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pytorch實現(xiàn)CNN卷積神經(jīng)網(wǎng)絡(luò)

更新時間：2021年05月28日 17:02:11 作者：小山愛學(xué)習(xí)

這篇文章主要為大家詳細(xì)介紹了pytorch實現(xiàn)CNN卷積神經(jīng)網(wǎng)絡(luò)，文中示例代碼介紹的非常詳細(xì)，具有一定的參考價值，感興趣的小伙伴們可以參考一下

本文為大家講解了pytorch實現(xiàn)CNN卷積神經(jīng)網(wǎng)絡(luò)，供大家參考，具體內(nèi)容如下

我對卷積神經(jīng)網(wǎng)絡(luò)的一些認(rèn)識

卷積神經(jīng)網(wǎng)絡(luò)是時下最為流行的一種深度學(xué)習(xí)網(wǎng)絡(luò)，由于其具有局部感受野等特性，讓其與人眼識別圖像具有相似性，因此被廣泛應(yīng)用于圖像識別中，本人是研究機械故障診斷方面的，一般利用旋轉(zhuǎn)機械的振動信號作為數(shù)據(jù)。

對一維信號，通常采取的方法有兩種，第一，直接對其做一維卷積，第二，反映到時頻圖像上，這就變成了圖像識別，此前一直都在利用keras搭建網(wǎng)絡(luò)，最近學(xué)了pytroch搭建cnn的方法，進(jìn)行一下代碼的嘗試。所用數(shù)據(jù)為經(jīng)典的minist手寫字體數(shù)據(jù)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(wǎng)上下載數(shù)據(jù)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.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 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, 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內(nèi)容，希望對大家的學(xué)習(xí)有所幫助，也希望大家多多支持腳本之家。

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