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  • 利用torch.nn实现前馈神经网络解决 二分类 任务

    1导入实验所需要的包

    import torch
    import numpy as np
    import random
    from IPython import display
    from matplotlib import pyplot as plt
    from torch import nn
    import torch.utils.data as Data
    import torch.optim as optim
    from torch.nn import init
    import os
    os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE"

    2自定义数据

    num_inputs = 200
    x1 = torch.normal(2,1,(10000, num_inputs))
    y1 = torch.ones(10000,1) # 标签1 
    x1_train = x1[:7000]
    x1_test = x1[7000:]
    x2 = torch.normal(-2,1,(10000, num_inputs))
    y2 = torch.zeros(10000,1) # 标签0
    x2_train = x2[:7000]
    x2_test = x2[7000:]
    # 注意 x, y 数据的数据形式一定要像下面一样 (torch.cat 是合并数据)---按行合并
    trainfeatures = torch.cat((x1_train,x2_train), 0).type(torch.FloatTensor)  #[14000, 200]
    trainlabels = torch.cat((y1[:7000], y2[:7000]), 0).type(torch.FloatTensor) #[14000, 1]
    testfeatures = torch.cat((x1_test,x2_test), 0).type(torch.FloatTensor) #[6000, 200]
    testlabels = torch.cat((y1[7000:], y2[7000:]), 0).type(torch.FloatTensor) #[6000, 1]

    3 读取数据

    batch_size = 50
    dataset = Data.TensorDataset(trainfeatures, trainlabels)
    train_iter = Data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True,  num_workers=0 )

    4 模型定义和参数初始化

    #模型定义和参数初始化
    num_hiddens,num_outputs = 256,1
    net = nn.Sequential(
            nn.Linear(num_inputs,num_hiddens),
            nn.ReLU(),
            nn.Linear(num_hiddens,num_outputs)
            )
    for params in net.parameters():
        init.normal_(params,mean=0,std=0.01)

    5 定义交叉熵损失函数和优化器

    lr = 0.0005
    num_epochs = 100
    loss_fn = torch.nn.BCEWithLogitsLoss()
    optimizer = torch.optim.SGD(net.parameters(),lr)

    6 定义模型训练函数

    #定义模型训练函数
    def train(net,train_iter,loss_fn,num_epochs,batch_size,params=None,lr=None,optimizer=None):
        train_ls = []
        test_ls = []
        for epoch in range(num_epochs):
            train_l_sum, train_acc_num,n = 0.0,0.0,0
            for X, y in train_iter:
                y_hat = net(X)
                loss = loss_fn(y_hat, y) 
                optimizer.zero_grad()
                loss.backward() 
                optimizer.step()
                train_l_sum += loss.item()*y.shape[0]
                n+= y.shape[0]
            train_labels = trainlabels.view(-1,1)
            test_labels = testlabels.view(-1,1)
            train_ls.append(train_l_sum/n)
            test_ls.append(loss_fn(net(testfeatures),test_labels).item()*testfeatures.shape[0])
    print('epoch %d,train_loss %.6f,test_loss %f'%(epoch+1,train_ls[epoch],test_ls[epoch]))
        return train_ls,test_ls

    7 开始训练模型

    train_loss,test_loss = train(net,train_iter,loss_fn,num_epochs,batch_size,net.parameters,lr,optimizer)

    因上求缘,果上努力~~~~ 作者:每天卷学习,转载请注明原文链接:https://www.cnblogs.com/BlairGrowing/p/15510668.html

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  • 原文地址:https://www.cnblogs.com/BlairGrowing/p/15510668.html
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