百度飞桨七日深度学习手势识别

本文主要是介绍百度飞桨七日深度学习手势识别，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！

百度飞桨七日深度学习手势识别，paddlepaddle免费GPU算力，以及很好的封装，对初学者灰常友好~~~~。

下面是其中的手势识别作业，采用LeNet网络，初步感受了调参的魅力（雾😄），激发了学习理论的决心。

# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原
# View dataset directory. This directory will be recovered automatically after resetting environment. 
!ls /home/aistudio/data

# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.
# View personal work directory. All changes under this directory will be kept even after reset. Please clean unnecessary files in time to speed up environment loading.
!ls /home/aistudio/work


!cd /home/aistudio/data/data23668 && unzip -qo Dataset.zip
!cd /home/aistudio/data/data23668/Dataset && rm -f */.DS_Store # 删除无关文件

import os
import time
import random
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import paddle
import 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


# 生成图像列表
data_path = '/home/aistudio/data/data23668/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':continuecharacter_imgs = os.listdir(os.path.join(data_path,character_folder))count = 0 for img in character_imgs:if img =='.DS_Store':continueif 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')

# 定义训练集和测试集的reader
def data_mapper(sample):img, label = sampleimg = Image.open(img)img = img.resize((100, 100), Image.ANTIALIAS)img = np.array(img).astype('float32')img = img.transpose((2, 0, 1))img = img/255.0return img, labeldef 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)

# 用于训练的数据提供器
train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=data_reader('./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('./test_data.list'), batch_size=32)

#定义LeNet网络
class LeNet(fluid.dygraph.Layer):def __init__(self, training= True):super(LeNet, self).__init__()self.conv1 = Conv2D(num_channels=3, num_filters=32, filter_size=3, act='relu')self.pool1 = Pool2D(pool_size=2, pool_stride=2)self.conv2 = Conv2D(num_channels=32, num_filters=32, filter_size=3, act='relu')self.pool2 = Pool2D(pool_size=2, pool_stride=2)self.conv3 = Conv2D(num_channels=32, num_filters=64, filter_size=3, act='relu')self.pool3 = Pool2D(pool_size=2, pool_stride=2)#self.conv4 = Conv2D(num_channels=32, num_filters=64, filter_size=3, act='relu')#self.pool4 = Pool2D(pool_size=2, pool_stride=2)self.fc1 = Linear(input_dim=6400, output_dim=4096, act='relu')self.drop_ratiol = 0.5 if training else 0.0self.fc2 = Linear(input_dim=4096, output_dim=10)def forward(self, inputs):conv1 = self.conv1(inputs)  # 32 32 98 98pool1 = self.pool1(conv1)  # 32 32 49 49conv2 = self.conv2(pool1)  # 32 32 47 47pool2 = self.pool2(conv2)  # 32 32 23 23conv3 = self.conv3(pool2)  # 32 64 21 21pool3 = self.pool3(conv3)  # 32 64 10 10#conv4 = self.conv4(pool3)  # 32 64 21 21#pool4 = self.pool4(conv4)  # 32 64 10 10rs_1 = fluid.layers.reshape(pool3, [pool3.shape[0], -1])fc1 = self.fc1(rs_1)drop1 = fluid.layers.dropout(fc1, self.drop_ratiol)y = self.fc2(drop1)return y
```python#用动态图进行训练
with fluid.dygraph.guard():model=MyDNN() #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=20 #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)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()opt.minimize(avg_loss)model.clear_gradients()fluid.save_dygraph(model.state_dict(),'MyDNN')#保存模型

#模型校验
with fluid.dygraph.guard():accs = []model_dict, _ = fluid.load_dygraph('MyDNN')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)

#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/255.0print(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()))