Skip to content

Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
tianyizheng02 merged 14 commits into from
Oct 10, 2023
Merged

Added categorical_crossentropy loss function #10152

Changes from all commits
Commits
Show all changes
14 commits
Select commit Hold shift + click to select a range
4893c31
Create categorical_cross_entropy.py
THEGAMECHANGER416 Oct 9, 2023
dc3129b
Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1
THEGAMECHANGER416 Oct 9, 2023
35fa3f3
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 9, 2023
341df18
Update machine_learning/loss_functions/categorical_cross_entropy.py
THEGAMECHANGER416 Oct 9, 2023
b673376
Update categorical_cross_entropy.py
THEGAMECHANGER416 Oct 9, 2023
06110d9
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 9, 2023
1cff490
Update categorical_cross_entropy.py
THEGAMECHANGER416 Oct 9, 2023
65c4702
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 9, 2023
eeb0bc6
Update machine_learning/loss_functions/categorical_cross_entropy.py
THEGAMECHANGER416 Oct 10, 2023
c60aad4
Update machine_learning/loss_functions/categorical_cross_entropy.py
THEGAMECHANGER416 Oct 10, 2023
e194822
Update machine_learning/loss_functions/categorical_cross_entropy.py
THEGAMECHANGER416 Oct 10, 2023
f163f47
Update machine_learning/loss_functions/categorical_cross_entropy.py
THEGAMECHANGER416 Oct 10, 2023
cef2b27
Update categorical_cross_entropy.py
THEGAMECHANGER416 Oct 10, 2023
94bb4ce
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 10, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
85 changes: 85 additions & 0 deletions machine_learning/loss_functions/categorical_cross_entropy.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
"""
Calculate Categorical Cross-Entropy Loss between true class labels and
predicted class probabilities.

Parameters:
- y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.
- y_pred: Predicted class probabilities as a NumPy array.
- epsilon: Small constant to avoid numerical instability.

Returns:
- ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

Example:
>>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
>>> categorical_cross_entropy(true_labels, pred_probs)
0.567395975254385

>>> y_true = np.array([[1, 0], [0, 1]])
>>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
>>> categorical_cross_entropy(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: Input arrays must have the same shape.

>>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
>>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
>>> categorical_cross_entropy(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.
>>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
>>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
>>> categorical_cross_entropy(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: y_true must be one-hot encoded.

>>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
>>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
>>> categorical_cross_entropy(y_true, y_pred)
Traceback (most recent call last):
...
ValueError: Predicted probabilities must sum to approximately 1.
"""
if y_true.shape != y_pred.shape:
raise ValueError("Input arrays must have the same shape.")

if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
raise ValueError("y_true must be one-hot encoded.")

if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
raise ValueError("Predicted probabilities must sum to approximately 1.")

# Clip predicted probabilities to avoid log(0)
y_pred = np.clip(y_pred, epsilon, 1)

# Calculate categorical cross-entropy loss
return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
import doctest

doctest.testmod()
, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
})();
Added categorical_crossentropy loss function by THEGAMECHANGER416 · Pull Request #10152 · TheAlgorithms/Python · GitHub
Skip to content
Added categorical_crossentropy loss function#10152

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

tianyizheng02
merged 14 commits into

from

Oct 10, 2023
 Merged


Added categorical_crossentropy loss function
#10152
 Changes from all commits
Commits

Show all changes


14 commits


Select commit
Hold shift + click to select a range
4893c31

Create categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
dc3129b

Merge pull request #1 from THEGAMECHANGER416/THEGAMECHANGER416-patch-1


THEGAMECHANGER416 Oct 9, 2023
35fa3f3

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
341df18

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
b673376

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
06110d9

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
1cff490

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 9, 2023
65c4702

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 9, 2023
eeb0bc6

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
c60aad4

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
e194822

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
f163f47

Update machine_learning/loss_functions/categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
cef2b27

Update categorical_cross_entropy.py


THEGAMECHANGER416 Oct 10, 2023
94bb4ce

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 10, 2023

File filter

Filter by extension
Filter by extension
Conversations

Failed to load comments. 
Loading
Jump to

Jump to file


Failed to load files. 
Loading
 Diff view

 Diff view


85 changes: 85 additions & 0 deletions
machine_learning/loss_functions/categorical_cross_entropy.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,85 @@
"""
Categorical Cross-Entropy Loss

This function calculates the Categorical Cross-Entropy Loss between true class
labels and predicted class probabilities.

Formula:
Categorical Cross-Entropy Loss = -Σ(y_true * ln(y_pred))

Resources:
- [Wikipedia - Cross entropy](https://en.wikipedia.org/wiki/Cross_entropy)
"""

import numpy as np


def categorical_cross_entropy(
 y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1e-15
) -> float:
 """
 Calculate Categorical Cross-Entropy Loss between true class labels and
 predicted class probabilities.

 Parameters:
 - y_true: True class labels (one-hot encoded) as a NumPy array.
Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 - y_pred: Predicted class probabilities as a NumPy array.
 - epsilon: Small constant to avoid numerical instability.

 Returns:
 - ce_loss: Categorical Cross-Entropy Loss as a floating-point number.

 Example:
 >>> true_labels = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
 >>> pred_probs = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1], [0.0, 0.1, 0.9]])
 >>> categorical_cross_entropy(true_labels, pred_probs)
 0.567395975254385

 >>> y_true = np.array([[1, 0], [0, 1]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Input arrays must have the same shape.

 >>> y_true = np.array([[2, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

Comment thread
THEGAMECHANGER416 marked this conversation as resolved.

 >>> y_true = np.array([[1, 0, 1], [1, 0, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.0], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: y_true must be one-hot encoded.

 >>> y_true = np.array([[1, 0, 0], [0, 1, 0]])
 >>> y_pred = np.array([[0.9, 0.1, 0.1], [0.2, 0.7, 0.1]])
 >>> categorical_cross_entropy(y_true, y_pred)
 Traceback (most recent call last):
 ...
 ValueError: Predicted probabilities must sum to approximately 1.
 """
 if y_true.shape != y_pred.shape:
 raise ValueError("Input arrays must have the same shape.")

 if np.any((y_true != 0) & (y_true != 1)) or np.any(y_true.sum(axis=1) != 1):
 raise ValueError("y_true must be one-hot encoded.")

 if not np.all(np.isclose(np.sum(y_pred, axis=1), 1, rtol=epsilon, atol=epsilon)):
 raise ValueError("Predicted probabilities must sum to approximately 1.")

 # Clip predicted probabilities to avoid log(0)
 y_pred = np.clip(y_pred, epsilon, 1)

 # Calculate categorical cross-entropy loss
 return -np.sum(y_true * np.log(y_pred))


if __name__ == "__main__":
 import doctest

 doctest.testmod()