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Updated equations of gradients and y_pred #3845

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12 changes: 6 additions & 6 deletions beginner_source/examples_tensor/polynomial_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@
just cast the Tensor to a cuda datatype.
"""

import torch
import math
import torch


dtype = torch.float
Expand All @@ -39,19 +39,19 @@
learning_rate = 1e-6
for t in range(2000):
# Forward pass: compute predicted y
y_pred = a + b * x + c * x ** 2 + d * x ** 3
y_pred = a + (b * x) + (c * x.pow(2)) + (d * x.pow(3))

# Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
if t % 100 == 99:
print(t, loss)

# Backprop to compute gradients of a, b, c, d with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
grad_y_pred = -2.0 * (y - y_pred)
grad_a = grad_y_pred.sum()
grad_b = (grad_y_pred * x).sum()
grad_c = (grad_y_pred * x ** 2).sum()
grad_d = (grad_y_pred * x ** 3).sum()
grad_b = torch.matmul(input=grad_y_pred, other=x).sum()
grad_c = torch.matmul(input=grad_y_pred, other=x.pow(2)).sum()
grad_d = torch.matmul(input=grad_y_pred, other=x.pow(3)).sum()

# Update weights using gradient descent
a -= learning_rate * grad_a
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