[EXTERNAL] docs(keras2): use categorical_crossentropy for single-label multi-class#3048
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EldritchGriffin merged 6 commits into01-edu:masterfrom Aug 31, 2025
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…ng data ### Fix ambiguous instruction about row indexing #### What changed The sentence: > "Load the data using genfromtxt, specifying the delimiter as ';', and optimize the numpy array size by reducing the data types. Use np.float32 and verify that the resulting numpy array weighs 76800 bytes." was unclear about whether the CSV header should be skipped or not. It now says: > "Load the data using genfromtxt, specifying the delimiter as ';' with excluding the headers, and optimize the numpy array size by reducing the data types. Use np.float32 and verify that the resulting numpy array weighs 76800 bytes." #### Why Later instructions refer to specific row numbers (like the 2nd, 7th, and 12th rows). Without stating whether the header counts as a row, the meaning is ambiguous — it affects the indexing. This fix makes it clear that the header should be excluded. #### No code changes This is a documentation fix only.
[EXTERNAL] fix(numpy): clarify that CSV header is excluded when loading data
…ss in Exercise 4 ## Summary Docs-only fix in **Keras 2 → Exercise 4 (Multi-class classification – Optimize)**: Replace the incorrect mention of `binary_crossentropy` with the correct `categorical_crossentropy` for **single-label multi-class** classification. ## Why `binary_crossentropy` is intended for **binary** or **multi-label** tasks. For **single-label multi-class** with a softmax output, the appropriate loss is **`categorical_crossentropy`**. This correction prevents learner confusion and aligns the subject with standard Keras practice. ## Scope of Change * **Updated text** in Exercise 4 to say `categorical_crossentropy` instead of `binary_crossentropy`.
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Summary
Docs-only fix in Keras 2 → Exercise 4 (Multi-class classification – Optimize):
Replace the incorrect mention of
binary_crossentropywith the correctcategorical_crossentropyfor single-label multi-class classification.Why
binary_crossentropyis intended for binary or multi-label tasks.For single-label multi-class with a softmax output, the appropriate loss is
categorical_crossentropy. This correction prevents learner confusion and aligns the subject with standard Keras practice.Scope of Change
categorical_crossentropyinstead ofbinary_crossentropy.