宝安做网站的公司,wordpress母狗,网站设计客户对接流程,阿里云 域名 做网站度学习之pytorch实现线性回归 pytorch用到的函数torch.nn.Linearn()函数torch.nn.MSELoss()函数torch.optim.SGD() 代码实现结果分析 pytorch用到的函数
torch.nn.Linearn()函数
torch.nn.Linear(in_features, # 输入的神经元个数out_features, # 输出神经元个数biasTrue # 是… 度学习之pytorch实现线性回归 pytorch用到的函数torch.nn.Linearn()函数torch.nn.MSELoss()函数torch.optim.SGD() 代码实现结果分析 pytorch用到的函数
torch.nn.Linearn()函数
torch.nn.Linear(in_features, # 输入的神经元个数out_features, # 输出神经元个数biasTrue # 是否包含偏置)作用j进行线性变换 Linear(1, 1) : 表示一维输入一维输出 torch.nn.MSELoss()函数 torch.optim.SGD() 优化器对象 代码实现
import torchx_data torch.tensor([[1.0], [2.0], [3.0]]) # 将x_data设置为tensor类型数据
y_data torch.tensor([[2.0], [4.0], [6.0]])class LinearModel(torch.nn.Module):def __init__(self):super(LinearModel, self).__init__() # 继承父类self.linear torch.nn.Linear(1, 1)# 用torch.nn.Linear来构造对象 (y w * x b)def forward(self, x):y_pred self.linear(x) #调用之前的构造的对象(调用构造函数)计算 y w * x breturn y_predmodel LinearModel()criterion torch.nn.MSELoss(size_averageFalse) # 定义损失函数不求平均损失(为False)#优化器对象
# #model.parameters()会扫描module中的所有成员如果成员中有相应权重那么都会将结果加到要训练的参数集合上
# #类似权重的更新
optimizer torch.optim.SGD(model.parameters(), lr0.01) # 定义梯度优化器为随机梯度下降for epoch in range(10000): # 训练过程y_pred model(x_data) # 向前传播求y_predloss criterion(y_pred, y_data) # 根据y_pred和y_data求损失print(epoch, loss)# 记住在backward之前要先梯度归零optimizer.zero_grad() # 将优化器数值清零loss.backward() # 反向传播计算梯度optimizer.step() # 根据梯度更新参数#打印权重和b
print(w , model.linear.weight.item())
print(b , model.linear.bias.item())#检测模型
x_test torch.tensor([4.0])
y_test model(x_test)
print(y_pred , y_test.data) # 测试结果分析 9961 tensor(4.0927e-12, grad_fn) 9962 tensor(4.0927e-12, grad_fn) 9963 tensor(4.0927e-12, grad_fn) 9964 tensor(4.0927e-12, grad_fn) 9965 tensor(4.0927e-12, grad_fn) 9966 tensor(4.0927e-12, grad_fn) 9967 tensor(4.0927e-12, grad_fn) 9968 tensor(4.0927e-12, grad_fn) 9969 tensor(4.0927e-12, grad_fn) 9970 tensor(4.0927e-12, grad_fn) 9971 tensor(4.0927e-12, grad_fn) 9972 tensor(4.0927e-12, grad_fn) 9973 tensor(4.0927e-12, grad_fn) 9974 tensor(4.0927e-12, grad_fn) 9975 tensor(4.0927e-12, grad_fn) 9976 tensor(4.0927e-12, grad_fn) 9977 tensor(4.0927e-12, grad_fn) 9978 tensor(4.0927e-12, grad_fn) 9979 tensor(4.0927e-12, grad_fn) 9980 tensor(4.0927e-12, grad_fn) 9981 tensor(4.0927e-12, grad_fn) 9982 tensor(4.0927e-12, grad_fn) 9983 tensor(4.0927e-12, grad_fn) 9984 tensor(4.0927e-12, grad_fn) 9985 tensor(4.0927e-12, grad_fn) 9986 tensor(4.0927e-12, grad_fn) 9987 tensor(4.0927e-12, grad_fn) 9988 tensor(4.0927e-12, grad_fn) 9989 tensor(4.0927e-12, grad_fn) 9990 tensor(4.0927e-12, grad_fn) 9991 tensor(4.0927e-12, grad_fn) 9992 tensor(4.0927e-12, grad_fn) 9993 tensor(4.0927e-12, grad_fn) 9994 tensor(4.0927e-12, grad_fn) 9995 tensor(4.0927e-12, grad_fn) 9996 tensor(4.0927e-12, grad_fn) 9997 tensor(4.0927e-12, grad_fn) 9998 tensor(4.0927e-12, grad_fn) 9999 tensor(4.0927e-12, grad_fn) w 1.9999985694885254 b 2.979139480885351e-06 y_pred tensor([8.0000]) 因为轮数过多这里展示后面几轮 模型的准确性跟轮数的多少有关系 如果轮数为100最后测试结果的y_pred肯定不为8.00这里轮数为10000,预测结果跟实际结果基本一样 这里是轮数为100结果是 7点多有一定误差 0 tensor(101.4680, grad_fn) 1 tensor(45.8508, grad_fn) 2 tensor(21.0819, grad_fn) 3 tensor(10.0458, grad_fn) 4 tensor(5.1234, grad_fn) 5 tensor(2.9227, grad_fn) 6 tensor(1.9338, grad_fn) 7 tensor(1.4844, grad_fn) 8 tensor(1.2754, grad_fn) 9 tensor(1.1736, grad_fn) 10 tensor(1.1195, grad_fn) 11 tensor(1.0869, grad_fn) 12 tensor(1.0639, grad_fn) 13 tensor(1.0453, grad_fn) 14 tensor(1.0288, grad_fn) 15 tensor(1.0134, grad_fn) 16 tensor(0.9985, grad_fn) 17 tensor(0.9841, grad_fn) 18 tensor(0.9699, grad_fn) 19 tensor(0.9559, grad_fn) 20 tensor(0.9421, grad_fn) 21 tensor(0.9286, grad_fn) 22 tensor(0.9153, grad_fn) 23 tensor(0.9021, grad_fn) 24 tensor(0.8891, grad_fn) 25 tensor(0.8764, grad_fn) 26 tensor(0.8638, grad_fn) 27 tensor(0.8513, grad_fn) 28 tensor(0.8391, grad_fn) 29 tensor(0.8271, grad_fn) 30 tensor(0.8152, grad_fn) 31 tensor(0.8034, grad_fn) 32 tensor(0.7919, grad_fn) 33 tensor(0.7805, grad_fn) 34 tensor(0.7693, grad_fn) 35 tensor(0.7582, grad_fn) 36 tensor(0.7474, grad_fn) 37 tensor(0.7366, grad_fn) 38 tensor(0.7260, grad_fn) 39 tensor(0.7156, grad_fn) 40 tensor(0.7053, grad_fn) 41 tensor(0.6952, grad_fn) 42 tensor(0.6852, grad_fn) 43 tensor(0.6753, grad_fn) 44 tensor(0.6656, grad_fn) 45 tensor(0.6561, grad_fn) 46 tensor(0.6466, grad_fn) 47 tensor(0.6373, grad_fn) 48 tensor(0.6282, grad_fn) 49 tensor(0.6192, grad_fn) 50 tensor(0.6103, grad_fn) 51 tensor(0.6015, grad_fn) 52 tensor(0.5928, grad_fn) 53 tensor(0.5843, grad_fn) 54 tensor(0.5759, grad_fn) 55 tensor(0.5676, grad_fn) 56 tensor(0.5595, grad_fn) 57 tensor(0.5514, grad_fn) 58 tensor(0.5435, grad_fn) 59 tensor(0.5357, grad_fn) 60 tensor(0.5280, grad_fn) 61 tensor(0.5204, grad_fn) 62 tensor(0.5129, grad_fn) 63 tensor(0.5056, grad_fn) 64 tensor(0.4983, grad_fn) 65 tensor(0.4911, grad_fn) 66 tensor(0.4841, grad_fn) 67 tensor(0.4771, grad_fn) 68 tensor(0.4703, grad_fn) 69 tensor(0.4635, grad_fn) 70 tensor(0.4569, grad_fn) 71 tensor(0.4503, grad_fn) 72 tensor(0.4438, grad_fn) 73 tensor(0.4374, grad_fn) 74 tensor(0.4311, grad_fn) 75 tensor(0.4250, grad_fn) 76 tensor(0.4188, grad_fn) 77 tensor(0.4128, grad_fn) 78 tensor(0.4069, grad_fn) 79 tensor(0.4010, grad_fn) 80 tensor(0.3953, grad_fn) 81 tensor(0.3896, grad_fn) 82 tensor(0.3840, grad_fn) 83 tensor(0.3785, grad_fn) 84 tensor(0.3730, grad_fn) 85 tensor(0.3677, grad_fn) 86 tensor(0.3624, grad_fn) 87 tensor(0.3572, grad_fn) 88 tensor(0.3521, grad_fn) 89 tensor(0.3470, grad_fn) 90 tensor(0.3420, grad_fn) 91 tensor(0.3371, grad_fn) 92 tensor(0.3322, grad_fn) 93 tensor(0.3275, grad_fn) 94 tensor(0.3228, grad_fn) 95 tensor(0.3181, grad_fn) 96 tensor(0.3136, grad_fn) 97 tensor(0.3091, grad_fn) 98 tensor(0.3046, grad_fn) 99 tensor(0.3002, grad_fn) w 1.6352288722991943 b 0.8292105793952942 y_pred tensor([7.3701]) Process finished with exit code 0
