本文主要介绍如何使用Pytorch进行神经网络的搭建，从而完成分类的任务。
原始待分类的数据如下
原始分类数据
我们采用批训练的方式，共训练10代，每一批次为30个数据。这次我们没有采用继承的方式来搭建网络，而直接使用torch.nn.Sequential来搭建网络。
代码如下：
<code>import torchimport torch.nn.functional as Fimport torch.utils.data as Dataimport matplotlib.pyplot as plt# 假数据n_data = torch.ones(200, 2) # 数据的基本形态x0 = torch.normal(3*n_data, 1) # 类型0 x data (tensor), shape=(100, 2)y0 = torch.zeros(200) # 类型0 y data (tensor), shape=(100, )x1 = torch.normal(1*n_data, 1) # 类型1 x data (tensor), shape=(100, 1)y1 = torch.ones(200) # 类型1 y data (tensor), shape=(100, )# 注意 x, y 数据的数据形式是一定要像下面一样 (torch.cat 是在合并数据)x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # FloatTensor = 32-bit floatingy = torch.cat((y0, y1), ).type(torch.LongTensor) # LongTensor = 64-bit integernet=torch.nn.Sequential( torch.nn.Linear(2, 10), torch.nn.ReLU(), torch.nn.Linear(10, 2) )BATCH_SIZE=30torch_dataset = Data.TensorDataset(x, y)loader = Data.DataLoader( dataset=torch_dataset, # torch TensorDataset format batch_size=BATCH_SIZE, # mini batch size shuffle=True, # random shuffle for training)optimizer = torch.optim.SGD(net.parameters(), lr=0.02)loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hottedplt.ion() # something about plottingfor epoch in range(10): # train entire dataset 3 times for step, (batch_x, batch_y) in enumerate(loader): # for each training step out = net(batch_x) # input x and predict based on x loss = loss_func(out, batch_y) # must be (1. nn output, 2. target), the target label is NOT one-hotted optimizer.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradient # plot and show learning process plt.cla() out=net(x) prediction = torch.max(out, 1)[1] pred_y = prediction.data.numpy() target_y = y.data.numpy() plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn') accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size) plt.text(1.5, 3, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'}) plt.pause(0.1)plt.ioff()plt.show()/<code>
使用单层网络的训练结果：
单层神经网络
双层神经网络
可以看到拟合这样简单的分类任务，网络层数对预测的结果影响不大。但是当我们进行多特征分类任务时，就需要增加网络层数。