本文为大家讲解了pytorch实现CNN卷积神经网络，供大家参考，具体内容如下

我对卷积神经网络的一些认识

卷积神经网络是时下最为流行的一种深度学习网络，由于其具有局部感受野等特性，让其与人眼识别图像具有相似性，因此被广泛应用于图像识别中，本人是研究机械故障诊断方面的，一般利用旋转机械的振动信号作为数据。

对一维信号，通常采取的方法有两种，第一，直接对其做一维卷积，第二，反映到时频图像上，这就变成了图像识别，此前一直都在利用keras搭建网络，最近学了pytroch搭建cnn的方法，进行一下代码的尝试。所用数据为经典的minist手写字体数据集

import torchimport torch.nn as nnimport torch.utils.data as Dataimport torchvisionimport matplotlib.pyplot as plt`EPOCH = 1BATCH_SIZE = 50LR = 0.001DOWNLOAD_MNIST = True从网上下载数据集：```pythontrain_data = torchvision.datasets.MNIST( root="./mnist/", train = True, transform=torchvision.transforms.ToTensor(), download = DOWNLOAD_MNIST,)print(train_data.train_data.size())print(train_data.train_labels.size())```plt.imshow(train_data.train_data[0].numpy(), cmap='autumn')plt.title("%i" % train_data.train_labels[0])plt.show()train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)test_data = torchvision.datasets.MNIST(root="./mnist/", train=False)test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255.test_y = test_data.test_labels[:2000]class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d( in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2, ), nn.ReLU(), nn.MaxPool2d(kernel_size=2), ) self.conv2 = nn.Sequential( nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2), ) self.out = nn.Linear(32*7*7, 10) # fully connected layer, output 10 classes def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = x.view(x.size(0), -1) # flatten the output of conv2 to (batch_size, 32*7*7) output = self.out(x) return output optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)loss_func = nn.CrossEntropyLoss() from matplotlib import cmtry: from sklearn.manifold import TSNE; HAS_SK = Trueexcept: HAS_SK = False; print('Please install sklearn for layer visualization')def plot_with_labels(lowDWeights, labels): plt.cla() X, Y = lowDWeights[:, 0], lowDWeights[:, 1] for x, y, s in zip(X, Y, labels): c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9) plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)plt.ion()for epoch in range(EPOCH): for step, (b_x, b_y) in enumerate(train_loader): output = cnn(b_x) loss = loss_func(output, b_y) optimizer.zero_grad() loss.backward() optimizer.step() if step % 50 == 0: test_output = cnn(test_x) pred_y = torch.max(test_output, 1)[1].data.numpy() accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0)) print("Epoch: ", epoch, "| train loss: %.4f" % loss.data.numpy(), "| test accuracy: %.2f" % accuracy) plt.ioff()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。