Соси кошек вместе с кодом! Эта статья участвует【Эссе "Мяу Звезды"】.
Классификация кошек и собак на основе сети PaddlePaddle2.x LeNet
Классификация изображений заключается в различении различных типов изображений в соответствии с семантической информацией изображения, что является важной базовой проблемой компьютерного зрения.
Классификация кошек и собак - это проблема грубой классификации в классификации изображений.
# 使用2.0.0以上PaddlePaddle框架
import paddle
print(paddle.__version__)
2.0.1
Сначала импортируйте необходимые пакеты
zipfile-------------> модуль python, распакуйте zip-файл
os------------->модуль python, который можно использовать для работы операционной системы
paddle ---> Фреймворк для глубокого обучения PaddlePaddle
os------------->модуль python, который можно использовать для работы операционной системы
numpy ------------ > сторонняя библиотека python для научных вычислений
PIL ------------> Библиотека изображений Python, сторонняя библиотека обработки изображений Python
matplotlib -----> библиотека рисования python pyplot: структура рисования matplotlib
os-------------> предоставляет богатые методы для работы с файлами и каталогами
sys-------------> Предоставляет доступ к некоторым переменным, используемым или поддерживаемым интерпретатором, и функциям, которые сильно взаимодействуют с интерпретатором.
Модуль pickle------------> реализует базовую сериализацию и десериализацию данных.
warnings.filterwarnings("ignore")------------> Игнорировать все предупреждения
cpu_count ------------> Получить количество ядер процессора компьютера
# 导入需要的包
import warnings
warnings.filterwarnings("ignore")
import tarfile
import paddle
import numpy as np
from PIL import Image
import sys
import pickle
from multiprocessing import cpu_count
import matplotlib.pyplot as plt
import os
from paddle.nn import MaxPool2D,Conv2D,BatchNorm
from paddle.nn import Linear
print("本教程基于Paddle的版本号为："+paddle.__version__)
本教程基于Paddle的版本号为：2.0.1
'''
参数配置
'''
train_parameters = {
"input_size": [1, 28, 28], #输入图片的shape
"class_dim": 2, #分类数
"src_path":"data/data9154/cifar-10-python.tar.gz", #原始数据集路径
"target_path":"/home/aistudio/data/", #要解压的路径
"num_epochs": 10, #训练轮数
"train_batch_size": 100, #训练时每个批次的大小
"learning_strategy": { #优化函数相关的配置
"lr": 0.001 #超参数学习率
},
'skip_steps': 5, #每N个批次打印一次结果
'save_steps': 5, #每N个批次保存一次模型参数
"checkpoints": "/home/aistudio/checkpoints" #保存的路径
}
Шаг 1: Подготовьте данные
- (1) Распаковать исходный набор данных
- (2) Создать набор данных и загрузчик данных
Введение в набор данных
Мы используем набор данных CIFAR10. Набор данных CIFAR10 содержит 60 000 цветных изображений 32x32 в 10 категориях, каждая из которых содержит 6 000 изображений. Среди них 50 000 изображений используются в качестве обучающего набора и 10 000 изображений используются в качестве набора для проверки. На этот раз мы делаем прогнозы только для кошек и собак.
В PaddlePaddle встроено несколько часто используемых наборов данных, и CIFAR10 особенно прост в использовании.
from paddle.vision.datasets import Cifar10
1.1 Распаковать исходный набор данных
#解压原始数据集函数
def untar_data(src_path,target_path):
'''
解压原始数据集，将src_path路径下的tar包解压至target_path目录下
'''
if(not os.path.isdir(target_path + "cifar-10-batches-py")):
tar = tarfile.open(src_path)
tar.extractall(path=target_path)
tar.close()
print('数据集解压完成')
else:
print('文件已存在')
#参数初始化
src_path=train_parameters['src_path']
target_path=train_parameters['target_path']
#解压原始数据到指定路径
untar_data(src_path,target_path)
文件已存在
1.2 Создание набора данных и загрузчика данных
train_dataset и eval_dataset
Пользовательский ридер для обработки обучающих и тестовых наборов
paddle.reader.shuffle() означает, что элементы данных BUF_SIZE каждый раз кэшируются и перемешиваются
paddle.batch() означает, что каждый BATCH_SIZE формирует пакет
def unpickle(file):
# data：a 10000x3072 numpy array of uint8s. Each row of the array stores a 32x32 colour image.
# The first 1024 entries contain the red channel values, the next 1024 the green,
# and the final 1024 the blue. The image is stored in row-major order,
# so that the first 32 entries of the array are the red channel values of the first row of the image.
# labels：a list of 10000 numbers in the range 0-9.
# The number at index i indicates the label of the ith image in the array data.
fo = open(file, 'rb')
dict = pickle.load(fo,encoding = 'bytes')
train_labels = dict[b'labels']
train_array = dict[b'data']
train_array=train_array.tolist()
fo.close()
data_len=len(train_labels)
for i in range(data_len-1,-1,-1):
if train_labels[i]==3:
train_labels[i]=0
elif train_labels[i]==5:
train_labels[i]=1
else:
train_labels.pop(i)
train_array.pop(i)
train_array=np.array(train_array)
return train_labels, train_array
import paddle.vision.transforms as T
from paddle.vision.transforms import Compose, Normalize, Resize, Grayscale
from PIL import Image
'''
自定义dataset数据集
'''
from paddle.io import Dataset
class MyDataset(paddle.io.Dataset):
"""
步骤一：继承paddle.io.Dataset类
"""
def __init__(self, mode='train'):
"""
步骤二：实现构造函数，定义数据集大小
"""
super(MyDataset, self).__init__()
# 保存标签数据
self.data = []
# 保存图像数据
self.img_datas = []
# 临时变量
xs=[]
ys=[]
temp_labels=[]
temp_datas=[]
# transform定义，转灰度图，缩放到28*28尺寸，归一化
mean = [127.5]
std = [127.5]
self.transforms = Compose([Resize((28,28)), Grayscale(), Normalize(mean, std, 'CHW')])
if mode == 'train':
#批量读入训练数据
for i in range(1,6):
temp_label,temp_data=unpickle(target_path +"cifar-10-batches-py/data_batch_%d" % (i,))
ys.append(temp_label)
xs.append(temp_data)
temp_labels=np.concatenate(ys)
temp_datas=np.concatenate(xs)
else:
##批量读入测试数据
temp_labels,temp_datas=unpickle(target_path +"cifar-10-batches-py/test_batch")
temp_labels=np.array(temp_labels)
temp_datas=np.array(temp_datas)
# 转为3*32*32图像数据
temp_datas = temp_datas.reshape((-1,3,32,32))
self.data=temp_labels
self.img_datas = temp_datas
def __getitem__(self, index):
"""
步骤三：实现__getitem__方法，定义指定index时如何获取数据，并返回单条数据（训练数据，对应的标签）
"""
#返回单一数据和标签
data_image = self.img_datas[index]
# 从numpy载入Image
data_image = Image.fromarray(data_image, 'RGB')
# 取图像并应用transform进行resize、灰度、normalize
t_data_image = self.transforms(data_image)
# 取标签
label = self.data[index]
return t_data_image, np.array(label, dtype='int64')
def __len__(self):
"""
步骤四：实现__len__方法，返回数据集总数目
"""
#返回数据总数
return len(self.data)
# 测试定义的数据集
train_dataset = MyDataset(mode='train')
eval_dataset = MyDataset(mode='val')
print('=============train_dataset =============')
#输出数据集的形状和标签
print('train_dataset.__getitem__(1)[0].shape',train_dataset.__getitem__(1)[0].shape)
print('train_dataset.__getitem__(1)[1]', train_dataset.__getitem__(1)[1])
#输出数据集的长度
print('train_dataset.__len__()',train_dataset.__len__())
print('=============eval_dataset =============')
#输出数据集的长度
print('eval_dataset.__getitem__(1)[0].shape',eval_dataset.__getitem__(1)[0].shape)
print('eval_dataset.__getitem__(1)[1]', eval_dataset.__getitem__(1)[1])
print('eval_dataset.__len__()',eval_dataset.__len__())
=============train_dataset =============
train_dataset.__getitem__(1)[0].shape (1, 28, 28)
train_dataset.__getitem__(1)[1] 0
train_dataset.__len__() 10000
=============eval_dataset =============
eval_dataset.__getitem__(1)[0].shape (1, 28, 28)
eval_dataset.__getitem__(1)[1] 0
eval_dataset.__len__() 2000
#训练数据DataLoad加载
train_loader = paddle.io.DataLoader(train_dataset,
batch_size=train_parameters['train_batch_size'],
shuffle=True
)
#测试数据DataLoad加载
eval_loader = paddle.io.DataLoader(eval_dataset,
batch_size=train_parameters['train_batch_size'],
shuffle=False
)
Шаг 2. Настройка сети
(1) Строительство сети
*** Сетевая модель CNN
В модели CNN сверточная нейронная сеть может лучше использовать структурную информацию изображения. Более простая сверточная нейронная сеть Ленет, построенная в PaddlePaddle ниже.
LeNet-5 является ранним представителем модели сверточной нейронной сети, предложенной LeCun в 1998 году. Модель имеет последовательную структуру, в основном включающую 7 слоев (2 сверточных слоя, 2 объединяющих слоя и 3 полносвязных слоя), а сверточные слои и объединяющие слои расположены попеременно.
import paddle
import paddle.nn as nn
class LeNet(nn.Layer):
# Lenet定义
def __init__(self, num_classes=10):
# 分类数，默认10
super(LeNet, self).__init__()
self.num_classes = num_classes
self.features = nn.Sequential(
nn.Conv2D(
1, 6, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2D(2, 2),
nn.Conv2D(
6, 16, 5, stride=1, padding=0),
nn.ReLU(),
nn.MaxPool2D(2, 2))
if num_classes > 0:
self.fc = nn.Sequential(
nn.Linear(400, 120),
nn.Linear(120, 84), nn.Linear(84, num_classes))
def forward(self, inputs):
x = self.features(inputs)
if self.num_classes > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
# 定义网络
network=LeNet(num_classes=train_parameters['class_dim'])
# 装配模型
model=paddle.Model(network)
# 打印网络结构
model.summary((1, 1, 28 , 28))
---------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===========================================================================
Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
Linear-1 [[1, 400]] [1, 120] 48,120
Linear-2 [[1, 120]] [1, 84] 10,164
Linear-3 [[1, 84]] [1, 2] 170
===========================================================================
Total params: 60,930
Trainable params: 60,930
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.23
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 60930, 'trainable_params': 60930}
Шаг 3. Обучение модели и Шаг 4. Оценка модели
Используйте оптимизатор paddle.optimizer.Adam для оптимизации
Используйте F.cross_entropy для расчета значения убытка
# 绘制损失函数图
def draw_process(title,color,iters,data,label):
plt.title(title, fontsize=24)
plt.xlabel("iter", fontsize=20)
plt.ylabel(label, fontsize=20)
plt.plot(iters, data,color=color,label=label)
plt.legend()
plt.grid()
plt.show()
# 模型训练
# 初始化LeNet模型
model=LeNet(num_classes=train_parameters['class_dim'])
# 训练模式
model.train()
# 交叉熵
cross_entropy = paddle.nn.CrossEntropyLoss()
# 优化器
optimizer = paddle.optimizer.Adam(learning_rate=train_parameters['learning_strategy']['lr'],
parameters=model.parameters())
# 绘制loss、acc曲线图变量
steps = 0
Iters, total_loss, total_acc = [], [], []
# 开始训练
for epo in range(train_parameters['num_epochs']):
for _, data in enumerate(train_loader()):
steps += 1
x_data = data[0]
x_data = paddle.to_tensor (x_data)
y_data = paddle.to_tensor(data[1])
y_data = paddle.unsqueeze(y_data, 1)
predicts = model(x_data)
# 计算交叉熵
loss = cross_entropy(predicts, y_data)
# 计算精确度
acc = paddle.metric.accuracy(predicts, y_data)
# 反向传播
loss.backward()
optimizer.step()
# 梯度清零
optimizer.clear_grad()
if steps % train_parameters["skip_steps"] == 0:
Iters.append(steps)
total_loss.append(loss.numpy()[0])
total_acc.append(acc.numpy()[0])
#打印中间过程
print('epo: {}, step: {}, loss is: {}, acc is: {}'\
.format(epo, steps, loss.numpy(), acc.numpy()))
#保存模型参数
if steps % train_parameters["save_steps"] == 0:
save_path = train_parameters["checkpoints"]+"/"+"save_dir_" + str(steps) + '.pdparams'
print('save model to: ' + save_path)
paddle.save(model.state_dict(),save_path)
paddle.save(model.state_dict(),train_parameters["checkpoints"]+"/"+"save_dir_final.pdparams")
draw_process("trainning loss","red",Iters,total_loss,"trainning loss")
draw_process("trainning acc","green",Iters,total_acc,"trainning acc")
epo: 0, step: 5, loss is: [1.1097064], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_5.pdparams
epo: 0, step: 10, loss is: [0.8729222], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_10.pdparams
epo: 0, step: 15, loss is: [0.77851003], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_15.pdparams
epo: 0, step: 20, loss is: [0.68859595], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_20.pdparams
epo: 0, step: 25, loss is: [0.71485907], acc is: [0.48]
save model to: /home/aistudio/checkpoints/save_dir_25.pdparams
epo: 0, step: 30, loss is: [0.69014424], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_30.pdparams
epo: 0, step: 35, loss is: [0.7331408], acc is: [0.42]
save model to: /home/aistudio/checkpoints/save_dir_35.pdparams
epo: 0, step: 40, loss is: [0.6923569], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_40.pdparams
epo: 0, step: 45, loss is: [0.70091367], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_45.pdparams
epo: 0, step: 50, loss is: [0.69078857], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_50.pdparams
epo: 0, step: 55, loss is: [0.69088614], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_55.pdparams
epo: 0, step: 60, loss is: [0.7027031], acc is: [0.46]
save model to: /home/aistudio/checkpoints/save_dir_60.pdparams
epo: 0, step: 65, loss is: [0.6824346], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_65.pdparams
epo: 0, step: 70, loss is: [0.6795273], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_70.pdparams
epo: 0, step: 75, loss is: [0.6809163], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_75.pdparams
epo: 0, step: 80, loss is: [0.7107715], acc is: [0.43]
save model to: /home/aistudio/checkpoints/save_dir_80.pdparams
epo: 0, step: 85, loss is: [0.70901597], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_85.pdparams
epo: 0, step: 90, loss is: [0.7054188], acc is: [0.44]
save model to: /home/aistudio/checkpoints/save_dir_90.pdparams
epo: 0, step: 95, loss is: [0.6982265], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_95.pdparams
epo: 0, step: 100, loss is: [0.6998703], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_100.pdparams
epo: 1, step: 105, loss is: [0.70260566], acc is: [0.44]
save model to: /home/aistudio/checkpoints/save_dir_105.pdparams
epo: 1, step: 110, loss is: [0.67828727], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_110.pdparams
epo: 1, step: 115, loss is: [0.68608195], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_115.pdparams
epo: 1, step: 120, loss is: [0.697596], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_120.pdparams
epo: 1, step: 125, loss is: [0.7016902], acc is: [0.5]
save model to: /home/aistudio/checkpoints/save_dir_125.pdparams
epo: 1, step: 130, loss is: [0.6790494], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_130.pdparams
epo: 1, step: 135, loss is: [0.68013227], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_135.pdparams
epo: 1, step: 140, loss is: [0.70905924], acc is: [0.45]
save model to: /home/aistudio/checkpoints/save_dir_140.pdparams
epo: 1, step: 145, loss is: [0.6931264], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_145.pdparams
epo: 1, step: 150, loss is: [0.6971727], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_150.pdparams
epo: 1, step: 155, loss is: [0.67896414], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_155.pdparams
epo: 1, step: 160, loss is: [0.67097855], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_160.pdparams
epo: 1, step: 165, loss is: [0.69235575], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_165.pdparams
epo: 1, step: 170, loss is: [0.6894104], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_170.pdparams
epo: 1, step: 175, loss is: [0.70366347], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_175.pdparams
epo: 1, step: 180, loss is: [0.69162464], acc is: [0.48]
save model to: /home/aistudio/checkpoints/save_dir_180.pdparams
epo: 1, step: 185, loss is: [0.67835146], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_185.pdparams
epo: 1, step: 190, loss is: [0.6919897], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_190.pdparams
epo: 1, step: 195, loss is: [0.69632596], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_195.pdparams
epo: 1, step: 200, loss is: [0.70401454], acc is: [0.41]
save model to: /home/aistudio/checkpoints/save_dir_200.pdparams
epo: 2, step: 205, loss is: [0.72231257], acc is: [0.47]
save model to: /home/aistudio/checkpoints/save_dir_205.pdparams
epo: 2, step: 210, loss is: [0.6722144], acc is: [0.65]
save model to: /home/aistudio/checkpoints/save_dir_210.pdparams
epo: 2, step: 215, loss is: [0.7005479], acc is: [0.43]
save model to: /home/aistudio/checkpoints/save_dir_215.pdparams
epo: 2, step: 220, loss is: [0.68955404], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_220.pdparams
epo: 2, step: 225, loss is: [0.68503153], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_225.pdparams
epo: 2, step: 230, loss is: [0.6742158], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_230.pdparams
epo: 2, step: 235, loss is: [0.68807405], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_235.pdparams
epo: 2, step: 240, loss is: [0.7038729], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_240.pdparams
epo: 2, step: 245, loss is: [0.69256955], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_245.pdparams
epo: 2, step: 250, loss is: [0.6998977], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_250.pdparams
epo: 2, step: 255, loss is: [0.6635308], acc is: [0.64]
save model to: /home/aistudio/checkpoints/save_dir_255.pdparams
epo: 2, step: 260, loss is: [0.6831071], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_260.pdparams
epo: 2, step: 265, loss is: [0.6725425], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_265.pdparams
epo: 2, step: 270, loss is: [0.6881926], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_270.pdparams
epo: 2, step: 275, loss is: [0.69550765], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_275.pdparams
epo: 2, step: 280, loss is: [0.68708885], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_280.pdparams
epo: 2, step: 285, loss is: [0.68473077], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_285.pdparams
epo: 2, step: 290, loss is: [0.6903842], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_290.pdparams
epo: 2, step: 295, loss is: [0.7028897], acc is: [0.48]
save model to: /home/aistudio/checkpoints/save_dir_295.pdparams
epo: 2, step: 300, loss is: [0.6931243], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_300.pdparams
epo: 3, step: 305, loss is: [0.68098104], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_305.pdparams
epo: 3, step: 310, loss is: [0.6757507], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_310.pdparams
epo: 3, step: 315, loss is: [0.7027341], acc is: [0.44]
save model to: /home/aistudio/checkpoints/save_dir_315.pdparams
epo: 3, step: 320, loss is: [0.7009732], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_320.pdparams
epo: 3, step: 325, loss is: [0.7078163], acc is: [0.47]
save model to: /home/aistudio/checkpoints/save_dir_325.pdparams
epo: 3, step: 330, loss is: [0.6958405], acc is: [0.44]
save model to: /home/aistudio/checkpoints/save_dir_330.pdparams
epo: 3, step: 335, loss is: [0.69992703], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_335.pdparams
epo: 3, step: 340, loss is: [0.69363695], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_340.pdparams
epo: 3, step: 345, loss is: [0.6923307], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_345.pdparams
epo: 3, step: 350, loss is: [0.6739081], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_350.pdparams
epo: 3, step: 355, loss is: [0.68306243], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_355.pdparams
epo: 3, step: 360, loss is: [0.66385293], acc is: [0.64]
save model to: /home/aistudio/checkpoints/save_dir_360.pdparams
epo: 3, step: 365, loss is: [0.6816753], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_365.pdparams
epo: 3, step: 370, loss is: [0.6921282], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_370.pdparams
epo: 3, step: 375, loss is: [0.6865966], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_375.pdparams
epo: 3, step: 380, loss is: [0.69338584], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_380.pdparams
epo: 3, step: 385, loss is: [0.6800542], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_385.pdparams
epo: 3, step: 390, loss is: [0.6839569], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_390.pdparams
epo: 3, step: 395, loss is: [0.6774286], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_395.pdparams
epo: 3, step: 400, loss is: [0.7004008], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_400.pdparams
epo: 4, step: 405, loss is: [0.7059412], acc is: [0.44]
save model to: /home/aistudio/checkpoints/save_dir_405.pdparams
epo: 4, step: 410, loss is: [0.69455093], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_410.pdparams
epo: 4, step: 415, loss is: [0.6933525], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_415.pdparams
epo: 4, step: 420, loss is: [0.7079694], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_420.pdparams
epo: 4, step: 425, loss is: [0.6937676], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_425.pdparams
epo: 4, step: 430, loss is: [0.68947273], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_430.pdparams
epo: 4, step: 435, loss is: [0.6781409], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_435.pdparams
epo: 4, step: 440, loss is: [0.6878584], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_440.pdparams
epo: 4, step: 445, loss is: [0.66629666], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_445.pdparams
epo: 4, step: 450, loss is: [0.66666824], acc is: [0.62]
save model to: /home/aistudio/checkpoints/save_dir_450.pdparams
epo: 4, step: 455, loss is: [0.67528206], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_455.pdparams
epo: 4, step: 460, loss is: [0.7015488], acc is: [0.47]
save model to: /home/aistudio/checkpoints/save_dir_460.pdparams
epo: 4, step: 465, loss is: [0.6915476], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_465.pdparams
epo: 4, step: 470, loss is: [0.6868398], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_470.pdparams
epo: 4, step: 475, loss is: [0.69640535], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_475.pdparams
epo: 4, step: 480, loss is: [0.6844581], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_480.pdparams
epo: 4, step: 485, loss is: [0.678205], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_485.pdparams
epo: 4, step: 490, loss is: [0.6782288], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_490.pdparams
epo: 4, step: 495, loss is: [0.6809789], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_495.pdparams
epo: 4, step: 500, loss is: [0.6791268], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_500.pdparams
epo: 5, step: 505, loss is: [0.66857773], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_505.pdparams
epo: 5, step: 510, loss is: [0.68727225], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_510.pdparams
epo: 5, step: 515, loss is: [0.68932843], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_515.pdparams
epo: 5, step: 520, loss is: [0.68978363], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_520.pdparams
epo: 5, step: 525, loss is: [0.69064134], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_525.pdparams
epo: 5, step: 530, loss is: [0.682237], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_530.pdparams
epo: 5, step: 535, loss is: [0.68976945], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_535.pdparams
epo: 5, step: 540, loss is: [0.67902535], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_540.pdparams
epo: 5, step: 545, loss is: [0.67134506], acc is: [0.65]
save model to: /home/aistudio/checkpoints/save_dir_545.pdparams
epo: 5, step: 550, loss is: [0.6688429], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_550.pdparams
epo: 5, step: 555, loss is: [0.7254223], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_555.pdparams
epo: 5, step: 560, loss is: [0.69241136], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_560.pdparams
epo: 5, step: 565, loss is: [0.6801878], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_565.pdparams
epo: 5, step: 570, loss is: [0.6906636], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_570.pdparams
epo: 5, step: 575, loss is: [0.70213795], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_575.pdparams
epo: 5, step: 580, loss is: [0.69319504], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_580.pdparams
epo: 5, step: 585, loss is: [0.7011637], acc is: [0.48]
save model to: /home/aistudio/checkpoints/save_dir_585.pdparams
epo: 5, step: 590, loss is: [0.6848818], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_590.pdparams
epo: 5, step: 595, loss is: [0.67795885], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_595.pdparams
epo: 5, step: 600, loss is: [0.6833943], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_600.pdparams
epo: 6, step: 605, loss is: [0.6795752], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_605.pdparams
epo: 6, step: 610, loss is: [0.6964473], acc is: [0.5]
save model to: /home/aistudio/checkpoints/save_dir_610.pdparams
epo: 6, step: 615, loss is: [0.7281563], acc is: [0.43]
save model to: /home/aistudio/checkpoints/save_dir_615.pdparams
epo: 6, step: 620, loss is: [0.675564], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_620.pdparams
epo: 6, step: 625, loss is: [0.6895311], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_625.pdparams
epo: 6, step: 630, loss is: [0.67448664], acc is: [0.64]
save model to: /home/aistudio/checkpoints/save_dir_630.pdparams
epo: 6, step: 635, loss is: [0.6737503], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_635.pdparams
epo: 6, step: 640, loss is: [0.70881164], acc is: [0.46]
save model to: /home/aistudio/checkpoints/save_dir_640.pdparams
epo: 6, step: 645, loss is: [0.68261325], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_645.pdparams
epo: 6, step: 650, loss is: [0.6765045], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_650.pdparams
epo: 6, step: 655, loss is: [0.6759614], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_655.pdparams
epo: 6, step: 660, loss is: [0.6793112], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_660.pdparams
epo: 6, step: 665, loss is: [0.6845392], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_665.pdparams
epo: 6, step: 670, loss is: [0.6814833], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_670.pdparams
epo: 6, step: 675, loss is: [0.68463284], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_675.pdparams
epo: 6, step: 680, loss is: [0.6939957], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_680.pdparams
epo: 6, step: 685, loss is: [0.6949662], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_685.pdparams
epo: 6, step: 690, loss is: [0.6850964], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_690.pdparams
epo: 6, step: 695, loss is: [0.6783737], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_695.pdparams
epo: 6, step: 700, loss is: [0.6847418], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_700.pdparams
epo: 7, step: 705, loss is: [0.6696134], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_705.pdparams
epo: 7, step: 710, loss is: [0.699369], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_710.pdparams
epo: 7, step: 715, loss is: [0.6834408], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_715.pdparams
epo: 7, step: 720, loss is: [0.6834759], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_720.pdparams
epo: 7, step: 725, loss is: [0.68610823], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_725.pdparams
epo: 7, step: 730, loss is: [0.667547], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_730.pdparams
epo: 7, step: 735, loss is: [0.70002645], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_735.pdparams
epo: 7, step: 740, loss is: [0.6882743], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_740.pdparams
epo: 7, step: 745, loss is: [0.6829937], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_745.pdparams
epo: 7, step: 750, loss is: [0.6799063], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_750.pdparams
epo: 7, step: 755, loss is: [0.6759838], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_755.pdparams
epo: 7, step: 760, loss is: [0.7013712], acc is: [0.47]
save model to: /home/aistudio/checkpoints/save_dir_760.pdparams
epo: 7, step: 765, loss is: [0.67678285], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_765.pdparams
epo: 7, step: 770, loss is: [0.6903254], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_770.pdparams
epo: 7, step: 775, loss is: [0.71212935], acc is: [0.45]
save model to: /home/aistudio/checkpoints/save_dir_775.pdparams
epo: 7, step: 780, loss is: [0.66622734], acc is: [0.62]
save model to: /home/aistudio/checkpoints/save_dir_780.pdparams
epo: 7, step: 785, loss is: [0.6900478], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_785.pdparams
epo: 7, step: 790, loss is: [0.6736644], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_790.pdparams
epo: 7, step: 795, loss is: [0.70363], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_795.pdparams
epo: 7, step: 800, loss is: [0.6934418], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_800.pdparams
epo: 8, step: 805, loss is: [0.6693335], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_805.pdparams
epo: 8, step: 810, loss is: [0.694484], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_810.pdparams
epo: 8, step: 815, loss is: [0.7255772], acc is: [0.37]
save model to: /home/aistudio/checkpoints/save_dir_815.pdparams
epo: 8, step: 820, loss is: [0.6915439], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_820.pdparams
epo: 8, step: 825, loss is: [0.6881697], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_825.pdparams
epo: 8, step: 830, loss is: [0.68885416], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_830.pdparams
epo: 8, step: 835, loss is: [0.6819633], acc is: [0.52]
save model to: /home/aistudio/checkpoints/save_dir_835.pdparams
epo: 8, step: 840, loss is: [0.68416965], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_840.pdparams
epo: 8, step: 845, loss is: [0.6801962], acc is: [0.5]
save model to: /home/aistudio/checkpoints/save_dir_845.pdparams
epo: 8, step: 850, loss is: [0.67312473], acc is: [0.62]
save model to: /home/aistudio/checkpoints/save_dir_850.pdparams
epo: 8, step: 855, loss is: [0.6651606], acc is: [0.64]
save model to: /home/aistudio/checkpoints/save_dir_855.pdparams
epo: 8, step: 860, loss is: [0.66604716], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_860.pdparams
epo: 8, step: 865, loss is: [0.6775603], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_865.pdparams
epo: 8, step: 870, loss is: [0.6985699], acc is: [0.5]
save model to: /home/aistudio/checkpoints/save_dir_870.pdparams
epo: 8, step: 875, loss is: [0.6906618], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_875.pdparams
epo: 8, step: 880, loss is: [0.6838692], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_880.pdparams
epo: 8, step: 885, loss is: [0.6818925], acc is: [0.63]
save model to: /home/aistudio/checkpoints/save_dir_885.pdparams
epo: 8, step: 890, loss is: [0.7003258], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_890.pdparams
epo: 8, step: 895, loss is: [0.7080064], acc is: [0.5]
save model to: /home/aistudio/checkpoints/save_dir_895.pdparams
epo: 8, step: 900, loss is: [0.67341954], acc is: [0.61]
save model to: /home/aistudio/checkpoints/save_dir_900.pdparams
epo: 9, step: 905, loss is: [0.68930835], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_905.pdparams
epo: 9, step: 910, loss is: [0.7058289], acc is: [0.46]
save model to: /home/aistudio/checkpoints/save_dir_910.pdparams
epo: 9, step: 915, loss is: [0.67915636], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_915.pdparams
epo: 9, step: 920, loss is: [0.687831], acc is: [0.58]
save model to: /home/aistudio/checkpoints/save_dir_920.pdparams
epo: 9, step: 925, loss is: [0.6957987], acc is: [0.46]
save model to: /home/aistudio/checkpoints/save_dir_925.pdparams
epo: 9, step: 930, loss is: [0.6923476], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_930.pdparams
epo: 9, step: 935, loss is: [0.70298016], acc is: [0.47]
save model to: /home/aistudio/checkpoints/save_dir_935.pdparams
epo: 9, step: 940, loss is: [0.69297534], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_940.pdparams
epo: 9, step: 945, loss is: [0.6787053], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_945.pdparams
epo: 9, step: 950, loss is: [0.6894692], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_950.pdparams
epo: 9, step: 955, loss is: [0.70166737], acc is: [0.51]
save model to: /home/aistudio/checkpoints/save_dir_955.pdparams
epo: 9, step: 960, loss is: [0.69754714], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_960.pdparams
epo: 9, step: 965, loss is: [0.6867398], acc is: [0.54]
save model to: /home/aistudio/checkpoints/save_dir_965.pdparams
epo: 9, step: 970, loss is: [0.6726653], acc is: [0.59]
save model to: /home/aistudio/checkpoints/save_dir_970.pdparams
epo: 9, step: 975, loss is: [0.6738178], acc is: [0.56]
save model to: /home/aistudio/checkpoints/save_dir_975.pdparams
epo: 9, step: 980, loss is: [0.6699579], acc is: [0.55]
save model to: /home/aistudio/checkpoints/save_dir_980.pdparams
epo: 9, step: 985, loss is: [0.6805726], acc is: [0.57]
save model to: /home/aistudio/checkpoints/save_dir_985.pdparams
epo: 9, step: 990, loss is: [0.68973434], acc is: [0.53]
save model to: /home/aistudio/checkpoints/save_dir_990.pdparams
epo: 9, step: 995, loss is: [0.66840816], acc is: [0.6]
save model to: /home/aistudio/checkpoints/save_dir_995.pdparams
epo: 9, step: 1000, loss is: [0.7072126], acc is: [0.49]
save model to: /home/aistudio/checkpoints/save_dir_1000.pdparams
Проверка модели
После завершения обучения необходимо проверить эффект модели.В это время загружается набор тестовых данных, а затем обученная модель используется для прогнозирования набора тестов, а также рассчитываются потери и точность.
'''
模型评估
'''
model__state_dict = paddle.load(train_parameters["checkpoints"]+"/"+"save_dir_final.pdparams")
model_eval = LeNet( num_classes=train_parameters['class_dim'])
model_eval.set_state_dict(model__state_dict)
model_eval.eval()
accs = []
for _, data in enumerate(eval_loader()):
x_data = data[0]
y_data = paddle.to_tensor(data[1])
y_data = paddle.unsqueeze(y_data, 1)
predicts = model_eval(x_data)
# 计算acc
acc = paddle.metric.accuracy(predicts, y_data)
accs.append(acc.numpy()[0])
print('模型在验证集上的准确率为：',np.mean(accs))
模型在验证集上的准确率为： 0.5365
Шаг 5. Предсказание модели
# 图片预处理
def load_image(file):
'''
预测图片预处理
'''
#打开图片
im = Image.open(file)
#将图片调整为跟训练数据一样的大小 28*28，设定ANTIALIAS，即抗锯齿.resize是缩放
im = im.resize((28, 28), Image.ANTIALIAS)
# 转灰度图
im = im.convert('1')
#建立图片矩阵 类型为float32
im = np.array(im).astype(np.float32)
#矩阵转置
#将像素值从【0-255】转换为【0-1】
im = im / 255.0
#print(im)
im = np.expand_dims(im, axis=0)
# 保持和之前输入image维度一致
print('im_shape的维度：',im.shape)
return im
'''
模型预测
'''
# 载入模型
model__state_dict = paddle.load(train_parameters["checkpoints"]+"/"+"save_dir_final.pdparams")
model_eval = LeNet( num_classes=train_parameters['class_dim'])
model_eval.set_state_dict(model__state_dict)
#训练模式
model.eval()
#展示预测图片
infer_path='/home/aistudio/data/data7940/dog.png'
img = Image.open(infer_path)
plt.imshow(img) #根据数组绘制图像
plt.show() #显示图像
#对预测图片进行预处理
infer_img = load_image(infer_path)
infer_img = infer_img.reshape(1,28,28)
# infer_img = infer_img.toGra
#定义标签列表
label_list = [ "cat", "dog"]
data = infer_img
dy_x_data = np.array(data).astype('float32')
dy_x_data=dy_x_data[np.newaxis,:, : ,:]
img = paddle.to_tensor(dy_x_data)
out = model(img)
lab = np.argmax(out.numpy()) #argmax():返回最大数的索引
print(label_list[lab])
im_shape的维度： (1, 28, 28)
dog