pytorch实现CNN卷积神经网络

  • Z时代
  • 2024-01-10
  • 分类：综合

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 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
从网上下载数据集：
```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()

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