UNDERSTANDING NATURE AND IMPLICATIONS OF LONG TERM CONSEQUENCES FOR SPECIFIC PARTIES DEPENDING UPON WHETHER OR HOW THE PROBLEM AT HAND IS ADDRESSED ADDITIONAL RESOURCES THAT COULD HELP IN UNDERSTANDING NATURE AND IMPLICATIONS OF SHORT TERM CONSEQUENCES FOR ALL RELEVANT PARTIES DEPENDING UPON WHETHER OR HOW THE PROBLEM AT HAND IS ADDRESSED ADDITIONAL RESOURCES THAT COULD HELP IN UNDERSTANDING NATURE AND IMPLICATIONS OF SHORT TERM CONSEQUENCES FOR SPECIFIC PARTIES DEPENDING UPON
