【天池—街景字符编码识别】Task3 字符识别模型

本文主要是介绍【天池—街景字符编码识别】Task3 字符识别模型，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

CNN介绍

卷积神经网络（简称CNN）是一类特殊的人工神经网络，是深度学习中重要的一个分支。CNN在很多领域都表现优异，精度和速度比传统计算学习算法高很多。特别是在计算机视觉领域，CNN是解决图像分类、图像检索、物体检测和语义分割的主流模型。
CNN每一层由众多的卷积核组成，每个卷积核对输入的像素进行卷积操作，得到下一次的输入。随着网络层的增加卷积核会逐渐扩大感受野，并缩减图像的尺寸。
CNN是一种层次模型，输入的是原始的像素数据。CNN通过卷积（convolution）、池化（pooling）、非线性激活函数（non-linear activation function）和全连接层（fully connected layer）构成。

Pytorch构建CNN模型

在Pytorch中构建CNN模型非常简单，只需要定义好模型的参数和正向传播即可，Pytorch会根据正向传播自动计算反向传播。

import torch
torch.manual_seed(0)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = Trueimport torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data.dataset import Dataset# 定义模型
class SVHN_Model1(nn.Module):def __init__(self):super(SVHN_Model1, self).__init__()# CNN提取特征模块self.cnn = nn.Sequential(nn.Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2)),nn.ReLU(),  nn.MaxPool2d(2),nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2)),nn.ReLU(), nn.MaxPool2d(2),)# self.fc1 = nn.Linear(32*3*7, 11)self.fc2 = nn.Linear(32*3*7, 11)self.fc3 = nn.Linear(32*3*7, 11)self.fc4 = nn.Linear(32*3*7, 11)self.fc5 = nn.Linear(32*3*7, 11)self.fc6 = nn.Linear(32*3*7, 11)def forward(self, img):        feat = self.cnn(img)feat = feat.view(feat.shape[0], -1)c1 = self.fc1(feat)c2 = self.fc2(feat)c3 = self.fc3(feat)c4 = self.fc4(feat)c5 = self.fc5(feat)c6 = self.fc6(feat)return c1, c2, c3, c4, c5, c6model = SVHN_Model1()

训练代码：

# 损失函数
criterion = nn.CrossEntropyLoss()
# 优化器
optimizer = torch.optim.Adam(model.parameters(), 0.005)loss_plot, c0_plot = [], []
# 迭代10个Epoch
for epoch in range(10):for data in train_loader:c0, c1, c2, c3, c4, c5 = model(data[0])loss = criterion(c0, data[1][:, 0]) + \criterion(c1, data[1][:, 1]) + \criterion(c2, data[1][:, 2]) + \criterion(c3, data[1][:, 3]) + \criterion(c4, data[1][:, 4]) + \criterion(c5, data[1][:, 5])loss /= 6optimizer.zero_grad()loss.backward()optimizer.step()loss_plot.append(loss.item())c0_plot.append((c0.argmax(1) == data[1][:, 0]).sum().item()*1.0 / c0.shape[0])print(epoch)

迁移学习：

class SVHN_Model2(nn.Module):def __init__(self):super(SVHN_Model1, self).__init__()model_conv = models.resnet18(pretrained=True)model_conv.avgpool = nn.AdaptiveAvgPool2d(1)model_conv = nn.Sequential(*list(model_conv.children())[:-1])self.cnn = model_convself.fc1 = nn.Linear(512, 11)self.fc2 = nn.Linear(512, 11)self.fc3 = nn.Linear(512, 11)self.fc4 = nn.Linear(512, 11)self.fc5 = nn.Linear(512, 11)def forward(self, img):        feat = self.cnn(img)# print(feat.shape)feat = feat.view(feat.shape[0], -1)c1 = self.fc1(feat)c2 = self.fc2(feat)c3 = self.fc3(feat)c4 = self.fc4(feat)c5 = self.fc5(feat)return c1, c2, c3, c4, c5