一 查看原始函数
初始化
%matplotlib inline
import random
import torch
from d2l import torch as d2l1.1 生成原始数据
def synthetic_data(w, b, num_examples):
x = torch.normal(0, 1, (num_examples, len(w)))
y = torch.matmul(x, w) + b
print('x:', x)
print('y:', y)
y += torch.normal(0, 0.01, y.shape) # 噪声
return x, y.reshape((-1 , 1))true_w = torch.tensor([2.])
true_b = 4.2
print(f'true_w: {true_w}, true_b: {true_b}')
features, labels = synthetic_data(true_w, true_b, 10)
1.2 数据转换
def data_iter(batch_size, features, labels):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
for i in range(0, num_examples, batch_size):
batch_indices = torch.tensor(indices[i: min(i + batch_size, num_examples)])
yield features[batch_indices], labels[batch_indices]
batch_size = 10
for x, y in data_iter(batch_size, features, labels):
print(f'x: {x}, \ny: {y}')
1.3 初始化权重
随机初始化，w使用 均值0，方差 0.01 的随机值， b 初始化为1。
w = torch.normal(0, 0.01, size = (1,1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
w, b
二 执行训练
查看训练过程中的 参数变化：
print(f'true_w: {true_w}, true_b: {true_b}')
def squared_loss(y_hat, y):
return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2
def linreg(x, w, b):
return torch.matmul(x, w) + b
def sgd(params, lr, batch_size):
with torch.no_grad():
for param in params:
# print('param:', param, 'param.grad:', param.grad)
param -= lr * param.grad / batch_size
param.grad.zero_()
lr = 0.03
num_epochs = 1000
for epoch in range(num_epochs):
for x, y in data_iter(batch_size, features, labels):
l = squared_loss(linreg(x, w, b), y) # 计算总损失
print('w:', w, 'b:', b) # l:', l, '\n
l.sum().backward()
sgd([w, b], lr, batch_size)
三 测试梯度更新
初始化数据
%matplotlib inline
import random
import torch
from d2l import torch as d2l
def synthetic_data(w, b, num_examples):
x = torch.normal(0, 1, (num_examples, len(w)))
y = torch.matmul(x, w) + b
print('x:', x)
print('y:', y)
y += torch.normal(0, 0.01, y.shape) # 噪声
return x, y.reshape((-1 , 1))
true_w = torch.tensor([2.])
true_b = 4.2
print(f'true_w: {true_w}, true_b: {true_b}')
features, labels = synthetic_data(true_w, true_b, 10)
def data_iter(batch_size, features, labels):
num_examples = len(features)
indices = list(range(num_examples))
random.shuffle(indices)
for i in range(0, num_examples, batch_size):
batch_indices = torch.tensor(indices[i: min(i + batch_size, num_examples)])
yield features[batch_indices], labels[batch_indices]
batch_size = 10
for x, y in data_iter(batch_size, features, labels):
print(f'x: {x}, \ny: {y}')
w = torch.normal(0, 0.01, size = (1,1), requires_grad=True)
b = torch.zeros(1, requires_grad=True)
w, b3.1 测试更新
print(f'true_w: {true_w}, true_b: {true_b}')
def squared_loss(y_hat, y):
return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2
def linreg(x, w, b):
return torch.matmul(x, w) + b
def sgd(params, lr, batch_size):
with torch.no_grad():
for param in params:
print('param:', param, 'param.grad:', param.grad)
# param -= lr * param.grad / batch_size
# param.grad.zero_()
lr = 0.03
num_epochs = 2
for epoch in range(num_epochs):
for x, y in data_iter(batch_size, features, labels):
l = squared_loss(linreg(x, w, b), y) # 计算总损失
print(f'\nepoch: {epoch},w:', w, 'b:', b) # l:', l, '\n
l.sum().backward() # 计算更新梯度
sgd([w, b], lr, batch_size)使用 l.sum().backward() # 计算更新梯度：
不使用更新时：
print(f'true_w: {true_w}, true_b: {true_b}')
def squared_loss(y_hat, y):
return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2
def linreg(x, w, b):
return torch.matmul(x, w) + b
def sgd(params, lr, batch_size):
with torch.no_grad():
for param in params:
print('param:', param, 'param.grad:', param.grad)
# param -= lr * param.grad / batch_size
# param.grad.zero_()
lr = 0.03
num_epochs = 2
for epoch in range(num_epochs):
for x, y in data_iter(batch_size, features, labels):
l = squared_loss(linreg(x, w, b), y) # 计算总损失
print(f'\nepoch: {epoch},w:', w, 'b:', b) # l:', l, '\n
# l.sum().backward() # 计算更新梯度
sgd([w, b], lr, batch_size)
# break
