[Term Entry] PyTorch Tensor Operations .hstack() #5528
+70
−0
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
|
mamtawardhani
added
new entry
fixed meta data table
mamtawardhani
requested changes
Oct 19, 2024
There was a problem hiding this comment.
Hey @clonymontana thank you for contributing to Codecademy Docs 😄
I've suggested some changes, could you please review and modify those at your earliest convenience? Thank you! 🚀
Comment on lines
1
to
15
| --- | ||
| Title: '.hstack()' | ||
| Description: 'The `.hastack(tensors)` is a function used to concetante two or more tensors along horizontal axis.' | ||
| Subjects: | ||
| - 'AI' | ||
| - 'Data Science' | ||
| Tags: | ||
| - 'AI' | ||
| - 'Data Types' | ||
| - 'Deep Learning' | ||
| - 'Functions' | ||
| CatalogContent: | ||
| - 'intro-to-py-torch-and-neural-networks' | ||
| - 'paths/data-science' | ||
| --- |
There was a problem hiding this comment.
Suggested change
| --- | |
| Title: '.hstack()' | |
| Description: 'The `.hastack(tensors)` is a function used to concetante two or more tensors along horizontal axis.' | |
| Subjects: | |
| - 'AI' | |
| - 'Data Science' | |
| Tags: | |
| - 'AI' | |
| - 'Data Types' | |
| - 'Deep Learning' | |
| - 'Functions' | |
| CatalogContent: | |
| - 'intro-to-py-torch-and-neural-networks' | |
| - 'paths/data-science' | |
| --- | |
| --- | |
| Title: '.hstack()' | |
| Description: 'Concatenates two or more tensors along the horizontal axis (column-wise)' | |
| Subjects: | |
| - 'AI' | |
| - 'Data Science' | |
| Tags: | |
| - 'AI' | |
| - 'Data Types' | |
| - 'Deep Learning' | |
| - 'Functions' | |
| CatalogContent: | |
| - 'intro-to-py-torch-and-neural-networks' | |
| - 'paths/data-science' | |
| --- |
There was a problem hiding this comment.
description should start with a verb
| - 'paths/data-science' | ||
| --- | ||
|
|
||
| In PyTorch, **`.hastack()`** (short for horizontal stack)is a function used to concatenate two or more tensors along the horizontal axis (axis1). This operation is useful for combining data with the same number of rows but differing in the number of columns. It acts similarly to numpy's 'np.hastack()' and is particulary handy when you're working with data that needs to be concatenated side by side before being fed into a model for training or inference. |
There was a problem hiding this comment.
Suggested change
| In PyTorch, **`.hastack()`** (short for horizontal stack)is a function used to concatenate two or more tensors along the horizontal axis (axis1). This operation is useful for combining data with the same number of rows but differing in the number of columns. It acts similarly to numpy's 'np.hastack()' and is particulary handy when you're working with data that needs to be concatenated side by side before being fed into a model for training or inference. | |
| In PyTorch, **`.hstack()`** (short for horizontal stack) is a function used to concatenate two or more tensors along the horizontal axis (`axis=1`). This operation is helpful in combining data with the same number of rows but differing in the number of columns. It acts similarly to NumPy's `np.hstack()` and is particularly handy for data that needs to be concatenated side by side before being fed into a model for training or inference. |
There was a problem hiding this comment.
fixed spelling mistakes and reframed the sentence a bit
|
|
||
| The basic syntax of `.hstack()` in PyTorch is as falows: | ||
|
|
||
| ```python |
There was a problem hiding this comment.
Suggested change
| ```python | |
| ```pseudo |
There was a problem hiding this comment.
syntax is to be wrapped in the pseudo block
|
|
||
| Where: | ||
|
|
||
| - `tensors` is a sequence of tesors with the same numbers of rows. |
There was a problem hiding this comment.
Suggested change
| - `tensors` is a sequence of tesors with the same numbers of rows. | |
| - `tensors`: A sequence of tensors with the same number of rows. All tensors must have the same number of dimensions and the same size in all dimensions except for the dimension corresponding to the horizontal stacking. |
| Where: | ||
|
|
||
| - `tensors` is a sequence of tesors with the same numbers of rows. | ||
| - The function returns a new tensor containing the horizontal concatenation of the imput tensors. |
There was a problem hiding this comment.
Suggested change
| - The function returns a new tensor containing the horizontal concatenation of the imput tensors. | |
| The function returns a new tensor containing the horizontal concatenation of the input tensors. |
There was a problem hiding this comment.
No bullet point needed as it is not a parameter
| b = torch.tensor([[5, 6],[7, 8]]) | ||
|
|
||
| # Horizontal stack | ||
| c = toarch.hstack((a, b)) |
There was a problem hiding this comment.
Suggested change
| c = toarch.hstack((a, b)) | |
| c = torch.hstack((a, b)) |
|
|
||
| # Exemple | ||
|
|
||
| Here's simple example demonstrating how '.hstack()' can be used to concatenate tensors: |
There was a problem hiding this comment.
Suggested change
| Here's simple example demonstrating how '.hstack()' can be used to concatenate tensors: | |
| Here's an example demonstrating how `.hstack()` can be used to concatenate tensors: |
Comment on lines
58
to
68
|
|
||
| # Codebyte Exemple | ||
|
|
||
| ```python | ||
| import torch | ||
|
|
||
| # Create 1D tensors | ||
| a = torch.tensor([1, 2]) | ||
| b = torch.tensor([3, 4]) | ||
|
|
||
| # Horizontal stack | ||
| c = torch.tensor((a, b)) | ||
|
|
||
| print(c) | ||
| ``` | ||
| In this Codebyte example, two 1-dimensional tensors are horizontally concatenated, showcasing how `.hstack()`seamlessly combines tensors not just in 2D but also in 1D tensors, further emphasizing the function's utility in handling tensors of varios dimensions as long as thay share the same number of rows (or are all 1-dimensional). | ||
|
|
||
|
|
||
|
|
||
|
|
There was a problem hiding this comment.
This can be removed since the Codecademy Compiler does not have PyTorch dependencies yet.
mamtawardhani
added
status: review 1️⃣ completed
status: waiting for author
and removed
status: under review
|
Hello @mamtawardhani Thank you for the opportunity to contribute 😄 |
clonymontana
force-pushed
the
PyTorch-Tensor-Operations
branch
from
5d50bf1
to
0a0bae9
Compare
mamtawardhani
approved these changes
Oct 21, 2024
There was a problem hiding this comment.
Thank you for contributing to Codecademy Docs @clonymontana 😄
The entry looks good for a second round of review! 🚀
mamtawardhani
added
status: ready for next review
and removed
status: waiting for author
labels
Sriparno08
added
status: under review
clonymontana
force-pushed
the
PyTorch-Tensor-Operations
branch
from
e3b1da1
to
8fb51fd
Compare
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
-Added the .hstack() function term under PyTorch
-Closes Issues #5468
Issue Solved
Type of Change
Checklist
mainbranch.Issues Solvedsection.