Overview of PSV Eindhoven U19
The PSV Eindhoven U19 team, based in the Netherlands, competes in the prestigious Dutch Under-19 league. Known for its strong youth development program, the team is managed by a seasoned coach who focuses on nurturing young talent. The squad typically plays in a flexible formation, adapting to opponents’ strengths and weaknesses.
Team History and Achievements
PSV Eindhoven U19 has a rich history of success in youth football. The team has won multiple league titles and cups, showcasing their dominance in Dutch youth football. Notable seasons include their championship win in 2020 and consistent top-three finishes over the past five years.
Current Squad and Key Players
The current squad boasts several standout players, including top performers like striker Johan de Jong and midfielder Lars Veldhuizen. Their roles are crucial in both attack and defense, with impressive statistics that highlight their contributions to the team’s success.
Team Playing Style and Tactics
The team employs a dynamic 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their attacking prowess and tactical flexibility, while their weaknesses include occasional defensive lapses against counterattacks.
Interesting Facts and Unique Traits
PSV Eindhoven U19 is affectionately known as “De Feilen” (The Eagles) among fans. The team has a passionate fanbase that supports them through thick and thin. They have historic rivalries with Ajax U19 and Feyenoord U19, adding an extra layer of excitement to their matches.
Lists & Rankings of Players, Stats, or Performance Metrics
- Johan de Jong: Top scorer ✅
- Lars Veldhuizen: Assists leader 🎰
- Pascal de Vries: Defensive solidity 💡
Comparisons with Other Teams in the League or Division
Compared to rivals Ajax U19 and Feyenoord U19, PSV Eindhoven U19 excels in youth development and tactical adaptability. While Ajax is known for its technical skills, PSV’s balanced approach often gives them an edge.
Case Studies or Notable Matches
A breakthrough game for PSV Eindhoven U19 was their 3-1 victory over Ajax U19 last season, which marked a turning point in their campaign. This match highlighted their strategic depth and ability to perform under pressure.
| Statistic | Last Season | This Season (to date) |
|---|---|---|
| Total Goals Scored | 45 | 22 |
| Total Goals Conceded | 18 | 10 |
| Last Five Matches Form (W-D-L) | N/A | 4-0-1 |
| Odds for Next Match Win/Loss/Draw | N/A | 1.8/3.5/3.6 |
Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks </h2
To bet effectively on PSV Eindhoven U19, consider their recent form against key rivals. Analyze individual player performances and how they fit into the team’s tactics. Keep an eye on upcoming fixtures against weaker teams for higher winning odds.
Frequently Asked Questions (FAQs)
What are PSV Eindhoven U19’s recent form statistics?
Their recent form shows four wins out of five matches this season, indicating strong performance levels.
Who are the key players to watch?
Johan de Jong and Lars Veldhuizen are pivotal players whose performances significantly impact match outcomes.
Casual betting tips for upcoming matches?
Analyzing head-to-head records against upcoming opponents can provide insights into potential outcomes.
“PSV Eindhoven U19 consistently demonstrates excellence in youth development,” says football analyst Mark van der Heide.
Moving Forward: Pros & Cons of Current Form or Performance ✅❌ Lists </h2
- Promising Pros:
- Achieving high-scoring games ✅
- Maintaining solid defensive records ✅
- Potential cons include:
- Sporadic defensive errors ❌
- Vulnerability during high-pressure situations ❌
</u
<>: Hi there! I’m working on a project involving image processing using PyTorch tensors, specifically trying to convert these tensors into PIL images for visualization purposes.
Here’s my current code:
python
def tensor_to_image(tensor: torch.Tensor) -> PIL.Image:
“””
Hint: use torchvision.transforms to convert it back
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W)
You can display an image using following snippet:
plt.imshow(img.permute(1,2,0))
“””
# put your code here
# image = tensor[0].cpu().detach()
# image = transforms.ToPILImage()(image)
# return image
However, when I run it with certain tensors that have more than one channel at dimension `D`, I encounter issues. Here’s the error traceback I get:
Traceback (most recent call last):
File “main.py”, line 15, in
img = tensor_to_image(sample_tensor)
File “main.py”, line 7, in tensor_to_image
image = transforms.ToPILImage()(image)
TypeError: __call__() missing 1 required positional argument: ‘img’
I’m not sure why this error is happening since I thought `ToPILImage` would handle it properly after selecting the first channel with `tensor[0]`. Could you help me figure out what’s going wrong here? Any guidance would be greatly appreciated!
<>: python
It looks like the issue arises because `transforms.ToPILImage()` expects a single channel or three-channel input when converting from a tensor to an image. If your tensor has more than one channel at dimension `D`, you’ll need to handle each channel separately or select specific channels.
Here’s how you can modify your function:
python
import torch
from torchvision import transforms
def tensor_to_image(tensor: torch.Tensor) -> PIL.Image:
“””
Convert a PyTorch tensor to a PIL Image.
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W)
“””
# Move the tensor from GPU to CPU if necessary
tensor = tensor.cpu()
# Select the first channel if C > D; otherwise handle differently
if tensor.shape[0] > tensor.shape[1]:
# Assuming you want to visualize each slice along D separately
images = [transforms.ToPILImage()(tensor[c].detach()) for c in range(tensor.shape[0])]
return images[0] # Return the first image as an example
elif tensor.shape[0] == 3:
# If it’s already RGB-like format
image = transforms.ToPILImage()(tensor.squeeze(0).detach())
return image
else:
raise ValueError(“Unsupported number of channels”)
# Example usage:
# img = tensor_to_image(sample_tensor)
This code checks if there are more channels than slices along `D` and processes accordingly. Adjust based on how you want to visualize your data! 😊
<>: Thanks for the help! Now, I’m thinking about handling cases where there might be multiple channels at dimension D more flexibly. Instead of just taking the first channel or assuming RGB-like format, maybe we could allow selecting specific channels or even averaging them? How could we modify the function to accommodate this?
<>: Sure! You can modify the function to allow selecting specific channels or averaging them by adding parameters for these options:
python
import torch
from torchvision import transforms
def tensor_to_image(tensor: torch.Tensor, select_channels=None, average_channels=False) -> PIL.Image:
“””
Convert a PyTorch tensor to a PIL Image.
Parameters:
– select_channels: List of indices specifying which channels to use.
– average_channels: Boolean indicating whether to average all channels.
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W) if D > C; otherwise single-channel image.
Returns:
A list of images if multiple slices are processed.
Raises:
ValueError if unsupported configuration is provided.
“””
# Move the tensor from GPU to CPU if necessary
tensor = tensor.cpu()
if select_channels is not None:
# Select specified channels along C dimension
selected_tensor = torch.stack([tensor[c] for c in select_channels], dim=0)
elif average_channels:
# Average across all channels along C dimension
selected_tensor = torch.mean(tensor.float(), dim=0).unsqueeze(0).byte()
else:
selected_tensor = tensor
# Process each slice along D dimension separately
images = [transforms.ToPILImage()(selected_tensor[d].detach()) for d in range(selected_tensor.shape[1])]
return images
# Example usage:
# img_list = tensor_to_image(sample_tensor, select_channels=[0], average_channels=False)
This version allows you to specify which channels you want via `select_channels` or average them using `average_channels`. Adjust these parameters based on your needs! 😊<>: Hi there! I’m working with this Python function called `get_cooking_time`, which calculates cooking times based on different methods like baking or steaming using some complex mathematical formulas involving logarithms.
Here’s the snippet:
python
def get_cooking_time(self):
“””
Calculation of cooking time requires self.weight attribute set.
Literature research led us to several formulas that calculate cooking time based on weight.
Our solution factors in weight but also several other attributes too that we believe will help
our customers determine cooking time more accurately.
“””
# cooking_time_minutes= ((self.weight * .36) / self.crockpot_volume) + .6
# Original formula came from NESCO crockpot brand.
if self.cooking_method == “baking”:
return ((self.weight * .36) / self.crockpot_volume) + .6
elif self.cooking_method == “slow”:
return ((self.weight * .36) / self.crockpot_volume) + .45
elif self.cooking_method == “rapid”:
return ((self.weight * .36) / self.crockpot_volume) + .3
else:
raise ValueError(“Set proper cooking_method attribute”)
def calculate_baking_time(self):
return ((self.weight * .35) / self.crockpot_volume) + .55
def calculate_slow_cooking_time(self):
return ((self.weight * .4 ) / self.crockpot_volume ) + .45
def calculate_rapid_cooking_time(self):
return ((self.weight * .5 ) / self.crockpot_volume ) + .4
I’m feeling a bit confused about why there are separate functions defined within this method but not being used anywhere else outside it. Could you help me understand how these nested functions contribute to calculating cooking time? Should they be integrated differently? Thanks!
<>: python
It looks like those nested functions (`calculate_baking_time`, `calculate_slow_cooking_time`, `calculate_rapid_cooking_time`) aren’t being utilized within your main method (`get_cooking_time`). They’re defined but never called or returned.
To integrate them effectively:
1. **Remove Redundancy**: If they’re meant to replace parts of your existing logic within `get_cooking_time`, consider refactoring by calling these functions instead.
2. **Refactor Logic**: You could replace sections of your current conditional logic with calls to these functions:
python
def get_cooking_time(self):
if self.cooking_method == “baking”:
return self.calculate_baking_time()
elif self.cooking_method == “slow”:
return self.calculate_slow_cooking_time()
elif self.cooking_method == “rapid”:
return self.calculate_rapid_cooking_time()
else:
raise ValueError(“Set proper cooking_method attribute”)
def calculate_baking_time(self):
return ((self.weight * .35) / self.crockpot_volume) + .55
def calculate_slow_cooking_time(self):
return ((self.weight * .4 ) / self.crockpot_volume ) + .45
def calculate_rapid_cooking_time(self):
return ((self.weight * .5 ) / self.crockpot_volume ) + .4
This approach makes your code cleaner by separating concerns into distinct methods while maintaining functionality. 😊
<>: Got it! So by refactoring like this, I can keep things tidy without redundant calculations. Do you think there’s any other way to simplify this further? Maybe something about naming conventions or handling exceptions?
<>: Sure! Here are some additional suggestions:
1. **Naming Conventions**: Use descriptive names that clearly indicate what each function does. For instance, prefix method names with `calculate_` consistently.
python
def calculate_baking_time(self):
…
def calculate_slow_coking_duration(self):
…
However, since they all follow similar patterns now due to refactoring (`calculate__time`), ensure consistency.
2. **Exception Handling**: Consider custom exceptions for better clarity when raising errors.
python
class InvalidCookingMethodError(Exception):
pass
def get_cooking_time(self):
try:
…
except InvalidCookingMethodError as e:
print(f”Error: {e}”)
raise
3. **Docstrings**: Add docstrings explaining what each method does for better documentation.
By implementing these changes, your code will be more readable and maintainable! 😊<>: Hi there,
I’m working on implementing LDA training using Gibbs sampling as part of my machine learning project involving topic modeling on text data extracted from CSV files related to medical topics like diabetes symptoms detection.
Here’s my current implementation:
python
class Lda(
methods.LavenderMixin,
base.LavenderBaseModel[‘vectorizer’, ‘topic_model’],
metaclass=abc.ABCMeta,
):
def __init__(
self,
vectorizer:[None,’vectorizer’]=None,
topic_model:[None,’topic_model’]=None,
n_components:int=100,
max_iter:int=5,
tol:floating=0.,
evaluations_every:int=10,
hyperparameters:{str,(int,float)}={‘alpha’:’symmetric’,’eta’:’symmetric’},
random_state=None,
priors:list=[‘uniform’],
n_jobs=-1,
metrics:str=’perplexity’,
init_passes:int=5,
sampling_strategy:{str,int,list,int}=’term_frequency’,
dtype=np.float32,
):
self._set(
vectorizer=self.vectorizer(),
topic_model=self.topic_model(
self.vectorizer(),
n_components=n_components,
max_iter=max_iter,
tol=tol,
evaluations_every=evaluations_every,
hyperparameters=hyperparameters,
random_state=random_state,
priors=priors,
n_jobs=n_jobs,
metrics=metrics,
init_passes=init_passes,
sampling_strategy=sampling_strategy),
name=’LDA’,
path=path.join(path.abspath(path.curdir),time.strftime(‘%Y-%m-%d %H-%M-%S’)),
file=’lda’,
random_state=random_state,
dtype=dtype
)
def train(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
topics:[int],
passes:int=5,#Number document passes per iteration during training
chunks:int=None,#Number documents per pass during training
}
):
self.topic_model.partial_fit(tokenized=self.tokenized(samples=self.samples(texts=texts,csv=csv,pandas=pandas))))
def transform(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
return topics
def predict(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
predicted=[max(topics[x])for x,_in enumerate(topics)]
print(predicted)
def score(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x,y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
predicted=[max(topics[x])for x,_in enumerate(topics)]
score=model_selection.cross_val_score(Lda(n_components=n_topics),tokenized,score,cv=cv,n_jobs=-1,)
print(score.mean())
def save(self,path:str,name:str=’LDA’):
pickle.dump([
self.vectorizer(),
self.topic_model(),
pd.get_dummies(pd.Series(predicted)),
pd.get_dummies(pd.Series(y))),
open(path+name+’.pickle’,’-wb’))
json.dump([
glossary,self.gensim.corpora.Dictionary.to_json(gensim.corpora.Dictionary(train_token)),
dtm.transpose().tolist()],open(path+name+’_dtm.json’,’-w’))
gensim.corpora.MmCorpus.serialize(path+name+’_corpus.mm’,dtm.transpose())
lda.save(path+name+’.model’)
vectorizers_path=path.join(path.dirname(__file__),’..’)
json.dump({
wordcloud_path:path.join(vectorizers_path,name+’_wordcloud.json’),
wordcloud_words_path:path.join(vectorizers_path,name+’_wordcloud_words.json’),
frequencies_path:path.join(vectorizers_path,name+’_frequencies.json’),
frequency_stems_path:path.join(vectorizers_path,name+’_frequency_stems.json’),
frequency_grams_path:path.join(vectorizers_path,name+’_frequency_grams.json’),
frequency_tokens_path:path.join(vectorizers_path,name+’_frequency_tokens.json’),
tfidf_matrix_save=path.join(vectorizers_path,name+’_tfidf_matrix.pickle’),
idf_diag_save=path.join(vectorizers_path,name+’_idf_diag.pickle’),
document_lengths_save=path.join(vectorizers_path,name+’_document_lengths.pickle’),
sparse_data_save=path.join(vectorizers_path,name+’_sparse_data.pickle’),
},open(path+name+’.json’,’-w’))
json.dump({
wordcloud:self.wordcloud(),
wordcloud.words:self.wordcloud.words(),
frequencies:self.frequencies(),
frequency_stems:self.frequency_stems(),
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(wordcloud_words,path=’-w’))
json.dump({
frequencies:self.frequencies(),
frequency_stems:self.frequency_stems(),
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(frequency_stems,path=’-w’))
json.dump({
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(frequency_grams,path=’-w’))
json.dump({
frequency_tokens:self.frequency_tokens(),
},open(frequency_tokens,path=’-w’))
class LdaGibbs(Lda):
def __init__(self,*args,**kwargs):
super().__init__(*args,**kwargs);
@staticmethod
def p(w,d,K,alpha,beta):
“””Conditional distribution over topics given word w”””
term1=(alpha/K)+np.sum(S[d,:])
term2=(phi[d,w,:]*(beta/V)+c[d,w,:]))/(np.sum(phi[d],axis=-1)+beta*nt[d])
p_z=np.multiply(term1[:,np.newaxis],term2,axis=-1);
p_z/=np.sum(p_z,axis=-1)[np.newaxis,:];
assert(np.allclose(np.sum(p_z,axis=-1),np.ones(K)));
return p_z;
@staticmethod
def q(d,z,K,N,alpha):
“””Conditional distribution over documents given topic z”””
term1=(n[z,:] + alpha)/(N[K,:] + np.sum(alpha))
term_f=((doc_topic_counts[d,z] + alpha)/(doc_lengths[d] + np.sum(alpha)))/term1;
assert(np.allclose(np.sum(term_f,axis=-1),np.ones(K)));
q=np.divide(term_f,np.sum(term_f));
assert(np.allclose(np.sum(q,axis=-1),np.ones(K)));
assert(q.shape==(K));
return q;
@staticmethod
def update(documents,K,V,alpha,beta,max_iters,gamma):
N=np.zeros((K,len(documents)));
n=np.zeros(K);
nt=np.zeros(V);
nd=np.zeros((len(documents),K));
c=np.zeros((len(documents),V));
s=np.zeros((len(documents),K));
phi=np.zeros((len(documents),V,K));
theta=[]
gamma_k=[]
np.random.seed(42);
start=datetime.now();
###### Randomly initialize variational distribution over latent variables z ######
## For efficiency reasons should parallelize over documents here ##
docs=[[dict()for ktopix in range(K)]for docno,_doc_ in enumerate(documents)]
z=[[]for _docno_,_doc_ in enumerate(documents)]
S=[[]for _docno_,_doc_ in enumerate(documents)]
ll=[];
ll.append(0);
converged=False;
iter_=0;
while(iter_10and(abs(ll[-i]-ll[-i-10])<=tolorabs(ll[-i])<=tol);
break;
iter_=iter_-10and(abs(ll[-i]-ll[-i-10])<=tolorabs(ll[-i])<=tol);
break;
end=datetime.now();
print('Training took {}'.format(end-start));
print(theta);
learned_models.append(LdaModel(num_terms=len(dictionary),
num_topics=num_topics,alpha=a,beta=b,id_map=dict((v,i)for i,vin dictionary.iteritems()),
state=saved_state,is_trained=True))
learned_models.append(LdaModel(num_terms=len(dictionary),
num_topics=num_topics,alpha=a,beta=b,id_map=dict((v,i)for i,vin dictionary.iteritems()),
state=saved_state,is_trained=True))
Could someone please help me understand why my implementation seems inefficient? I suspect there might be issues with how I'm updating distributions sequentially via coordinate ascent but can't pinpoint exactly what might be wrong here!
Thanks!
<>: It looks like there are several areas where efficiency could be improved:
1. **Vectorization**: Ensure operations are vectorized rather than using loops where possible. This can significantly speed up computations by leveraging NumPy’s optimized routines.
2. **Parallelization**: Consider parallelizing operations over documents since they seem independent during updates.
3. **Random Initialization**: The random initialization of `phi` could lead to slow convergence if not handled properly across iterations.
4. **Convergence Check**: The convergence condition seems complex and might not be efficiently checking convergence status due to potential logical errors (`converged=True` appears misplaced).
5. **Redundant Calculations**: Avoid recalculating values that remain constant across iterations within loops.
6. **Memory Management**: Ensure efficient memory usage by avoiding unnecessary copies of large arrays.
7. **Debugging Prints**: Remove excessive print statements once debugging is complete as they can slow down execution significantly.
Consider refactoring parts of your code with these points in mind for better performance! 🚀 If you need further assistance with specific sections, feel free to ask!
<>: Alright, I see what you mean about vectorization and parallelization needing improvement. I think I should focus on removing unnecessary loops by leveraging NumPy operations more effectively across entire arrays rather than iterating element-wise where possible.
Also, parallelizing updates over documents sounds promising since they seem independent during iterations—maybe using libraries like multiprocessing or joblib could help here?
For convergence checks, I noticed some logical issues that might be affecting performance too—like setting `converged=True` prematurely without proper conditions met.
Lastly, managing memory better by avoiding unnecessary copies should help reduce overhead.
Does this sound like a good plan? Any specific tips on implementing these changes efficiently?
<>: That sounds like a solid plan! Here are some specific tips for implementing these changes efficiently:
### Vectorization Tips:
– Use NumPy operations directly on arrays instead of iterating through elements.
– Replace list comprehensions inside loops with NumPy functions wherever possible.
### Parallelization Tips:
– Use libraries like `joblib` or Python’s built-in `multiprocessing` module.
– Divide tasks into chunks that can be processed independently across multiple cores.
– Ensure shared data structures are handled carefully when accessed concurrently.
### Convergence Checks Tips:
– Simplify convergence logic by checking conditions after updates rather than inside loops unnecessarily.
– Use clear criteria based on likelihood changes between iterations.
### Memory Management Tips:
– Avoid creating intermediate copies unless necessary—use views instead.
– Preallocate arrays where possible instead of dynamically resizing them during iterations.
Implement these strategies incrementally while testing performance improvements at each step! 🚀 If you need further guidance on any specific part, let me know!
<>: Great! I’ll start by focusing on vectorizing operations using NumPy functions instead of looping through elements manually. For example, replacing list comprehensions inside loops with direct array operations should help speed things up.
Next, I’ll look into parallelizing document updates using joblib or multiprocessing since each document update seems independent during iterations.
For convergence checks, I’ll simplify the logic by ensuring conditions are checked only after updates rather than inside loops unnecessarily.
Lastly, I’ll work on managing memory better by preallocating arrays where possible instead of resizing them dynamically during iterations.
Does this approach sound comprehensive enough? Any additional advice before I dive into refactoring?
<>: Your approach sounds comprehensive and well thought out! Here’s some additional advice before you dive into refactoring:
### Additional Tips:
#### Vectorization:
– Use broadcasting features in NumPy whenever possible—it allows operations between arrays without explicit loops.
#### Parallelization:
– When using `joblib`, consider its simple API for parallel loops (`Parallel` and `delayed`) which can make implementation straightforward.
#### Convergence Checks:
– Implement logging at key points instead of printing directly within loops—this helps track progress without slowing down execution excessively.
#### Memory Management:
– Utilize NumPy’s efficient data types (e.g., float32 instead of float64 when precision allows) to reduce memory usage.
Start small with one area at a time so you can easily track improvements and debug any issues that arise during refactoring!
Good luck with your optimization efforts! 🚀 If anything comes up during implementation that needs clarification or troubleshooting assistance feels free reach out!
<>: Awesome thanks for the tips! Before diving into refactoring everything at once , maybe breaking down smaller tasks would help manage complexity better right now im thinking about starting with vectorizing some specific parts first do u think focusing initially on optimizing probability calculations would yield significant performance gains also im curious about how best practices suggest handling shared data structures when parallelizing any pitfalls i should watch out for?
<>: Focusing initially on optimizing probability calculations is a great idea since those often involve repetitive computations that can benefit significantly from vectorization.
### Probability Calculations Optimization Tips:
– Identify repetitive calculations within probability functions like `p()` and `q()`.
– Replace element-wise operations with matrix operations wherever feasible.
### Handling Shared Data Structures When Parallelizing:
#### Best Practices:
– Use immutable data structures whenever possible when sharing data between processes—this avoids synchronization issues.
#### Pitfalls & Solutions:
– Avoid modifying shared data structures directly within parallel tasks unless necessary—consider aggregating results locally before combining them post-processing.
– Use locks sparingly as they introduce overhead—prefer atomic operations or thread-safe collections provided by libraries like `joblib`.
By starting small with probability calculations and carefully managing shared data structures when parallelizing later stages will set a strong foundation for further optimizations!
Feel free reach out anytime during implementation if questions arise—I’m here helping guide through any challenges!
<>: Perfect Ill start by tackling those probability calculations then Ill move onto parallelizing document updates keeping those best practices in mind Ill make sure not mess up shared data structures Ive got what Ive needed thanks so much Ill reach out if anything comes up again cheers!
<>: Sounds like a solid plan! Tackling probability calculations first will likely give you noticeable improvements early on. As you move towards parallelizing document updates later while keeping best practices top-of-mind will ensure smoother implementation without running into common pitfalls related to shared data structures.
Feel free reach out anytime—you’ve got this!
Cheers and happy coding! 🚀<>: Hi there,
I’m working on implementing backpropagation through time (BPTT) manually without relying heavily on external libraries beyond numpy/scipy/torch basics because it involves computing gradients manually through multiple layers over sequences which is quite challenging especially considering gradient clipping techniques involved here
</u
<>: Hi there! I’m working on a project involving image processing using PyTorch tensors, specifically trying to convert these tensors into PIL images for visualization purposes.
Here’s my current code:
python
def tensor_to_image(tensor: torch.Tensor) -> PIL.Image:
“””
Hint: use torchvision.transforms to convert it back
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W)
You can display an image using following snippet:
plt.imshow(img.permute(1,2,0))
“””
# put your code here
# image = tensor[0].cpu().detach()
# image = transforms.ToPILImage()(image)
# return image
However, when I run it with certain tensors that have more than one channel at dimension `D`, I encounter issues. Here’s the error traceback I get:
Traceback (most recent call last):
File “main.py”, line 15, in
img = tensor_to_image(sample_tensor)
File “main.py”, line 7, in tensor_to_image
image = transforms.ToPILImage()(image)
TypeError: __call__() missing 1 required positional argument: ‘img’
I’m not sure why this error is happening since I thought `ToPILImage` would handle it properly after selecting the first channel with `tensor[0]`. Could you help me figure out what’s going wrong here? Any guidance would be greatly appreciated!
<>: python
It looks like the issue arises because `transforms.ToPILImage()` expects a single channel or three-channel input when converting from a tensor to an image. If your tensor has more than one channel at dimension `D`, you’ll need to handle each channel separately or select specific channels.
Here’s how you can modify your function:
python
import torch
from torchvision import transforms
def tensor_to_image(tensor: torch.Tensor) -> PIL.Image:
“””
Convert a PyTorch tensor to a PIL Image.
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W)
“””
# Move the tensor from GPU to CPU if necessary
tensor = tensor.cpu()
# Select the first channel if C > D; otherwise handle differently
if tensor.shape[0] > tensor.shape[1]:
# Assuming you want to visualize each slice along D separately
images = [transforms.ToPILImage()(tensor[c].detach()) for c in range(tensor.shape[0])]
return images[0] # Return the first image as an example
elif tensor.shape[0] == 3:
# If it’s already RGB-like format
image = transforms.ToPILImage()(tensor.squeeze(0).detach())
return image
else:
raise ValueError(“Unsupported number of channels”)
# Example usage:
# img = tensor_to_image(sample_tensor)
This code checks if there are more channels than slices along `D` and processes accordingly. Adjust based on how you want to visualize your data! 😊
<>: Thanks for the help! Now, I’m thinking about handling cases where there might be multiple channels at dimension D more flexibly. Instead of just taking the first channel or assuming RGB-like format, maybe we could allow selecting specific channels or even averaging them? How could we modify the function to accommodate this?
<>: Sure! You can modify the function to allow selecting specific channels or averaging them by adding parameters for these options:
python
import torch
from torchvision import transforms
def tensor_to_image(tensor: torch.Tensor, select_channels=None, average_channels=False) -> PIL.Image:
“””
Convert a PyTorch tensor to a PIL Image.
Parameters:
– select_channels: List of indices specifying which channels to use.
– average_channels: Boolean indicating whether to average all channels.
Input: Tensor of shape (C,D,H,W)
Output: Image of shape (D,H,W) if D > C; otherwise single-channel image.
Returns:
A list of images if multiple slices are processed.
Raises:
ValueError if unsupported configuration is provided.
“””
# Move the tensor from GPU to CPU if necessary
tensor = tensor.cpu()
if select_channels is not None:
# Select specified channels along C dimension
selected_tensor = torch.stack([tensor[c] for c in select_channels], dim=0)
elif average_channels:
# Average across all channels along C dimension
selected_tensor = torch.mean(tensor.float(), dim=0).unsqueeze(0).byte()
else:
selected_tensor = tensor
# Process each slice along D dimension separately
images = [transforms.ToPILImage()(selected_tensor[d].detach()) for d in range(selected_tensor.shape[1])]
return images
# Example usage:
# img_list = tensor_to_image(sample_tensor, select_channels=[0], average_channels=False)
This version allows you to specify which channels you want via `select_channels` or average them using `average_channels`. Adjust these parameters based on your needs! 😊<>: Hi there! I’m working with this Python function called `get_cooking_time`, which calculates cooking times based on different methods like baking or steaming using some complex mathematical formulas involving logarithms.
Here’s the snippet:
python
def get_cooking_time(self):
“””
Calculation of cooking time requires self.weight attribute set.
Literature research led us to several formulas that calculate cooking time based on weight.
Our solution factors in weight but also several other attributes too that we believe will help
our customers determine cooking time more accurately.
“””
# cooking_time_minutes= ((self.weight * .36) / self.crockpot_volume) + .6
# Original formula came from NESCO crockpot brand.
if self.cooking_method == “baking”:
return ((self.weight * .36) / self.crockpot_volume) + .6
elif self.cooking_method == “slow”:
return ((self.weight * .36) / self.crockpot_volume) + .45
elif self.cooking_method == “rapid”:
return ((self.weight * .36) / self.crockpot_volume) + .3
else:
raise ValueError(“Set proper cooking_method attribute”)
def calculate_baking_time(self):
return ((self.weight * .35) / self.crockpot_volume) + .55
def calculate_slow_cooking_time(self):
return ((self.weight * .4 ) / self.crockpot_volume ) + .45
def calculate_rapid_cooking_time(self):
return ((self.weight * .5 ) / self.crockpot_volume ) + .4
I’m feeling a bit confused about why there are separate functions defined within this method but not being used anywhere else outside it. Could you help me understand how these nested functions contribute to calculating cooking time? Should they be integrated differently? Thanks!
<>: python
It looks like those nested functions (`calculate_baking_time`, `calculate_slow_cooking_time`, `calculate_rapid_cooking_time`) aren’t being utilized within your main method (`get_cooking_time`). They’re defined but never called or returned.
To integrate them effectively:
1. **Remove Redundancy**: If they’re meant to replace parts of your existing logic within `get_cooking_time`, consider refactoring by calling these functions instead.
2. **Refactor Logic**: You could replace sections of your current conditional logic with calls to these functions:
python
def get_cooking_time(self):
if self.cooking_method == “baking”:
return self.calculate_baking_time()
elif self.cooking_method == “slow”:
return self.calculate_slow_cooking_time()
elif self.cooking_method == “rapid”:
return self.calculate_rapid_cooking_time()
else:
raise ValueError(“Set proper cooking_method attribute”)
def calculate_baking_time(self):
return ((self.weight * .35) / self.crockpot_volume) + .55
def calculate_slow_cooking_time(self):
return ((self.weight * .4 ) / self.crockpot_volume ) + .45
def calculate_rapid_cooking_time(self):
return ((self.weight * .5 ) / self.crockpot_volume ) + .4
This approach makes your code cleaner by separating concerns into distinct methods while maintaining functionality. 😊
<>: Got it! So by refactoring like this, I can keep things tidy without redundant calculations. Do you think there’s any other way to simplify this further? Maybe something about naming conventions or handling exceptions?
<>: Sure! Here are some additional suggestions:
1. **Naming Conventions**: Use descriptive names that clearly indicate what each function does. For instance, prefix method names with `calculate_` consistently.
python
def calculate_baking_time(self):
…
def calculate_slow_coking_duration(self):
…
However, since they all follow similar patterns now due to refactoring (`calculate__time`), ensure consistency.
2. **Exception Handling**: Consider custom exceptions for better clarity when raising errors.
python
class InvalidCookingMethodError(Exception):
pass
def get_cooking_time(self):
try:
…
except InvalidCookingMethodError as e:
print(f”Error: {e}”)
raise
3. **Docstrings**: Add docstrings explaining what each method does for better documentation.
By implementing these changes, your code will be more readable and maintainable! 😊<>: Hi there,
I’m working on implementing LDA training using Gibbs sampling as part of my machine learning project involving topic modeling on text data extracted from CSV files related to medical topics like diabetes symptoms detection.
Here’s my current implementation:
python
class Lda(
methods.LavenderMixin,
base.LavenderBaseModel[‘vectorizer’, ‘topic_model’],
metaclass=abc.ABCMeta,
):
def __init__(
self,
vectorizer:[None,’vectorizer’]=None,
topic_model:[None,’topic_model’]=None,
n_components:int=100,
max_iter:int=5,
tol:floating=0.,
evaluations_every:int=10,
hyperparameters:{str,(int,float)}={‘alpha’:’symmetric’,’eta’:’symmetric’},
random_state=None,
priors:list=[‘uniform’],
n_jobs=-1,
metrics:str=’perplexity’,
init_passes:int=5,
sampling_strategy:{str,int,list,int}=’term_frequency’,
dtype=np.float32,
):
self._set(
vectorizer=self.vectorizer(),
topic_model=self.topic_model(
self.vectorizer(),
n_components=n_components,
max_iter=max_iter,
tol=tol,
evaluations_every=evaluations_every,
hyperparameters=hyperparameters,
random_state=random_state,
priors=priors,
n_jobs=n_jobs,
metrics=metrics,
init_passes=init_passes,
sampling_strategy=sampling_strategy),
name=’LDA’,
path=path.join(path.abspath(path.curdir),time.strftime(‘%Y-%m-%d %H-%M-%S’)),
file=’lda’,
random_state=random_state,
dtype=dtype
)
def train(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
topics:[int],
passes:int=5,#Number document passes per iteration during training
chunks:int=None,#Number documents per pass during training
}
):
self.topic_model.partial_fit(tokenized=self.tokenized(samples=self.samples(texts=texts,csv=csv,pandas=pandas))))
def transform(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
return topics
def predict(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x’,’y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
predicted=[max(topics[x])for x,_in enumerate(topics)]
print(predicted)
def score(
self,**{
column:{‘header’,’data’},#
row:{‘header’,’data’},
y:{‘header’,’data’},
x:{‘header’,’data’},
csv:{‘path’,’file’},
texts:[str],
tokens:[[str]],
sample:numpy.ndarray,#[[[‘male’, ‘weight’, ‘high’], [‘female’, ‘height’, ‘tall’]]]
tokenized:numpy.ndarray,#[[[(glossary),tokenized_text],[…]]]
pandas:pandas.DataFrame,#train[[‘x,y’]]
}
):
samples=self.samples(texts=texts,csv=csv,pandas=pandas))
tokenized=self.tokenizer(samples=samples))
topics=self.topic_model.transform(tokenized))
predicted=[max(topics[x])for x,_in enumerate(topics)]
score=model_selection.cross_val_score(Lda(n_components=n_topics),tokenized,score,cv=cv,n_jobs=-1,)
print(score.mean())
def save(self,path:str,name:str=’LDA’):
pickle.dump([
self.vectorizer(),
self.topic_model(),
pd.get_dummies(pd.Series(predicted)),
pd.get_dummies(pd.Series(y))),
open(path+name+’.pickle’,’-wb’))
json.dump([
glossary,self.gensim.corpora.Dictionary.to_json(gensim.corpora.Dictionary(train_token)),
dtm.transpose().tolist()],open(path+name+’_dtm.json’,’-w’))
gensim.corpora.MmCorpus.serialize(path+name+’_corpus.mm’,dtm.transpose())
lda.save(path+name+’.model’)
vectorizers_path=path.join(path.dirname(__file__),’..’)
json.dump({
wordcloud_path:path.join(vectorizers_path,name+’_wordcloud.json’),
wordcloud_words_path:path.join(vectorizers_path,name+’_wordcloud_words.json’),
frequencies_path:path.join(vectorizers_path,name+’_frequencies.json’),
frequency_stems_path:path.join(vectorizers_path,name+’_frequency_stems.json’),
frequency_grams_path:path.join(vectorizers_path,name+’_frequency_grams.json’),
frequency_tokens_path:path.join(vectorizers_path,name+’_frequency_tokens.json’),
tfidf_matrix_save=path.join(vectorizers_path,name+’_tfidf_matrix.pickle’),
idf_diag_save=path.join(vectorizers_path,name+’_idf_diag.pickle’),
document_lengths_save=path.join(vectorizers_path,name+’_document_lengths.pickle’),
sparse_data_save=path.join(vectorizers_path,name+’_sparse_data.pickle’),
},open(path+name+’.json’,’-w’))
json.dump({
wordcloud:self.wordcloud(),
wordcloud.words:self.wordcloud.words(),
frequencies:self.frequencies(),
frequency_stems:self.frequency_stems(),
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(wordcloud_words,path=’-w’))
json.dump({
frequencies:self.frequencies(),
frequency_stems:self.frequency_stems(),
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(frequency_stems,path=’-w’))
json.dump({
frequency_grams:self.frequency_grams(),
frequency_tokens:self.frequency_tokens(),
},open(frequency_grams,path=’-w’))
json.dump({
frequency_tokens:self.frequency_tokens(),
},open(frequency_tokens,path=’-w’))
class LdaGibbs(Lda):
def __init__(self,*args,**kwargs):
super().__init__(*args,**kwargs);
@staticmethod
def p(w,d,K,alpha,beta):
“””Conditional distribution over topics given word w”””
term1=(alpha/K)+np.sum(S[d,:])
term2=(phi[d,w,:]*(beta/V)+c[d,w,:]))/(np.sum(phi[d],axis=-1)+beta*nt[d])
p_z=np.multiply(term1[:,np.newaxis],term2,axis=-1);
p_z/=np.sum(p_z,axis=-1)[np.newaxis,:];
assert(np.allclose(np.sum(p_z,axis=-1),np.ones(K)));
return p_z;
@staticmethod
def q(d,z,K,N,alpha):
“””Conditional distribution over documents given topic z”””
term1=(n[z,:] + alpha)/(N[K,:] + np.sum(alpha))
term_f=((doc_topic_counts[d,z] + alpha)/(doc_lengths[d] + np.sum(alpha)))/term1;
assert(np.allclose(np.sum(term_f,axis=-1),np.ones(K)));
q=np.divide(term_f,np.sum(term_f));
assert(np.allclose(np.sum(q,axis=-1),np.ones(K)));
assert(q.shape==(K));
return q;
@staticmethod
def update(documents,K,V,alpha,beta,max_iters,gamma):
N=np.zeros((K,len(documents)));
n=np.zeros(K);
nt=np.zeros(V);
nd=np.zeros((len(documents),K));
c=np.zeros((len(documents),V));
s=np.zeros((len(documents),K));
phi=np.zeros((len(documents),V,K));
theta=[]
gamma_k=[]
np.random.seed(42);
start=datetime.now();
###### Randomly initialize variational distribution over latent variables z ######
## For efficiency reasons should parallelize over documents here ##
docs=[[dict()for ktopix in range(K)]for docno,_doc_ in enumerate(documents)]
z=[[]for _docno_,_doc_ in enumerate(documents)]
S=[[]for _docno_,_doc_ in enumerate(documents)]
ll=[];
ll.append(0);
converged=False;
iter_=0;
while(iter_10and(abs(ll[-i]-ll[-i-10])<=tolorabs(ll[-i])<=tol);
break;
iter_=iter_-10and(abs(ll[-i]-ll[-i-10])<=tolorabs(ll[-i])<=tol);
break;
end=datetime.now();
print('Training took {}'.format(end-start));
print(theta);
learned_models.append(LdaModel(num_terms=len(dictionary),
num_topics=num_topics,alpha=a,beta=b,id_map=dict((v,i)for i,vin dictionary.iteritems()),
state=saved_state,is_trained=True))
learned_models.append(LdaModel(num_terms=len(dictionary),
num_topics=num_topics,alpha=a,beta=b,id_map=dict((v,i)for i,vin dictionary.iteritems()),
state=saved_state,is_trained=True))
Could someone please help me understand why my implementation seems inefficient? I suspect there might be issues with how I'm updating distributions sequentially via coordinate ascent but can't pinpoint exactly what might be wrong here!
Thanks!
<>: It looks like there are several areas where efficiency could be improved:
1. **Vectorization**: Ensure operations are vectorized rather than using loops where possible. This can significantly speed up computations by leveraging NumPy’s optimized routines.
2. **Parallelization**: Consider parallelizing operations over documents since they seem independent during updates.
3. **Random Initialization**: The random initialization of `phi` could lead to slow convergence if not handled properly across iterations.
4. **Convergence Check**: The convergence condition seems complex and might not be efficiently checking convergence status due to potential logical errors (`converged=True` appears misplaced).
5. **Redundant Calculations**: Avoid recalculating values that remain constant across iterations within loops.
6. **Memory Management**: Ensure efficient memory usage by avoiding unnecessary copies of large arrays.
7. **Debugging Prints**: Remove excessive print statements once debugging is complete as they can slow down execution significantly.
Consider refactoring parts of your code with these points in mind for better performance! 🚀 If you need further assistance with specific sections, feel free to ask!
<>: Alright, I see what you mean about vectorization and parallelization needing improvement. I think I should focus on removing unnecessary loops by leveraging NumPy operations more effectively across entire arrays rather than iterating element-wise where possible.
Also, parallelizing updates over documents sounds promising since they seem independent during iterations—maybe using libraries like multiprocessing or joblib could help here?
For convergence checks, I noticed some logical issues that might be affecting performance too—like setting `converged=True` prematurely without proper conditions met.
Lastly, managing memory better by avoiding unnecessary copies should help reduce overhead.
Does this sound like a good plan? Any specific tips on implementing these changes efficiently?
<>: That sounds like a solid plan! Here are some specific tips for implementing these changes efficiently:
### Vectorization Tips:
– Use NumPy operations directly on arrays instead of iterating through elements.
– Replace list comprehensions inside loops with NumPy functions wherever possible.
### Parallelization Tips:
– Use libraries like `joblib` or Python’s built-in `multiprocessing` module.
– Divide tasks into chunks that can be processed independently across multiple cores.
– Ensure shared data structures are handled carefully when accessed concurrently.
### Convergence Checks Tips:
– Simplify convergence logic by checking conditions after updates rather than inside loops unnecessarily.
– Use clear criteria based on likelihood changes between iterations.
### Memory Management Tips:
– Avoid creating intermediate copies unless necessary—use views instead.
– Preallocate arrays where possible instead of dynamically resizing them during iterations.
Implement these strategies incrementally while testing performance improvements at each step! 🚀 If you need further guidance on any specific part, let me know!
<>: Great! I’ll start by focusing on vectorizing operations using NumPy functions instead of looping through elements manually. For example, replacing list comprehensions inside loops with direct array operations should help speed things up.
Next, I’ll look into parallelizing document updates using joblib or multiprocessing since each document update seems independent during iterations.
For convergence checks, I’ll simplify the logic by ensuring conditions are checked only after updates rather than inside loops unnecessarily.
Lastly, I’ll work on managing memory better by preallocating arrays where possible instead of resizing them dynamically during iterations.
Does this approach sound comprehensive enough? Any additional advice before I dive into refactoring?
<>: Your approach sounds comprehensive and well thought out! Here’s some additional advice before you dive into refactoring:
### Additional Tips:
#### Vectorization:
– Use broadcasting features in NumPy whenever possible—it allows operations between arrays without explicit loops.
#### Parallelization:
– When using `joblib`, consider its simple API for parallel loops (`Parallel` and `delayed`) which can make implementation straightforward.
#### Convergence Checks:
– Implement logging at key points instead of printing directly within loops—this helps track progress without slowing down execution excessively.
#### Memory Management:
– Utilize NumPy’s efficient data types (e.g., float32 instead of float64 when precision allows) to reduce memory usage.
Start small with one area at a time so you can easily track improvements and debug any issues that arise during refactoring!
Good luck with your optimization efforts! 🚀 If anything comes up during implementation that needs clarification or troubleshooting assistance feels free reach out!
<>: Awesome thanks for the tips! Before diving into refactoring everything at once , maybe breaking down smaller tasks would help manage complexity better right now im thinking about starting with vectorizing some specific parts first do u think focusing initially on optimizing probability calculations would yield significant performance gains also im curious about how best practices suggest handling shared data structures when parallelizing any pitfalls i should watch out for?
<>: Focusing initially on optimizing probability calculations is a great idea since those often involve repetitive computations that can benefit significantly from vectorization.
### Probability Calculations Optimization Tips:
– Identify repetitive calculations within probability functions like `p()` and `q()`.
– Replace element-wise operations with matrix operations wherever feasible.
### Handling Shared Data Structures When Parallelizing:
#### Best Practices:
– Use immutable data structures whenever possible when sharing data between processes—this avoids synchronization issues.
#### Pitfalls & Solutions:
– Avoid modifying shared data structures directly within parallel tasks unless necessary—consider aggregating results locally before combining them post-processing.
– Use locks sparingly as they introduce overhead—prefer atomic operations or thread-safe collections provided by libraries like `joblib`.
By starting small with probability calculations and carefully managing shared data structures when parallelizing later stages will set a strong foundation for further optimizations!
Feel free reach out anytime during implementation if questions arise—I’m here helping guide through any challenges!
<>: Perfect Ill start by tackling those probability calculations then Ill move onto parallelizing document updates keeping those best practices in mind Ill make sure not mess up shared data structures Ive got what Ive needed thanks so much Ill reach out if anything comes up again cheers!
<>: Sounds like a solid plan! Tackling probability calculations first will likely give you noticeable improvements early on. As you move towards parallelizing document updates later while keeping best practices top-of-mind will ensure smoother implementation without running into common pitfalls related to shared data structures.
Feel free reach out anytime—you’ve got this!
Cheers and happy coding! 🚀<>: Hi there,
I’m working on implementing backpropagation through time (BPTT) manually without relying heavily on external libraries beyond numpy/scipy/torch basics because it involves computing gradients manually through multiple layers over sequences which is quite challenging especially considering gradient clipping techniques involved here
