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NumPy热身

原文:https://pytorch.org/tutorials/beginner/examples_tensor/polynomial_numpy.html#sphx-glr-beginner-examples-tensor-polynomial-numpy-py

校对:DrDavidS

这里我们准备一个三阶多项式,通过最小化平方欧几里得距离来训练,并预测函数 y = sin(x)-pipi上的值。

在本实现中,我们使用 numpy 手动实现前向传播,损失(loss)和反向传播。

numpy 数组是一种通用的 n 维数组;它跟深度学习,梯度或计算图没啥关系,只是执行通用数值计算的一种方法。

import numpy as np
import math

# Create random input and output data
x = np.linspace(-math.pi, math.pi, 2000)
y = np.sin(x)

# Randomly initialize weights
a = np.random.randn()
b = np.random.randn()
c = np.random.randn()
d = np.random.randn()

learning_rate = 1e-6
for t in range(2000):
# Forward pass: compute predicted y
# y = a + b x + c x^2 + d x^3
y_pred = a + b * x + c * x ** 2 + d * x ** 3

# Compute and print loss
loss = np.square(y_pred - y).sum()
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_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()

# Update weights
a -= learning_rate * grad_a
b -= learning_rate * grad_b
c -= learning_rate * grad_c
d -= learning_rate * grad_d

print(f'Result: y = {a} + {b} x + {c} x^2 + {d} x^3')

脚本的总运行时间:(0 分钟 0.000 秒)

下载 Python 源码:polynomial_numpy.py

下载 Jupyter 笔记本:polynomial_numpy.ipynb