PyTorch一小时掌握之autograd机制篇 PyTorch一小时掌握之autograd机制

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 + by = 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=)
None
tensor([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]])
True
True
True

文章图片

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 * xz = y + bprint(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 npimport torchimport 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 outinput_dim = 1output_dim = 1model = LinerRegressionModel(input_dim, output_dim)print(model)

输出结果:
LinerRegressionModel(
(liner): Linear(in_features=1, out_features=1, bias=True)
)

参数 & 损失函数

# 超参数enpochs = 1000learning_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tensorinputs = 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 600, 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 npimport torchimport 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)# 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 outinput_dim = 1output_dim = 1model = LinerRegressionModel(input_dim, output_dim)print(model)# 超参数enpochs = 1000learning_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tensorinputs = 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()))

【PyTorch一小时掌握之autograd机制篇】到此这篇关于PyTorch一小时掌握之autograd机制篇的文章就介绍到这了,更多相关PyTorch autograd内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！