百度AI Studio深度学习7日入门CV-Day02（DNN手势识别）
百度AI Studio深度学习7日入门CV-Day02（DNN手势识别）
一、课程说明
paddle安装参考上一篇文章：https://blog.csdn.net/weixin_43373616/article/details/105244311
课程地址：https://aistudio.baidu.com/aistudio/education/group/info/1149
二、手势识别
1、获取手势数据集
https://aistudio.baidu.com/aistudio/datasetdetail/23668?_=1585812845830
2、代码
1、导入库
import os
import time
import random
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import paddleimport paddle.fluid as fluid
import paddle.fluid.layers as layers
from multiprocessing import cpu_count
from paddle.fluid.dygraph import Pool2D,Conv2D
from paddle.fluid.dygraph import Linear
2、数据读取和转换
# 生成图像列表
data_path = 'F:\\Paddle\\Day_02_02\\Dataset\\Dataset'
character_folders = os.listdir(data_path)
# print(character_folders)
if(os.path.exists('./train_data.list')):
os.remove('./train_data.list')
if(os.path.exists('./test_data.list')):
os.remove('./test_data.list')
for character_folder in character_folders:
with open('./train_data.list', 'a') as f_train:
with open('./test_data.list', 'a') as f_test:
if character_folder == '.DS_Store':
continue
character_imgs = os.listdir(os.path.join(data_path,character_folder))
count = 0
for img in character_imgs:
if img =='.DS_Store':
continue
if count%10 == 0:
f_test.write(os.path.join(data_path,character_folder,img) + '\t' + character_folder + '\n')
else:
f_train.write(os.path.join(data_path,character_folder,img) + '\t' + character_folder + '\n')
count +=1
print('列表已生成')3、定义训练集和测试集的reader
def data_mapper(sample):
img, label = sample
img = Image.open(img)
img = img.resize((100, 100), Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1))
img = img/255.0
return img, label
def data_reader(data_list_path):
def reader():
with open(data_list_path, 'r') as f:
lines = f.readlines()
for line in lines:
img, label = line.split('\t')
yield img, int(label)
return paddle.reader.xmap_readers(data_mapper, reader, cpu_count(), 512)
4、数据提供器
# 用于训练的数据提供器
train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=data_reader('F:\\Paddle\\Day_02_02\\train_data.list'), buf_size=256), batch_size=32)
# 用于测试的数据提供器
test_reader = paddle.batch(reader=data_reader('F:\\Paddle\\Day_02_02\\test_data.list'), batch_size=32)
5、定义DNN网络
class MyDNN(fluid.dygraph.Layer):
def __init__(self):
super(MyDNN,self).__init__()
self.hidden1 = Linear(100,100,act='relu')
self.hidden2 = Linear(100,100,act='relu')
self.hidden3 = Linear(100,100,act='relu')
self.hidden4 = Linear(100,100,act='relu')
self.hidden5 = Linear(3*100*100,10,act='softmax')
def forward(self,input):
x1 = self.hidden1(input)
x2 = self.hidden2(x1)
x3 = self.hidden3(x2)
x4 = self.hidden4(x3)
x5 = fluid.layers.reshape(x4,shape=[-1,3*100*100])
y = self.hidden5(x5)
return y
6、训练
with fluid.dygraph.guard():
model=MyCNN() #模型实例化
model.train() #训练模式
opt=fluid.optimizer.SGDOptimizer(learning_rate=0.01, parameter_list=model.parameters())#优化器选用SGD随机梯度下降，学习率为0.001.
epochs_num=10 #迭代次数
for pass_num in range(epochs_num):
for batch_id,data in enumerate(train_reader()):
images=np.array([x[0].reshape(3,100,100) for x in data],np.float32)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]
#print(images.shape)#(32, 3, 100, 100)
# numpy转为variable
image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)
predict=model(image)#预测
# print(predict)
loss=fluid.layers.cross_entropy(predict,label)
avg_loss=fluid.layers.mean(loss)#获取loss值
acc=fluid.layers.accuracy(predict,label)#计算精度
if batch_id!=0 and batch_id%50==0:
print("train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(pass_num,batch_id,avg_loss.numpy(),acc.numpy()))
avg_loss.backward()# 反向传递
opt.minimize(avg_loss)# 更新参数
model.clear_gradients()# 每一轮参数更新介绍后，重置梯度
fluid.save_dygraph(model.state_dict(),'MyCNN')#保存模型7、模型检验
with fluid.dygraph.guard():
accs = []
model_dict, _ = fluid.load_dygraph('MyCNN')
model = MyDNN()
model.load_dict(model_dict) #加载模型参数
model.eval() #训练模式
for batch_id,data in enumerate(test_reader()):#测试集
images=np.array([x[0].reshape(3,100,100) for x in data],np.float32)
labels = np.array([x[1] for x in data]).astype('int64')
labels = labels[:, np.newaxis]
image=fluid.dygraph.to_variable(images)
label=fluid.dygraph.to_variable(labels)
predict=model(image)
acc=fluid.layers.accuracy(predict,label)
accs.append(acc.numpy()[0])
avg_acc = np.mean(accs)
print(avg_acc)8、预测图像
#读取预测图像，进行预测
def load_image(path):
img = Image.open(path)
img = img.resize((100, 100), Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1))
img = img/255.0
print(img.shape)
return img
#构建预测动态图过程
with fluid.dygraph.guard():
infer_path = '手势.JPG'
model=MyDNN()#模型实例化
model_dict,_=fluid.load_dygraph('MyDNN')
model.load_dict(model_dict)#加载模型参数
model.eval()#评估模式
infer_img = load_image(infer_path)
infer_img=np.array(infer_img).astype('float32')
infer_img=infer_img[np.newaxis,:, : ,:]
infer_img = fluid.dygraph.to_variable(infer_img)
result=model(infer_img)
display(Image.open('手势.JPG'))
print(np.argmax(result.numpy()))9、结果
因为为简单的DNN网络，模型精度不高，预测准确性不大，后期考虑用卷积神经网络来搭建网络。
10、参考链接地址：
用paddle实现一个CNN神经网络
paddle文档资料（API）