本文為大家講解了pytorch實現CNN卷積神經網路，供大家參考，具體內容如下

我對卷積神經網路的一些認識

卷積神經網路是時下最為流行的一種深度學習網路，由於其具有區域性感受野等特性，讓其與人眼識別影象具有相似性，因此被廣泛應用於影象識別中，本人是研究機械故障診斷方面的，一般利用旋轉機械的振動訊號作為資料。

對一維訊號，通常採取的方法有兩種，第一，直接對其做一維卷積，第二，反映到時頻影象上，這就變成了影象識別，此前一直都在利用keras搭建網路，最近學了pytroch搭建cnn的方法，進行一下程式碼的嘗試。所用資料為經典的minist手寫字型資料集

import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
`EPOCH = 1
BATCH_SIZE = 50
LR = 0.001
DOWNLOAD_MNIST = True

從網上下載資料集：

```python
train_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.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 cm
try: from sklearn.manifold import TSNE; HAS_SK = True
except: 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,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內容，希望對大家的學習有所幫助，也希望大家多多支援我們。