卷积神经网络CNN
import torch import torch.nn as nn from torch.autograd import Variable import matplotlib.pyplot as plt import torch.utils.data as Data import torchvision # 下载MNIST数据集 # 若已有该数据集，需改为DOWNLOAD_MNIST = False DOWNLOAD_MNIST = True train_data = torchvision.datasets.MNIST( root='./mnist', train=True, transform=torchvision.transforms.ToTensor(), download = DOWNLOAD_MNIST ) test_data = torchvision.datasets.MNIST(root='./mnist/',train=False) with torch.no_grad(): test_x = Variable(torch.unsqueeze(test_data.data, dim=1)).type(torch.FloatTensor).cuda() test_y = test_data.targets[:10000].cuda() # 配置批处理 train_loader = Data.DataLoader( dataset=train_data, batch_size=50, shuffle=True, num_workers=3 ) # CNN网络 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() # 第一个卷积层 self.conv1 = nn.Sequential( nn.Conv2d(# 原图(1, 28, 28) in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2 # (kernel_size-1)/2 ),# (1, 28, 28) -> (16, 28, 28) nn.ReLU(), nn.MaxPool2d(kernel_size=2) # (16, 28, 28) -> (16, 14, 14) ) # 第二个卷积层 self.conv2 = nn.Sequential( nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2) )# (16, 14, 14) -> (32, 7, 7) self.out = nn.Linear(32 * 7 * 7, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) output = self.out(x) return output cnn = CNN().cuda() # 配置网络优化器 optimizer = torch.optim.Adam(cnn.parameters(), lr = 0.001) loss_func = nn.CrossEntropyLoss() plt.ion() plt.show() plt.figure() steplist = [] losslist = [] accuracylist = [] for epoch in range(1): for step, (x, y) in enumerate(train_loader): b_x = Variable(x).cuda() b_y = Variable(y).cuda() output = cnn(b_x) loss = loss_func(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 10 == 0: test_output = cnn(test_x[:10000]) pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze() accuracy = sum(pred_y == test_y.cpu().numpy()) / test_y.size(0) print('Epoch:', epoch, ' | train loss:%.4f'%loss.item(), ' | test accuracy:%.2f'%accuracy) steplist.append(step) losslist.append(loss.item()) accuracylist.append(accuracy) plt.subplot(121) plt.title('loss') plt.plot(steplist, losslist, 'r-', lw=1) plt.subplot(122) plt.title('accuracy') plt.plot(steplist, accuracylist, 'b-', lw=1) plt.pause(0.1) plt.ioff() plt.show()
结果：
Epoch: 0 | train loss:1.4696 | test accuracy:0.69 Epoch: 0 | train loss:0.7815 | test accuracy:0.80 Epoch: 0 | train loss:0.5405 | test accuracy:0.79 ... Epoch: 0 | train loss:0.0603 | test accuracy:0.98 Epoch: 0 | train loss:0.0451 | test accuracy:0.98 Epoch: 0 | train loss:0.0594 | test accuracy:0.98
可以看到，CNN对MNIST数据集的预测能达到98%的准确率，这是一个相当好的结果
整个预测的可视化过程：
循环神经网络RNN
import torch import torch.nn as nn from torch.autograd import Variable import matplotlib.pyplot as plt import torch.utils.data as Data import torchvision DOWNLOAD_MNIST = True train_data = torchvision.datasets.MNIST( root='./mnist', train=True, transform=torchvision.transforms.ToTensor(), download = DOWNLOAD_MNIST ) train_loader = Data.DataLoader( dataset=train_data, batch_size=64, shuffle=True, ) test_data = torchvision.datasets.MNIST(root='./mnist/',train=False) with torch.no_grad(): test_x = (torch.unsqueeze(test_data.data, dim=1).type(torch.FloatTensor)/255.).cuda() test_y = test_data.targets[:10000].cuda() class RNN(nn.Module): def __init__(self): super(RNN, self).__init__() self.rnn = nn.LSTM( input_size=28, hidden_size=64, num_layers=1, batch_first=True ) self.out = nn.Linear(64, 10) def forward(self, x): r_out, (h_n, h_c) = self.rnn(x, None) out = self.out(r_out[:, -1, :]) # (batch, time_step[-1], input_size) return out rnn = RNN().cuda() optimizer = torch.optim.Adam(rnn.parameters(), lr = 0.001) loss_func = nn.CrossEntropyLoss() plt.ion() plt.show() plt.figure() steplist = [] losslist = [] accuracylist = [] for epoch in range(1): for step, (x, y) in enumerate(train_loader): b_x = Variable(x.view(-1, 28, 28)).cuda() b_y = Variable(y).cuda() output = rnn(b_x) loss = loss_func(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 10 == 0: test_output = rnn(test_x[:10000].view(-1, 28, 28)) pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze() accuracy = sum(pred_y == test_y.cpu().numpy()) / test_y.size(0) print('Epoch:', epoch, ' | train loss:%.4f'%loss.item(), ' | test accuracy:%.2f'%accuracy) steplist.append(step) losslist.append(loss.item()) accuracylist.append(accuracy) plt.subplot(121) plt.title('loss') plt.plot(steplist, losslist, 'r-', lw=1) plt.subplot(122) plt.title('accuracy') plt.plot(steplist, accuracylist, 'b-', lw=1) plt.pause(0.1) plt.ioff() plt.show() test_output = rnn(test_x[:10]) pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze() print(pred_y, 'prediction number') print(test_y[:10].cpu().numpy(), 'real number')
结果：
Epoch: 0 | train loss:2.2996 | test accuracy:0.13 Epoch: 0 | train loss:2.2977 | test accuracy:0.15 Epoch: 0 | train loss:2.2768 | test accuracy:0.21 ... Epoch: 0 | train loss:0.1875 | test accuracy:0.94 Epoch: 0 | train loss:0.2653 | test accuracy:0.94 Epoch: 0 | train loss:0.3055 | test accuracy:0.94
