Comments (1)
as per the video the reason why your solution does not work is because the +
operator is being invoked on the integer value first, and since it cannot be found there, it looks for the __radd__
on the Value
object, which you have not defined.
__radd__
stands for reverse add
from micrograd.
Related Issues (20)
- Issue with zero_grad? HOT 5
- _backward as lambdas? HOT 3
- Sequential MLP implementation
- Zero_grad only zeros the weight and bias nodes, not the nodes for addition and multiplication HOT 1
- A tensor version for micrograd inpired by this work
- Rename engine.py to value.py
- Vectorized implementation with PyTorch flavor
- Vectorized modification with GPU support.
- Another MiniGrad with the RAdam optimizer.
- Ensure backward() is idempotent HOT 4
- Regarding the gradient update of the __sub__ operation HOT 1
- Reseting the grad of weights and biases is not enough HOT 1
- Adjusting parameters by sign and magnitude of gradient HOT 1
- Topological sort - bug HOT 3
- backward member implementation question HOT 1
- type annotation lacking/ maybe also add docstrings.
- For addition adding incrementing grading makes sense, I can't make sense out of the incrementing it for multiplication too, potential bug? HOT 1
- radd
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from micrograd.