是先做网站还是先备案,常用网站域名,做优惠券网站要多少钱,wordpress Nirvanatorch.nn是专门为神经网络设计的模块化接口。nn构建于 Autograd之上#xff0c;可用来定义和运行神经网络。 nn.functional#xff0c;这个包中包含了神经网络中使用的一些常用函数#xff0c;这些函数的特点是#xff0c;不具有可学习的参数(如ReLU#xff0c;pool#…torch.nn是专门为神经网络设计的模块化接口。nn构建于 Autograd之上可用来定义和运行神经网络。 nn.functional这个包中包含了神经网络中使用的一些常用函数这些函数的特点是不具有可学习的参数(如ReLUpoolDropOut等)这些函数可以放在构造函数中也可以不放但是这里建议不放。
定义一个网络
PyTorch中已经为我们准备好了现成的网络模型只要继承nn.Module并实现它的forward方法PyTorch会根据autograd自动实现backward函数。
import torch
import torch.nn as nn
import torch.nn.functional as Fclass MLP(nn.Module):def __init__(self, user_num, user_dim, layers[32, 16, 8]):super(MLP, self).__init__() # 子类函数必须在构造函数中执行父类构造函数self.user_Embedding nn.Embedding(user_num, user_dim)self.mlp nn.Sequential() for id in range(1, len(layers)): # 这样可以实现MLP层数和每层神经单元数的自动调整self.mlp.add_module(Linear_layer_%d % id, nn.Linear(layers[id - 1], layers[id]))self.mlp.add_module(Relu_layer_%d % id, nn.ReLU(inplaceTrue))self.predict nn.Sequential(nn.Linear(layers[-1], 1),nn.Sigmoid(),)def forward(self, x):user self.user_Embedding(x)user self.mlp(user)score self.predict(user)return scoremodel MLP(1000, 64)
print(model)
MLP((user_Embedding): Embedding(1000, 64)(mlp): Sequential((Linear_layer_1): Linear(in_features32, out_features16, biasTrue)(Relu_layer_1): ReLU(inplaceTrue)(Linear_layer_2): Linear(in_features16, out_features8, biasTrue)(Relu_layer_2): ReLU(inplaceTrue))(predict): Sequential((0): Linear(in_features8, out_features1, biasTrue)(1): Sigmoid())
)for parameters in model.parameters():print(parameters)
Parameter containing:
tensor([[ 0.4192, -1.0525, 1.4208, 0.5376, 2.1371, 0.7074, 0.1017, 0.9701, 1.2824, -0.0436],[-0.6374, 0.0153, -0.1862, -0.6061, 0.5522, -1.1526, 0.3913, 0.3103,-0.1055, 0.6098],[-0.0367, -0.9573, -0.5106, -1.2440, 1.2201, -0.5424, 0.2045, 0.2208,-0.7557, -0.7811],[ 0.5457, 0.3586, 0.9871, -0.2117, 1.0885, 1.7162, -0.2125, 0.2652,-0.3262, 0.3047],[ 0.1039, 0.8132, 0.6638, 0.2618, 0.8552, 0.8300, 0.2349, 1.8830,-0.5149, -1.0468]], requires_gradTrue)
Parameter containing:
tensor([[-0.2395, 0.1461, -0.0161, 0.0267, -0.0353, 0.2085, 0.0046, -0.1572],[ 0.2267, 0.0129, -0.3296, -0.2270, 0.2268, 0.1771, -0.0992, 0.2148],[ 0.1906, 0.1896, -0.2703, -0.3506, 0.0248, 0.1949, -0.3117, 0.0721],[-0.3197, 0.2782, -0.1553, 0.2509, 0.0279, 0.2040, -0.1478, 0.2943]],requires_gradTrue)
Parameter containing:
tensor([ 0.0808, -0.3252, -0.0015, -0.0666], requires_gradTrue)
Parameter containing:
tensor([[-0.3243, 0.4393, -0.2430, 0.4330]], requires_gradTrue)
Parameter containing:
tensor([-0.0739], requires_gradTrue)for name,parameters in model.named_parameters():print(name,:,parameters.size())
user_Embedding.weight : torch.Size([5, 10])
mlp.Linear_layer_1.weight : torch.Size([4, 8])
mlp.Linear_layer_1.bias : torch.Size([4])
predict.0.weight : torch.Size([1, 4])
predict.0.bias : torch.Size([1])
