PyTorch一小时掌握之autograd机制篇

目录

• 概述
• 代码实现
• 手动定义求导
• 计算流量
• 反向传播计算
• 线性回归
• 导包
• 构造 x, y
• 构造模型
• 参数 & 损失函数
• 训练模型
• 完整代码

概述

PyTorch 干的最厉害的一件事情就是帮我们把反向传播全部计算好了.

代码实现

手动定义求导

import torch

# 方法一
x = torch.randn(3, 4, requires_grad=True)

# 方法二
x = torch.randn(3,4)
x.requires_grad = True

b = torch.randn(3, 4, requires_grad=True)
t = x + b
y = t.sum()

print(y)
print(y.backward())
print(b.grad)

print(x.requires_grad)
print(b.requires_grad)
print(t.requires_grad)

输出结果:

tensor(1.1532, grad_fn=<SumBackward0>)

None

tensor([[1., 1., 1., 1.],

[1., 1., 1., 1.],

[1., 1., 1., 1.]])

True

True

True

计算流量

# 计算流量
x = torch.rand(1)
w = torch.rand(1, requires_grad=True)
b = torch.rand(1, requires_grad=True)
y = w * x
z http://www.cppcns.com= y + b

print(x.requires_grad, w.requires_grad,b.requires_grad, z.requires_grad)
print(x.is_leaf, w.is_leaf, b.is_leaf, y.is_leaf,z.is_leaf)

输出结果:

False True True True

True True True False False

反向传播计算

# 反向传播
z.backward(retain_graph= True)  # 如果不清空会累加起来
print(w.grad)
print(b.grad)

输出结果:

tensor([0.1485])

tensor([1.])

线性回归

导包

import numpy as np
import torch
import torch.nn as nn

构造 x, y

# 构造数据
X_values = [i for i in range(11)]
X_train = np.array(X_values, dtype=np.float32)
X_train = X_train.reshape(-1, 1)
print(X_train.shape)  # (11, 1)

y_values = [2 * i + 1 for i in X_values]
y_train = np.array(y_values, dtype=np.float32)
y_train = y_train.reshape(-1,1)
print(y_train.shape)  # (11, 1)

输出结果:

(11, 1)

(11, 1)

构造模型

# 构造模型
class LinerRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinerRegressionModel, self).__init__()
        self.liner = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        out = self.liner(x)
        return out


input_dim = 1
output_dim = 1

model = LinerRegressionModel(input_dim, output_dim)
print(model)

输出结果:

LinerRegressionModel(

(liner): Linear(in_features=1, o编程客栈ut_features=1, bias=True)

)

参数 & 损失函数

# 超参数
enpochs = 1000
learning_rate = 0.01

# 损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

训练模型

# 训练模型
for epoch in range(enpochs):
    # 转成tensor
    inputs = torch.from_numpy(X_train)
    labels = torch.from_numpy(y_train)

    # 梯度每次迭代清零
    optimizer.zero_grad()

    # 前向传播
    outputs = model(inputs)

    # 计算损失
    loss = criterion(outputs, labels)

    # 反向传播
    loss.backward()

    # 更新参数
    optimizer.step()
    if epoch % 50 == 0:
        print("epoch {}, loss {}".format(epoch, loss.item()))

输出结果:

epoch 0, loss 114.47456359863281

epoch 50, loss 0.00021522105089388788

epoch 100, loss 0.00012275540211703628

epoch 150, loss 7.001651829341426e-05

epoch 200, loss 3.9934264350449666e-05

epoch 250, loss 2.2777328922529705e-05

epoch 300, loss 1.2990592040296178e-05

epoch 350, loss 7.409254521917319e-06

epoch 400, loss 4.227155841363128e-06

epoch 450, loss 2.410347860859474e-06

epoch 500, loss 1.3751249525739695e-06

epoch 550, loss 7.844975016269018e-07

epoch 600www.cppcns.com, loss 4.4756839656656666e-07

epoch 650, loss 2.5517596213830984e-07

epoch 700, loss 1.4577410922811396e-07

epoch 750, loss 8.30393886985803e-08

epoch 800, loss 4.747753479250605e-08

epoch 850, loss 2.709844615367274e-08

epoch 900, loss 1.5436164346738224e-08

epoch 950, loss 8.783858973515635e-09

完整代码

import numpy as np
import torch
import torch.nn as nn

# 构造数据
X_values = [i for i in range(11)]
X_train = np.array(X_values, dtype=np.float32)
X_train = X_train.reshape(-1, 1)
print(X_train.shape)  # (11, 1)

y_values = [2 * i + 1 for i in X_values]
y_train = np.array(y_values, dtype=np.float32)
y_train = y_train.reshape(-1,1)
print(y_train.shape)  # (11, 1)编程客栈

# 构造模型
class LinerRegressionModel(nn.Module):
    def __init__(self, input_dim, output_d编程客栈im):
        super(LinerRegressionModel, self).__init__()
        self.liner = nn.Linear(input_dim, output_dim)

    def forward(self, x):
        out = self.liner(x)
        return out


input_dim = 1
output_dim = 1

model = LinerRegressionModel(input_dim, output_dim)
print(model)

# 超参数
enpochs = 1000
learning_rate = 0.01

# 损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()

# 训练模型
for epoch in range(enpochs):
    # 转成tensor
    inputs = torch.from_numpy(X_train)
    labels = torch.from_numpy(y_train)

    # 梯度每次迭代清零
    optimizer.zero_grad()

    # 前向传播
    outputs = model(inputs)

    # 计算损失
    loss = criterion(outputs, labels)

    # 反向传播
    loss.backward()

    # 更新参数
    optimizer.step()
    if epoch % 50 == 0:
        print("epoch {}, loss {}".format(epoch, loss.item()))

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