Athletic Club U21: Champions of Basque Youth League - Squad, Stats & Achievements
Overview of Athletic Club U21
Athletic Club U21, a prominent football team based in the Basque Country, Spain, competes in the Segunda División B. Known for its passionate fanbase and rich history, the team plays under a 4-3-3 formation. Managed by a dedicated coaching staff, Athletic Club U21 has become a formidable force in youth football.
Team History and Achievements
Athletic Club U21 boasts a storied past with numerous titles and accolades. The team has consistently finished in top positions within their league, showcasing their competitive spirit. Notable seasons include their championship win in the 2019-2020 season and several runner-up finishes that highlight their consistent performance.
Current Squad and Key Players
The current squad features talented young players who are pivotal to the team’s success. Key players include:
Forward: Iker Muniain – Renowned for his goal-scoring ability and playmaking skills.
Midfielder: Unai Núñez – Known for his tactical intelligence and defensive prowess.
Defender: Íñigo Martínez – A stalwart defender with exceptional leadership qualities.
Team Playing Style and Tactics
Athletic Club U21 employs a dynamic 4-3-3 formation, emphasizing fluid attacking play and solid defense. Their strategy focuses on quick transitions and exploiting spaces in the opponent’s half. Strengths include high pressing and ball control, while weaknesses may arise from occasional lapses in concentration during counterattacks.
Interesting Facts and Unique Traits
The team is affectionately known as “Los Leones” (The Lions), reflecting their fierce playing style. Athletic Club U21 has a passionate fanbase that supports them through thick and thin. Rivalries with local teams add an extra layer of excitement to their matches, while traditions like pre-match rituals enhance the matchday experience.
Lists & Rankings of Players, Stats, or Performance Metrics
Key performance metrics include:
✅ Top Scorer: Iker Muniain – 15 goals this season
❌ Lowest Performer: Midfielder X – Struggling with form
🎰 Most Assists: Unai Núñez – 10 assists this season
💡 Player to Watch: Íñigo Martínez – Rising star with potential for more recognition
Comparisons with Other Teams in the League or Division
Athletic Club U21 is often compared to other top youth teams like Real Sociedad B and SD Eibar B. While Athletic Club U21 excels in offensive creativity, Real Sociedad B is known for its disciplined defense. SD Eibar B, on the other hand, is recognized for its fast-paced gameplay.
Case Studies or Notable Matches
A breakthrough game for Athletic Club U21 was their victory against CD Mirandés B in the 2021 season opener, which set the tone for their successful campaign. Another key victory was against CA Osasuna B, where strategic substitutions turned the game around.
Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds
Statistic
Data
Total Wins This Season
12
Total Draws This Season
5
Total Losses This Season
8
Average Goals Scored per Match
1.8</td
<>: Hi there! I’m working on implementing an attention mechanism using PyTorch’s `nn.MultiheadAttention` class. Here’s a snippet of my code:
python
class Attention(nn.Module):
def __init__(self):
super(Attention,self).__init__()
self.attention = nn.MultiheadAttention(embed_dim=512, num_heads=8)
def forward(self,x):
”’ x: [seq_len,batch_size,input_dim] ”’
value = x.permute(1,0,2)
out,self.attn = self.attention(value,value,value)
out = out.permute(1,0,2)
I’m trying to understand if this setup correctly applies multi-head attention to my input tensor `x`. Could you help clarify how I should be preparing my input data before feeding it into this model? Also, any tips on interpreting the output would be greatly appreciated! Thanks!
<>: python
Sure! Your setup looks mostly correct for using `nn.MultiheadAttention`. Here are some points to consider:
1. **Input Preparation**:
– Ensure your input tensor `x` is shaped as `[seq_len, batch_size, input_dim]`, which you’ve done by permuting it.
– The `embed_dim` parameter should match `input_dim`, so your choice of `512` seems appropriate.
2. **Output Interpretation**:
– The output tensor `out` will have the shape `[seq_len, batch_size, embed_dim]`.
– The attention weights (`self.attn`) have dimensions `[num_heads * batch_size * seq_len]`.
3. **Additional Tips**:
– Consider adding dropout or layer normalization if needed for better training stability.
– You might want to experiment with different numbers of heads (`num_heads`) based on your specific task.
Feel free to ask if you need more details! 😊
<>: Got it! Now I’m thinking about adding positional encoding to capture sequence order since attention mechanisms don’t inherently account for it. How can I integrate positional encoding into this setup? Should I modify the input tensor before passing it to the attention layer?
<>: Absolutely! Adding positional encoding is a great way to incorporate sequence order information into your model.
Here’s how you can integrate it:
1. **Positional Encoding**:
– You can create a positional encoding matrix with dimensions `[seq_len, embed_dim]`.
– Use sine and cosine functions of different frequencies as described in the “Attention Is All You Need” paper.
2. **Integration**:
– Add this positional encoding matrix to your input tensor `x` before passing it to the attention layer.
– Ensure both have compatible shapes by broadcasting.
Here’s a simple example:
python
import torch
import math
class PositionalEncoding(nn.Module):
def __init__(self, embed_dim, max_seq_len):
super(PositionalEncoding,self).__init__()
pe = torch.zeros(max_seq_len, embed_dim)
position = torch.arange(0., max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0., embed_dim, 2) * -(math.log(10000.0) / embed_dim))
3. **Modify Forward Method**:
– Use `x_with_pos_enc` instead of `x` when calling your attention layer.
This should help your model capture sequence order effectively! 😊
<>: Great advice! Now I’m considering adding layer normalization after applying positional encoding but before feeding it into the attention layer. How can I integrate layer normalization into this setup? Would it look something like this?
def forward(self,x):
x_with_pos_enc = self.pos_enc(x)
norm_x = self.layer_norm(x_with_pos_enc.permute(1,0,2))
value = norm_x.permute(1,0,2)
out,self.attn = self.attention(value,value,value)
out = out.permute(1 ,0 ,2 )
Does this look right? Any tweaks you would suggest?
<>: Your integration looks solid! Applying layer normalization after positional encoding is a common practice to stabilize training.
Here are a few points to ensure everything works smoothly:
1. **Layer Normalization**:
– You’re correctly applying it after adding positional encoding.
– Make sure `normalized_shape` matches `embed_dim`.
2. **Permutations**:
– Your permutations are correct; they align with what `nn.MultiheadAttention` expects.
3. **Tweaks**:
– Consider adding dropout after layer normalization if needed for regularization.
– Ensure that any additional operations (like dropout) are applied consistently across training runs.
Overall, your setup looks well-structured! Keep experimenting with these components based on your specific task requirements. 😊
<>: Awesome! Now I’m thinking about adding dropout after layer normalization to help with regularization and prevent overfitting. How should I integrate dropout into this setup? Would it look something like this?
Does this look right? Any other suggestions?
<>: python
Your integration of dropout looks good! Adding dropout after layer normalization is effective for regularization.
2. **Dropout Rate**:
– A rate of 0.1 is commonly used; adjust based on your dataset size and complexity.
3. **Training Mode**:
– Ensure that dropout behaves differently during training vs evaluation by using `.train()` and `.eval()` modes appropriately.
4. **Consistency**:
– Make sure all parts of your model use consistent dropout settings during training.
Your setup now includes essential components like positional encoding and regularization techniques—great job! Keep refining based on experimental results! 😊
<>: Perfect! That clears things up nicely. I’ll start experimenting with these changes and see how it impacts my model’s performance. Thanks for all the help!
<>: python
You’re welcome! It sounds like you’re on the right track with these enhancements—positioning yourself well for improved model performance through experimentation.
If you encounter any issues or have more questions as you test things out, feel free to reach out!
Happy coding and best of luck with your experiments! 🚀😊
<>: Hey there,
I’m having trouble understanding why my code isn’t working as expected when dealing with complex nested structures using JSONPath expressions within dictionaries containing lists of dictionaries or lists themselves.
Here’s what my function looks like:
python
def _get_values_in_path(
root_path: str,
paths_json_dict: Dict[str,str],
json_dict: Union[Dict[Any,int], List[int]]):
”’Get all values along path defined by root_path.
Args:
root_path: path starting from root object (dict or list),
consisting of keys separated by ‘/’.
E.g.: ‘root/a/b/0/c’
paths_json_dict : dictionary containing stored paths,
accessible via string representation.
E.g.: {‘root/a/b/0/c’: [”…]}
json_dict : actual JSON object (dict or list),
containing data corresponding to paths_json_dict.
Returns:
If repetitions are found along path,
return dict {repetition id : value found at path}
Otherwise return just value found at path.
”’
# Split path into keys leading from root obj down to target obj.
keys_to_traverse_list = root_path.split(‘/’)
# Try traversing given path.
# Start from root obj (dict or list).
temp_dict_or_list_for_traversing = json_dict
try:
# Save ids of any repetitions traversed.
repetition_ids_in_traversed_path = []
# Traverse down given path.
for k in keys_to_traverse_list[:-1]:
# Check whether following node is a ReDictObject.
if type(temp_dict_or_list_for_traversing) == ReDictObject:
# If so then add repeated ID(s) to our repetition_ids_in_traversed_path var.
repetition_ids_in_traversed_path.append(
temp_dict_or_list_for_traversing.get_repeated_id())
# Move down given key.
temp_dict_or_list_for_traversing=temp_dict_or_list_for_traversing[k]
# Check whether we are going beyond last element in given list.
if type(temp_dict_or_list_for_traversing) == list:
k_last_element=int(keys_to_traverse_list[-1])
if k_last_element >= len(temp_dict_or_list_for_traversing):
raise KeyError(‘Given path goes beyond last element:’,root_path)
else:
temp_dict_or_list_for_traversing=temp_dict_or_list_for_traversing[k_last_element]
else:
temp_dict_or_list_for_traversing=temp_dict_or_list_for_traversing[keys_to_traverse_list[-1]]
# Return results depending on whether we had traversed across repeated IDs or not.
if len(repetition_ids_in_traversed_path)==0:
return temp_dict_or_list_for_traversing
else:
target_value=[]
dict_of_repetitions={key:value
for key,value
in zip(repetition_ids_in_traversed_path,
temp_dict_or_list_for_traversing)}
return dict_of_repetitions
except (TypeError,json.decoder.JSONDecodeError,/):
raise KeyError(‘Given path does not exist:’,root_path)
And here’s what happens when I run my test cases:
plaintext
Traceback (most recent call last):
File “test_script.py”, line XX-in test_function_name,
AssertionError Traceback (most recent call last): File “/path/to/test_script.py”, line XX-in test_function_name assert result == expected_output File “/path/to/script.py”, line YY-in _get_values_in_path raise KeyError(‘Given path does not exist:’, root_path) KeyError:’Given path does not exist:’ ‘root/a/b/0/c’
I suspect there’s something wrong when handling lists within nested structures because sometimes valid paths aren’t recognized properly due to type mismatches or incorrect indexing logic when accessing elements within lists inside dictionaries.
Could someone point me towards what might be going wrong here? Why am I getting these errors even though my paths seem correct?
Thanks!
<>: It looks like there might be an issue with how you’re handling lists within nested structures due to potential type mismatches or incorrect indexing logic.
Here are a few things you could check:
1. **Type Checking**: Ensure that each key traversal checks whether the current object is indeed a dictionary before attempting dictionary-specific operations like accessing keys directly (`temp_dict_or_list_for_traversing[k]`). If it’s not guaranteed that every intermediate step will be a dictionary (or list), you might encounter unexpected types leading to errors.
2. **Index Conversion**: When accessing list elements using indices from strings (e.g., `’key’ -> int(key)`), ensure that conversion from string index (`keys_to_traverse_list[-1]`) happens only when necessary (i.e., when dealing with lists).
3. **ReDictObject Handling**: Verify that any custom objects like `ReDictObject` have methods properly implemented (`get_repeated_id()`) and handle cases where they might not behave as expected within your structure traversal logic.
4. **Error Handling**: The exception handling block `(TypeError,json.decoder.JSONDecodeError,/)` might be catching exceptions incorrectly due to syntax issues (`/`). Consider specifying exceptions separately without `/`.
5 debugging tip is adding print statements at each step of traversal inside `_get_values_in_path` function could help pinpoint exactly where things go wrong by showing intermediate states of variables.
By addressing these areas systematically while testing various edge cases involving nested dictionaries/lists combinations should lead towards resolving these issues effectively!
Let me know if further clarification helps or if there’s anything else specific you’d like assistance with!
<>: Alright, I think I need to refine how I’m handling type checking during traversal and ensure proper indexing logic when dealing with lists inside dictionaries. Also noticed that my exception handling syntax might be off due to `/`.
Here’s what I’m thinking:
– Add explicit checks before accessing elements as dictionaries or lists.
– Convert string indices only when accessing list elements.
– Fix exception handling syntax by specifying exceptions separately without `/`.
Does this approach make sense? Any other suggestions?
<>: Your approach sounds solid! Here’s how you can implement those improvements:
– **Explicit Type Checks**: Before accessing an element as part of a dictionary or list during traversal (`temp_dict_or_list_for_traversing[k]`), explicitly check its type using `isinstance()`. This will prevent unexpected type errors.
– **Index Conversion**: Convert string indices only when you confirm that an element is indeed part of a list (`if isinstance(temp_dict_or_list_for_traversing[k], list)`).
– **Exception Handling Syntax**: Specify exceptions separately without using `/`. For example:
python
except TypeError as e:
raise KeyError(‘Type error encountered:’, e) from e
except json.decoder.JSONDecodeError as e:
raise KeyError(‘JSON decode error encountered:’, e) from e
These changes should help make your code more robust against type mismatches and improve error clarity during debugging.
Feel free to reach out if you need further assistance! 😊
@import ‘~styles/variables’;
