7.示例：单层神经网络实现
本节使用神经网络在iris数据集的多分类示例来说明神经网络的整个流程。一般的神经网络训练包括几个重要的步骤：数据准备，初始化权重，激活函数，前向计算，损失函数，计算损失，反向传播，更新参数，直到收敛或者达到终止条件。
import torch
from torch import sigmoid
import torch.nn.functional as F
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from torch.autograd import Variable
from torch.optim import SGDN_FEATURE = 4
N_HIDDEN = 5
N_OUTPUT = 4
N_ITERS = 1000
LR = 0.5# 判断GPU是否可用
use_cuda = torch.cuda.is_available()
print("use_cuda: ",use_cuda)# 加载数据集
iris = load_iris()
print(iris.keys())# 数据预处理
x = iris['data']
y = iris['target']
print('x.shape: ',x.shape)
print('y.shape: ',y.shape)
print(y)
x = torch.FloatTensor(x)
y = torch.LongTensor(y)
x = Variable(x)
y = Variable(y)# 定义神经网络类，继承自torch.nn.Module
class Net(torch.nn.Module):# 定义构造函数def __init__(self,n_feature, n_hidden, n_output):super(Net,self).__init__()# 定义隐含层self.hidden = torch.nn.Linear(n_feature,n_hidden)# 定义输出层self.predict = torch.nn.Linear(n_hidden,n_output)# 定义前向传播def forward(self,x):# 计算隐含层，激活函数为sigmoidx = sigmoid(self.hidden(x))# 计算输出层，激活函数为log_softmax（多分类）out = F.log_softmax(self.predict(x),dim=1)return out# 定义神经网络实例
net = Net(n_feature=N_FEATURE,n_hidden=N_HIDDEN,n_output=N_OUTPUT)
print(net)# 如果GPU可用，把数据和模型都转到GPU上计算；CPU时调用.cpu()即可
if use_cuda:x = x.cuda()y = y.cuda()net = net.cuda()# 定义神经网络优化器：这里使用随机梯度下降SGD，学习率lr=0.5
optimizer = SGD(net.parameters(),lr=LR)# 开始训练神经网络
px,py = [],[]for i in range(N_ITERS):# 数据集传入网络前向计算预测值prediction = net(x)# 计算损失loss = F.nll_loss(prediction,y)# 清除网络状态optimizer.zero_grad()# 误差反向传播loss.backward()# 更新参数optimizer.step()# 打印并记录当前的index和lossprint(i," loss: ",loss.item())px.append(i)py.append(loss.item())plt.figure(figsize=(6,4),dpi=144)
plt.plot(px,py,'r-',lw=1)
plt.yticks([x * 0.1 for x in range(16)])
plt.show()
输出结果
use_cuda: True
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])
x.shape: (150, 4)
y.shape: (150,)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 22 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 2]
Net((hidden): Linear(in_features=4, out_features=5, bias=True)(predict): Linear(in_features=5, out_features=4, bias=True)
)
0 loss: 1.3780320882797241
1 loss: 1.244009017944336
2 loss: 1.1953575611114502
3 loss: 1.1671373844146729
4 loss: 1.147063136100769
5 loss: 1.1312648057937622
6 loss: 1.1179096698760986
7 loss: 1.10591721534729
8 loss: 1.0945632457733154
9 loss: 1.0833168029785156
10 loss: 1.0717663764953613
……
990 loss: 0.08581560850143433
991 loss: 0.0774485245347023
992 loss: 0.08574181795120239
993 loss: 0.07738661766052246
994 loss: 0.08566807955503464
995 loss: 0.07732491940259933
996 loss: 0.08559456467628479
997 loss: 0.07726352661848068
998 loss: 0.08552153408527374
999 loss: 0.0772024393081665
作者：金字塔下的小蜗牛
链接：https://www.jianshu.com/p/c2436d151744
来源：简书
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