文章目录
- 一、项目背景
- 二、数据处理
-
- 1、标签与特征分离
- 2、数据可视化
- 3、训练集和测试集
- 三、模型搭建
- 四、模型训练
- 五、完整代码
一、项目背景
数据集cnn_train.csv包含人类面部表情的图片的label和feature。在这里,面部表情识别相当于一个分类问题,共有7个类别。 其中label包括7种类型表情:
一共有28709个label,说明包含了28709张表情包嘿嘿。 每一行就是一张表情包4848=2304个像素,相当于4848个灰度值(intensity)(0为黑, 255为白)
二、数据处理
1、标签与特征分离
这一步为了后面方便读取数据集,对原数据进行处理,分离后分别保存为cnn_label.csv和cnn_data.csv.
# cnn_feature_label.py 将label和像素数据分离
import pandas as pd
path = 'cnn_train.csv'# 原数据路径
# 读取数据
df = pd.read_csv(path)
# 提取label数据
df_y = df[['label']]
# 提取feature(即像素)数据
df_x = df[['feature']]
# 将label写入label.csv
df_y.to_csv('cnn_label.csv', index=False, header=False)
# 将feature数据写入data.csv
df_x.to_csv('cnn_data.csv', index=False, header=False)
执行之后生成结果文件:
2、数据可视化
完成与标签分离后,下一步我们对特征进一步处理,也就是将每个数据行的2304个像素值合成每张48*48的表情图。
# face_view.py 数据可视化
import cv2
import numpy as np
# 指定存放图片的路径
path = './/face'
# 读取像素数据
data = np.loadtxt('cnn_data.csv')
# 按行取数据
for i in range(data.shape[0]):
face_array = data[i, :].reshape((48, 48)) # reshape
cv2.imwrite(path + '//' + '{}.jpg'.format(i), face_array) # 写图片
这段代码将写入28709张表情图,执行需要一小段时间。 结果如下:
3、训练集和测试集
第一步,我们要训练模型,需要划分一下训练集和验证集。一共有28709张图片,我取前24000张图片作为训练集,其他图片作为验证集。新建文件夹cnn_train和cnn_val,将0.jpg到23999.jpg放进文件夹cnn_train,将其他图片放进文件夹cnn_val。 第二步,对每张图片标记属于哪一个类别,存放在dataset.csv中,分别在刚刚训练集和测试集执行标记任务。
# cnn_picture_label.py 表情图片和类别标注
import os
import pandas as pd
def data_label(path):
# 读取label文件
df_label = pd.read_csv('cnn_label.csv', header=None)
# 查看该文件夹下所有文件
files_dir = os.listdir(path)
# 用于存放图片名
path_list = []
# 用于存放图片对应的label
label_list = []
# 遍历该文件夹下的所有文件
for file_dir in files_dir:
# 如果某文件是图片,则将其文件名以及对应的label取出,分别放入path_list和label_list这两个列表中
if os.path.splitext(file_dir)[1] == ".jpg":
path_list.append(file_dir)
index = int(os.path.splitext(file_dir)[0])
label_list.append(df_label.iat[index, 0])
# 将两个列表写进dataset.csv文件
path_s = pd.Series(path_list)
label_s = pd.Series(label_list)
df = pd.DataFrame()
df['path'] = path_s
df['label'] = label_s
df.to_csv(path + '\\dataset.csv', index=False, header=False)
def main():
# 指定文件夹路径
train_path = 'D:\\PyCharm_Project\\deep learning\\model\\cnn_train'
val_path = 'D:\\PyCharm_Project\\deep learning\\model\\cnn_val'
data_label(train_path)
data_label(val_path)
if __name__ == "__main__":
main()
完成之后如图:
第三步,重写Dataset类,它是Pytorch中图像数据集加载的一个基类,源码如下,我们需要重写类来实现加载上面的图像数据集。
import bisect
import warnings
from torch._utils import _accumulate
from torch import randperm
class Dataset(object):
r"""An abstract class representing a :class:`Dataset`.
All datasets that represent a map from keys to data samples should subclass
it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
data sample for a given key. Subclasses could also optionally overwrite
:meth:`__len__`, which is expected to return the size of the dataset by many
:class:`~torch.utils.data.Sampler` implementations and the default options
of :class:`~torch.utils.data.DataLoader`.
.. note::
:class:`~torch.utils.data.DataLoader` by default constructs a index
sampler that yields integral indices. To make it work with a map-style
dataset with non-integral indices/keys, a custom sampler must be provided.
"""
def __getitem__(self, index):
raise NotImplementedError
def __add__(self, other):
return ConcatDataset([self, other])
# No `def __len__(self)` default?
# See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]
# in pytorch/torch/utils/data/sampler.py
重写之后如下,自定义类名为FaceDataset:
class FaceDataset(data.Dataset):
# 初始化
def __init__(self, root):
super(FaceDataset, self).__init__()
self.root = root
df_path = pd.read_csv(root + '\\dataset.csv', header=None, usecols=[0])
df_label = pd.read_csv(root + '\\dataset.csv', header=None, usecols=[1])
self.path = np.array(df_path)[:, 0]
self.label = np.array(df_label)[:, 0]
# 读取某幅图片,item为索引号
def __getitem__(self, item):
# 图像数据用于训练,需为tensor类型,label用numpy或list均可
face = cv2.imread(self.root + '\\' + self.path[item])
# 读取单通道灰度图
face_gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
# 直方图均衡化
face_hist = cv2.equalizeHist(face_gray)
"""
像素值标准化
读出的数据是48X48的,而后续卷积神经网络中nn.Conv2d() API所接受的数据格式是(batch_size, channel, width, higth),
本次图片通道为1,因此我们要将48X48 reshape为1X48X48。
"""
face_normalized = face_hist.reshape(1, 48, 48) / 255.0
face_tensor = torch.from_numpy(face_normalized)
face_tensor = face_tensor.type('torch.FloatTensor')
label = self.label[item]
return face_tensor, label
# 获取数据集样本个数
def __len__(self):
return self.path.shape[0]
到此,就实现了数据集加载的过程,下面准备使用这个类将数据喂给模型训练了。
三、模型搭建
这是Github上面部表情识别的一个开源项目的模型结构,我们使用model B搭建网络模型。使用RRelu(随机修正线性单元)作为激活函数。卷积神经网络模型如下:
class FaceCNN(nn.Module):
# 初始化网络结构
def __init__(self):
super(FaceCNN, self).__init__()
# 第一层卷积、池化
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1), # 卷积层
nn.BatchNorm2d(num_features=64), # 归一化
nn.RReLU(inplace=True), # 激活函数
nn.MaxPool2d(kernel_size=2, stride=2), # 最大值池化
)
# 第二层卷积、池化
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=128),
nn.RReLU(inplace=True),
# output:(bitch_size, 128, 12 ,12)
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 第三层卷积、池化
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.RReLU(inplace=True),
# output:(bitch_size, 256, 6 ,6)
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 参数初始化
self.conv1.apply(gaussian_weights_init)
self.conv2.apply(gaussian_weights_init)
self.conv3.apply(gaussian_weights_init)
# 全连接层
self.fc = nn.Sequential(
nn.Dropout(p=0.2),
nn.Linear(in_features=256 * 6 * 6, out_features=4096),
nn.RReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(in_features=4096, out_features=1024),
nn.RReLU(inplace=True),
nn.Linear(in_features=1024, out_features=256),
nn.RReLU(inplace=True),
nn.Linear(in_features=256, out_features=7),
)
# 前向传播
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
# 数据扁平化
x = x.view(x.shape[0], -1)
y = self.fc(x)
return y
参数解析: 输入通道数in_channels,输出通道数(即卷积核的通道数)out_channels,卷积核大小kernel_size,步长stride,对称填0行列数padding。 第一层卷积:input:(bitch_size, 1, 48, 48), output(bitch_size, 64, 24, 24) 第二层卷积:input:(bitch_size, 64, 24, 24), output(bitch_size, 128, 12, 12) 第三层卷积:input:(bitch_size, 128, 12, 12), output:(bitch_size, 256, 6, 6)
四、模型训练
损失函数使用交叉熵,优化器是随机梯度下降SGD,其中weight_decay为正则项系数,每轮训练打印损失值,每5轮训练打印准确率。
def train(train_dataset, val_dataset, batch_size, epochs, learning_rate, wt_decay):
# 载入数据并分割batch
train_loader = data.DataLoader(train_dataset, batch_size)
# 构建模型
model = FaceCNN()
# 损失函数
loss_function = nn.CrossEntropyLoss()
# 优化器
optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay)
# 逐轮训练
for epoch in range(epochs):
# 记录损失值
loss_rate = 0
# scheduler.step() # 学习率衰减
model.train() # 模型训练
for images, labels in train_loader:
# 梯度清零
optimizer.zero_grad()
# 前向传播
output = model.forward(images)
# 误差计算
loss_rate = loss_function(output, labels)
# 误差的反向传播
loss_rate.backward()
# 更新参数
optimizer.step()
# 打印每轮的损失
print('After {} epochs , the loss_rate is : '.format(epoch + 1), loss_rate.item())
if epoch % 5 == 0:
model.eval() # 模型评估
acc_train = validate(model, train_dataset, batch_size)
acc_val = validate(model, val_dataset, batch_size)
print('After {} epochs , the acc_train is : '.format(epoch + 1), acc_train)
print('After {} epochs , the acc_val is : '.format(epoch + 1), acc_val)
return model
五、完整代码
"""
CNN_face.py 基于卷积神经网络的面部表情识别(Pytorch实现)
"""
import torch
import torch.utils.data as data
import torch.nn as nn
import torch.optim as optim
import numpy as np
import pandas as pd
import cv2
# 参数初始化
def gaussian_weights_init(m):
classname = m.__class__.__name__
# 字符串查找find,找不到返回-1,不等-1即字符串中含有该字符
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.04)
# 验证模型在验证集上的正确率
def validate(model, dataset, batch_size):
val_loader = data.DataLoader(dataset, batch_size)
result, num = 0.0, 0
for images, labels in val_loader:
pred = model.forward(images)
pred = np.argmax(pred.data.numpy(), axis=1)
labels = labels.data.numpy()
result += np.sum((pred == labels))
num += len(images)
acc = result / num
return acc
class FaceDataset(data.Dataset):
# 初始化
def __init__(self, root):
super(FaceDataset, self).__init__()
self.root = root
df_path = pd.read_csv(root + '\\dataset.csv', header=None, usecols=[0])
df_label = pd.read_csv(root + '\\dataset.csv', header=None, usecols=[1])
self.path = np.array(df_path)[:, 0]
self.label = np.array(df_label)[:, 0]
# 读取某幅图片,item为索引号
def __getitem__(self, item):
# 图像数据用于训练,需为tensor类型,label用numpy或list均可
face = cv2.imread(self.root + '\\' + self.path[item])
# 读取单通道灰度图
face_gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
# 直方图均衡化
face_hist = cv2.equalizeHist(face_gray)
"""
像素值标准化
读出的数据是48X48的,而后续卷积神经网络中nn.Conv2d() API所接受的数据格式是(batch_size, channel, width, higth),
本次图片通道为1,因此我们要将48X48 reshape为1X48X48。
"""
face_normalized = face_hist.reshape(1, 48, 48) / 255.0
face_tensor = torch.from_numpy(face_normalized)
face_tensor = face_tensor.type('torch.FloatTensor')
label = self.label[item]
return face_tensor, label
# 获取数据集样本个数
def __len__(self):
return self.path.shape[0]
class FaceCNN(nn.Module):
# 初始化网络结构
def __init__(self):
super(FaceCNN, self).__init__()
# 第一次卷积、池化
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1), # 卷积层
nn.BatchNorm2d(num_features=64), # 归一化
nn.RReLU(inplace=True), # 激活函数
nn.MaxPool2d(kernel_size=2, stride=2), # 最大值池化
)
# 第二次卷积、池化
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=128),
nn.RReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 第三次卷积、池化
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.RReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 参数初始化
self.conv1.apply(gaussian_weights_init)
self.conv2.apply(gaussian_weights_init)
self.conv3.apply(gaussian_weights_init)
# 全连接层
self.fc = nn.Sequential(
nn.Dropout(p=0.2),
nn.Linear(in_features=256 * 6 * 6, out_features=4096),
nn.RReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(in_features=4096, out_features=1024),
nn.RReLU(inplace=True),
nn.Linear(in_features=1024, out_features=256),
nn.RReLU(inplace=True),
nn.Linear(in_features=256, out_features=7),
)
# 前向传播
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
# 数据扁平化
x = x.view(x.shape[0], -1)
y = self.fc(x)
return y
def train(train_dataset, val_dataset, batch_size, epochs, learning_rate, wt_decay):
# 载入数据并分割batch
train_loader = data.DataLoader(train_dataset, batch_size)
# 构建模型
model = FaceCNN()
# 损失函数
loss_function = nn.CrossEntropyLoss()
# 优化器
optimizer = optim.SGD(model.parameters(), lr=learning_rate, weight_decay=wt_decay)
# 学习率衰减
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.8)
# 逐轮训练
for epoch in range(epochs):
# 记录损失值
loss_rate = 0
# scheduler.step() # 学习率衰减
model.train() # 模型训练
for images, labels in train_loader:
# 梯度清零
optimizer.zero_grad()
# 前向传播
output = model.forward(images)
# 误差计算
loss_rate = loss_function(output, labels)
# 误差的反向传播
loss_rate.backward()
# 更新参数
optimizer.step()
# 打印每轮的损失
print('After {} epochs , the loss_rate is : '.format(epoch + 1), loss_rate.item())
if epoch % 5 == 0:
model.eval() # 模型评估
acc_train = validate(model, train_dataset, batch_size)
acc_val = validate(model, val_dataset, batch_size)
print('After {} epochs , the acc_train is : '.format(epoch + 1), acc_train)
print('After {} epochs , the acc_val is : '.format(epoch + 1), acc_val)
return model
def main():
# 数据集实例化(创建数据集)
train_dataset = FaceDataset(root='D:\PyCharm_Project\deep learning\model\cnn_train')
val_dataset = FaceDataset(root='D:\PyCharm_Project\deep learning\model\cnn_val')
# 超参数可自行指定
model = train(train_dataset, val_dataset, batch_size=128, epochs=100, learning_rate=0.1, wt_decay=0)
# 保存模型
torch.save(model, 'model_net.pkl')
if __name__ == '__main__':
main()
以上程序代码的执行过程需要较长时间,目前我只能在CPU上跑程序,速度慢,算力不足,我差不多用了1天时间训练100轮,训练时间看不同电脑设备配置,如果在GPU上跑会快很多。 下面截取几个训练结果:
从结果可以看出,训练在60轮的时候,模型在训练集上的准确率达到99%以上,而在测试集上只有60%左右,很明显出现过拟合的情况,还可以进一步优化参数,使用正则等方法防止过拟合。另外,后面几十轮训练的提升很低,还需要找出原因。 这个过程我还在学习中,上面是目前达到的结果,希望之后能够把这个模型进一步优化,提高准确率。 小结: 学习了机器学习和深度学习有一段时间,基本上看的是李宏毅老师讲解的理论知识,还未真正去实现训练一个模型。这篇记录我第一次学习的项目过程,多有不足,还需不断实践。目前遇到的问题是:1、基本的理论知识能够理解,但是在公式推导和模型选择还未很好掌握。2、未具备训练一个模型的经验(代码实现),后续需要学习实战项目。 参考资料:
- 机器学习-李宏毅(2019)视频
- https://ntumlta2019.github.io/ml-web-hw3/
- https://www.cnblogs.com/HL-space/p/10888556.html
- https://github.com/amineHorseman/facial-expression-recognition-using-cnn
数据集cnn_train.csv我放在CSDN,如果下载不了可以评论留言哦! 本博文版权归作者所有! 禁止商业转载,非商业转载请注明出处! 本文链接:https://www.cnblogs.com/chenzhenhong/p/14072684.html 版权声明:本文为博主原创文章,转载请附上原文出处链接和本声明。