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()

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脚本之家。