PyTorch一小时掌握之autograd机制篇

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)

# 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 = 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)

# 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.])

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)

# 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, out_features=1, bias=True)
)

# 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()))

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)

# 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)

# 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()))