LESSON 44 OF 52 · CHAPTER 06
Classification Loss
Scalar categorical loss and selecting maximum score index
03-classification-loss.srp
// Stable categorical loss from logits without a softmax vector.
bind logits = [4.0];
bind label = 0;
print(crossentropy(logits, label));
print(logprob(logits, label));
// For multiple classes, select a class by its original index:
print(argmax([1.0, 4.0, 2.0]));
stdout
0.0
-0.0
1
Write what you expect this program to print. Check prediction for a line-level hint without revealing the answer, or reveal the verified output when ready. This checks displayed text only; it does not run Scarp or save your input.
UNDERSTAND THE CODE
Explanation & practice
What this example teaches
Scalar categorical loss and selecting maximum score index
Expected behavior
0.0, -0.0, 1
Practice: Read the source before looking at the output. Predict the values and their order, then use the pinned compiler to execute this lesson and compare the exact fixture. Identify which operation produces the result.
वर्गीकरण लॉस
crossentropy(logits, label) और logprob(logits, label) को एक वर्ग की संख्याओं पर चलाया गया है। argmax([1.0, 4.0, 2.0]) बड़े स्कोर का इंडेक्स लौटाता है। ये अंकगणितीय उदाहरण हैं, न कि प्रशिक्षित वर्गीकरण मॉडल।
आउटपुट: 0.0, -0.0 और 1 तीन पंक्तियों में। -0.0 का स्वरूप भी stdout फ़िक्स्चर का हिस्सा है।
अभ्यास: argmax का परिणाम 4.0 के बजाय 1 क्यों है?
Explanations are sourced from the project's English chapter guide and हिन्दी अध्याय. The Scarp program and output are unchanged.
SOURCE-BACKED SELF-CHECK · स्रोत-आधारित अभ्यास
Quick checkpoint / त्वरित अभ्यास
Which index does argmax([1.0, 4.0, 2.0]) print?argmax([1.0, 4.0, 2.0]) कौन-सा index प्रिंट करता है?
Reveal the source-backed answer / सही उत्तर देखें
1 — The maximum value is 4.0, which is at zero-based index 1. The fixture prints 1.1 — सबसे बड़ा मान 4.0 है, जो शून्य-आधारित index 1 पर है। इसलिए आउटपुट 1 है.
This question checks understanding of a recorded compiler output. It does not run Scarp in the browser. यह प्रश्न सत्यापित आउटपुट पर आधारित है; ब्राउज़र में Scarp नहीं चलता।
LEARNING PROGRESS
Keep your place
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TRY IT LOCALLY
Verify, then execute
Use the pinned compiler from the course root. Keep any imported _support files next to the source.
scarp verify examples/06-ai-foundations/03-classification-loss.srp
scarp exec examples/06-ai-foundations/03-classification-loss.srpThe website does not run code. Read the CLI guide ↗
Pinned compiler: Scarp v0.0.6-alpha.3
· ceff07d9014f. No simulated output.