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layer return Value instead of a list so a single neuron in the hidden layer pass a value while the Neuron expected a list[Value] (line 21)
IgorTavcar
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Mar 5, 2026
- Simplify backward pass: replace _backward closures with _local_grads tuples (from karpathy#115) - Zero grads before backward for idempotent backward() calls (from karpathy#102) - Add exp, log, tanh, softmax to Value class - Add transformer components: Linear, Embedding, LayerNorm, Attention, MultiHeadAttention, FeedForward, TransformerBlock, Transformer, cross_entropy - Move single-output unwrapping from Layer to MLP (from karpathy#111) - Add input shape assertion in Neuron (from karpathy#107) - Add MLP test (from karpathy#111) - Expand .gitignore with standard Python patterns Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Add simple test case for MLP to improve reasoning about the implementation
Fix single neuron output handling in MLP implementation
Expand .gitignore with standard Python patterns from GitHub repo.
Changes
Testing improvements:
test/test_nn.pywith a simple MLP test that validates both forward and backward passes with known expected values[This helped me resonate a bit better about the code implementation -- thought it could help others.]
Bug fix:
Layer.__call__()toMLP.__call__()[I believe layer(x) should always return a list[Value] based on the expected inout in Neuron activation computation (zip(w, v)).]
Housekeeping: