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fix description of equation in polynomial_autograd.py example #3897

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fix description of equation in polynomial_autograd.py example #3897

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673a1dd
fix description of equation in polynomial_autograd.py example
telamonian May 13, 2026
b6df7d2
Removed unused lines of code
telamonian May 16, 2026
852040a
fix typo
telamonian May 16, 2026
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8 changes: 3 additions & 5 deletions beginner_source/examples_autograd/polynomial_autograd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@
PyTorch: Tensors and autograd
-------------------------------

A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
to :math:`\pi` by minimizing squared Euclidean distance.
A third order polynomial, trained to predict :math:`y=e^x` from :math:`-1`
to :math:`1` by minimizing squared Euclidean distance.

This implementation computes the forward pass using operations on PyTorch
Tensors, and uses PyTorch autograd to compute gradients.
Expand All @@ -14,7 +14,6 @@
holding the gradient of ``x`` with respect to some scalar value.
"""
import torch
import math

# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
Expand All @@ -39,7 +38,6 @@
c = torch.randn((), dtype=dtype, requires_grad=True)
d = torch.randn((), dtype=dtype, requires_grad=True)

initial_loss = 1.
learning_rate = 1e-5
for t in range(5000):
# Forward pass: compute predicted y using operations on Tensors.
Expand All @@ -50,7 +48,7 @@
# loss.item() gets the scalar value held in the loss.
loss = (y_pred - y).pow(2).sum()

# Calculare initial loss, so we can report loss relative to it
# Calculate initial loss, so we can report loss relative to it
if t==0:
initial_loss=loss.item()

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