自己动手搭建模型：图像分类篇

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发布时间：2025-07-31 15:13:17

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来源于php中文网

原创

本文介绍用paddlepaddle搭建alexnet、vgg、resnet、densenet等深度学习模型的过程。先处理数据集，取两类图片划分训练、验证集，定义数据集类并预处理。接着分别构建各模型，展示结构与参数，最后训练验证，其中前三者预测准确，densenet略有偏差。

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自己动手搭建模型：图像分类篇 - php中文网

项目简介

对于我们初学者来说，很多时候只关注了模型的调用，对于paddle封装好的完整模型套件，只需要train,predict就可以完成一个深度学习任务，但是如果想进一步深入学习，则需要对模型的结构有所了解，试着自己用API去组建模型是一个很好的方案，因为在搭建的过程中会遇到很多结构图上看起来很简单，但是实现的时候会有点小迷糊的细节，这些细节会让你去更深的思考这些模型，同时在加深对模型理解的同时，还能增强编程能力。

数据集介绍

数据集取自第三届中国AI+创新创业大赛：半监督学习目标定位竞赛，原数据集取自IMAGENET，共100类，各类别分别100图片，为了便于训练和验证，只取其中2类作为数据集。

导入相关库

In [1]

import paddleimport paddle.nn as nnimport numpy as npfrom paddle.io import Dataset,DataLoaderfrom tqdm import tqdmimport osimport randomimport cv2from paddle.vision import transforms as Timport paddle.nn.functional as Fimport pandas as pdimport math

进行相关配置

In [2]

lr_base=1e-3epochs=10batch_size=4image_size=(256,256)

生成数据集文件和数据集

这里的数据集采自IMAGENET，因为只是为了验证模型的正确性，所以数据集比较小，两个类别各100张图片，分别置于image_class1,image_class2文件夹中

In [49]

!unzip -qo dataset.zip

In [3]

dataset=[]with open("dataset.txt","a") as f:    for img in os.listdir("image_class1"):
        dataset.append("image_class1/"+img+" 0\n")
        f.write("image_class1/"+img+" 0\n")    for img in os.listdir("image_class2"):
        dataset.append("image_class2/"+img+" 1\n")
        f.write("image_class2/"+img+" 1\n")
random.shuffle(dataset)
val=0.2offset=int(len(dataset)*val)with open("train_list.txt","a")as f:    for img in dataset[:-offset]:
        f.write(img)with open("val_list.txt","a")as f:    for img in dataset[-offset:]:
        f.write(img)

这里提示一下一定要注意数据的归一化预处理，否则模型通常不收敛

In [4]

#datasetn for alexnet c=3class DatasetN(Dataset):
    
    def __init__(self,data="dataset.txt",transforms=None):
        super().__init__()
        self.dataset=open(data).readlines()
        self.dataset=[d.strip() for d in self.dataset]
        self.transforms=transforms    
    def __getitem__(self,ind):
        data=self.dataset[ind]
        img,label=data.split(" ")
        img=cv2.imread(img,1)
        img=cv2.resize(img,(224,224)).astype("float32").transpose((2,0,1))
        img=img.reshape((-1,224,224))        if img is not None:
            img=img#self.transforms(img)
        img=(img-127.5)/255.0
        label=int(label)        return img,label    
    def __len__(self):
        return len(self.dataset)

train_set=DatasetN("train_list.txt",T.Compose([T.Normalize(data_format="CHW")]))
val_set=DatasetN("val_list.txt",T.Compose([T.Normalize(data_format="CHW")]))
train_loader=DataLoader(train_set,batch_size=batch_size,shuffle=True)
val_loader=DataLoader(val_set,batch_size=batch_size)

构建模型

AlexNet

图像分类模型中第一个被广泛注意到的使用卷积的深度学习模型，也是这一波深度学习浪潮的引领者，在2012年的IMAGENET中以远超第二名的成绩夺冠，自此深度学习再次走入人们的视野。这个模型在论文《ImageNet Classification with Deep Convolutional Neural Networks》中被提出。模型结构如图所示自己动手搭建模型：图像分类篇 - php中文网结构图分成了上下两层，是因为原作者在两块GPU中进行的模型训练，最后进行合并，在理解结构图时只需要看作同一层即可。

根据结构图，可以看出输入是一个3通道的图像，第一层是大小为11的卷积操作，接着是5* 5卷积和MaxPooling，以及连续的3* 3卷积和MaxPooling，最后将特征图接入全连接层，所以要先Flatten，最后一个全连接层的维度是所分类别的数量，在原文中是1000，需要根据自己的数据集进行修改。通常卷积和全连接层后面都会加上ReLU作为激活函数。

In [14]

#AlexNetclass AlexNet(nn.Layer):
    def __init__(self):
        super().__init__()
        self.layers=nn.LayerList()
        self.layers.append(nn.Sequential(nn.Conv2D(3,96,kernel_size=11,stride=4),nn.ReLU()))
        self.layers.append(nn.Sequential(nn.Conv2D(96,256,kernel_size=5,stride=1,padding=2),nn.ReLU(),nn.MaxPool2D(kernel_size=3,stride=2)))
        self.layers.append(nn.Sequential(nn.Conv2D(256,384,kernel_size=3,stride=1,padding=1),nn.ReLU(),nn.MaxPool2D(kernel_size=3,stride=2,padding=0)))
        self.layers.append(nn.Sequential(nn.Conv2D(384,384,kernel_size=3,stride=1,padding=1),nn.ReLU()))
        self.layers.append(nn.Sequential(nn.Conv2D(384,256,kernel_size=3,stride=1,padding=1),nn.ReLU(),nn.MaxPool2D(kernel_size=3,stride=2)))
        self.layers.append(nn.Sequential(nn.Flatten(),nn.Linear(6400,4096),nn.ReLU()))
        self.layers.append(nn.Sequential(nn.Linear(4096,4096),nn.ReLU()))
        self.layers.append(nn.Sequential(nn.Linear(4096,2)))    
    def forward(self,x):
        for layer in self.layers:
            y=layer(x)
            x=y        return y
network=AlexNet()
paddle.summary(network,(16,3,224,224))#使用paddle API验证结构正确性

---------------------------------------------------------------------------
 Layer (type)       Input Shape          Output Shape         Param #    
===========================================================================
   Conv2D-46    [[16, 3, 224, 224]]    [16, 96, 54, 54]       34,944     
    ReLU-64      [[16, 96, 54, 54]]    [16, 96, 54, 54]          0       
   Conv2D-47     [[16, 96, 54, 54]]   [16, 256, 54, 54]       614,656    
    ReLU-65     [[16, 256, 54, 54]]   [16, 256, 54, 54]          0       
 MaxPool2D-28   [[16, 256, 54, 54]]   [16, 256, 26, 26]          0       
   Conv2D-48    [[16, 256, 26, 26]]   [16, 384, 26, 26]       885,120    
    ReLU-66     [[16, 384, 26, 26]]   [16, 384, 26, 26]          0       
 MaxPool2D-29   [[16, 384, 26, 26]]   [16, 384, 12, 12]          0       
   Conv2D-49    [[16, 384, 12, 12]]   [16, 384, 12, 12]      1,327,488   
    ReLU-67     [[16, 384, 12, 12]]   [16, 384, 12, 12]          0       
   Conv2D-50    [[16, 384, 12, 12]]   [16, 256, 12, 12]       884,992    
    ReLU-68     [[16, 256, 12, 12]]   [16, 256, 12, 12]          0       
 MaxPool2D-30   [[16, 256, 12, 12]]    [16, 256, 5, 5]           0       
  Flatten-17     [[16, 256, 5, 5]]        [16, 6400]             0       
   Linear-28        [[16, 6400]]          [16, 4096]        26,218,496   
    ReLU-69         [[16, 4096]]          [16, 4096]             0       
   Linear-29        [[16, 4096]]          [16, 4096]        16,781,312   
    ReLU-70         [[16, 4096]]          [16, 4096]             0       
   Linear-30        [[16, 4096]]           [16, 2]             8,194     
===========================================================================
Total params: 46,755,202
Trainable params: 46,755,202
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 9.19
Forward/backward pass size (MB): 367.91
Params size (MB): 178.36
Estimated Total Size (MB): 555.45
---------------------------------------------------------------------------

{'total_params': 46755202, 'trainable_params': 46755202}

在模型训练和验证部分，验证结果为

Predict begin...step 10/10 [==============================] - 5ms/step        Predict samples: 10[1 1 0 1 0 0 0 1 0 1]

正确答案为1 1 0 1 0 0 0 1 0 1

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VGG Net

VGG网络是继AlexNet之后的另一个卷积网络，主要工作是使用了3* 3的卷积以及更深的网络，其基本结构还是Conv+Pooling，在论文 Very Deep Convolutional Networks for Large-Scale Image Recognition由Oxford Visual Geometry Group提出，常用的结构有VGG11、VGG13、VGG16、VGG19，是2014年ILSVRC竞赛的第二名，第一名是GoogLeNet，但是在很多的迁移学习中，VGG表现更优。模型结构图如下自己动手搭建模型：图像分类篇 - php中文网

In [15]

#VGG Netclass VGG(nn.Layer):

    def __init__(self,features,num_classes):
        super().__init__()
        self.features=features
        self.cls=nn.Sequential(nn.Dropout(0.5),nn.Linear(7*7*512,4096),nn.ReLU(),nn.Linear(4096,4096),nn.ReLU(),nn.Linear(4096,num_classes))    
    def forward(self,x):
        x=self.features(x)
        x=self.cls(x)        return xdef make_features(cfg):
    layers=[]
    in_c=3
    for v in cfg:        if v=="P":
            layers.append(nn.MaxPool2D(2))        else:
            layers.append(nn.Conv2D(in_c,v,kernel_size=3,stride=1,padding=1))
            layers.append(nn.ReLU())
            in_c=v
    layers.append(nn.Flatten())    return nn.Sequential(*layers)

cfg={    'VGG11': [64, 'P', 128, 'P', 256, 256, 'P', 512, 512, 'P', 512, 512, 'P'],    'VGG13': [64, 64, 'P', 128, 128, 'P', 256, 256, 'P', 512, 512, 'P', 512, 512, 'P'],    'VGG16': [64, 64, 'P', 128, 128, 'P', 256, 256, 256, 'P', 512, 512, 512, 'P', 512, 512, 512, 'P'],    "VGG19": [64, 64, 'P', 128, 128, 'P', 256, 256, 256, 256, 'P', 512, 512, 512, 512, 'P', 512, 512, 512, 512, 'P']
}

features=make_features(cfg["VGG11"])
network=VGG(features,2)
paddle.summary(network,(16,3,224,224))#使用paddle API验证结构正确性

----------------------------------------------------------------------------
 Layer (type)        Input Shape          Output Shape         Param #    
============================================================================
   Conv2D-51     [[16, 3, 224, 224]]   [16, 64, 224, 224]       1,792     
    ReLU-71     [[16, 64, 224, 224]]   [16, 64, 224, 224]         0       
 MaxPool2D-31   [[16, 64, 224, 224]]   [16, 64, 112, 112]         0       
   Conv2D-52    [[16, 64, 112, 112]]  [16, 128, 112, 112]      73,856     
    ReLU-72     [[16, 128, 112, 112]] [16, 128, 112, 112]         0       
 MaxPool2D-32   [[16, 128, 112, 112]]  [16, 128, 56, 56]          0       
   Conv2D-53     [[16, 128, 56, 56]]   [16, 256, 56, 56]       295,168    
    ReLU-73      [[16, 256, 56, 56]]   [16, 256, 56, 56]          0       
   Conv2D-54     [[16, 256, 56, 56]]   [16, 256, 56, 56]       590,080    
    ReLU-74      [[16, 256, 56, 56]]   [16, 256, 56, 56]          0       
 MaxPool2D-33    [[16, 256, 56, 56]]   [16, 256, 28, 28]          0       
   Conv2D-55     [[16, 256, 28, 28]]   [16, 512, 28, 28]      1,180,160   
    ReLU-75      [[16, 512, 28, 28]]   [16, 512, 28, 28]          0       
   Conv2D-56     [[16, 512, 28, 28]]   [16, 512, 28, 28]      2,359,808   
    ReLU-76      [[16, 512, 28, 28]]   [16, 512, 28, 28]          0       
 MaxPool2D-34    [[16, 512, 28, 28]]   [16, 512, 14, 14]          0       
   Conv2D-57     [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
    ReLU-77      [[16, 512, 14, 14]]   [16, 512, 14, 14]          0       
   Conv2D-58     [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
    ReLU-78      [[16, 512, 14, 14]]   [16, 512, 14, 14]          0       
 MaxPool2D-35    [[16, 512, 14, 14]]    [16, 512, 7, 7]           0       
  Flatten-19      [[16, 512, 7, 7]]       [16, 25088]             0       
   Dropout-1        [[16, 25088]]         [16, 25088]             0       
   Linear-31        [[16, 25088]]          [16, 4096]        102,764,544  
    ReLU-79         [[16, 4096]]           [16, 4096]             0       
   Linear-32        [[16, 4096]]           [16, 4096]        16,781,312   
    ReLU-80         [[16, 4096]]           [16, 4096]             0       
   Linear-33        [[16, 4096]]            [16, 2]             8,194     
============================================================================
Total params: 128,774,530
Trainable params: 128,774,530
Non-trainable params: 0
----------------------------------------------------------------------------
Input size (MB): 9.19
Forward/backward pass size (MB): 2007.94
Params size (MB): 491.24
Estimated Total Size (MB): 2508.36
----------------------------------------------------------------------------

{'total_params': 128774530, 'trainable_params': 128774530}

VGGNet11的训练和验证结果

Predict begin...step 10/10 [==============================] - 6ms/step        Predict samples: 10[1 1 0 1 0 0 0 1 0 1]

ResNet

大名鼎鼎的残差网络ResNet，残差块的提出使更深的网络模型成为可能，残差块的结构如图所示，自己动手搭建模型：图像分类篇 - php中文网增加了一个旁路连接，就使深层模型的训练成为可能。 ResNet模型的实现重点在于残差块的实现，这里单独定义一个残差块的类，在forward中

def forward(self,x):
   copy_x=x
   x=self.conv(x)   if self.in_c!=self.out_c:
		copy_x=self.w(copy_x)   return x+copy_x

分为两条路计算，一条是经典的卷积层，一条是旁路连接，同时如果卷积前后特征图通道数不同，则在旁路中补充一个卷积以调整。

In [16]

class Resblock(nn.Layer):

    def __init__(self,in_c,out_c,pool=False):
        super(Resblock,self).__init__()
        self.in_c,self.out_c=in_c,out_c        if pool==True:
            self.conv=nn.Sequential(nn.Conv2D(in_c,out_c,3,stride=2,padding=1),nn.Conv2D(out_c,out_c,3,padding=1),nn.BatchNorm(out_c))
            self.w=nn.Conv2D(in_c,out_c,kernel_size=1,stride=2)        else:
            self.conv=nn.Sequential(nn.Conv2D(in_c,out_c,3,padding=1),nn.Conv2D(out_c,out_c,3,padding=1),nn.BatchNorm(out_c))
            self.w=nn.Conv2D(in_c,out_c,kernel_size=1)    
    def forward(self,x):
        copy_x=x
        x=self.conv(x)        if self.in_c!=self.out_c:
            copy_x=self.w(copy_x)        return x+copy_x        
class ResNet(nn.Layer):
    
    def __init__(self):
        super().__init__()
        self.layers=nn.LayerList()
        self.layers.append(nn.Sequential(nn.Conv2D(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=1)))#,nn.MaxPool2D(2)
        for ind in range(3):
            self.layers.append(Resblock(64,64))
        self.layers.append(Resblock(64,128,True))        for ind in range(3):
            self.layers.append(Resblock(128,128))
        self.layers.append(Resblock(128,256,True))        for ind in range(5):
            self.layers.append(Resblock(256,256))
        self.layers.append(Resblock(256,512,True))        for ind in range(2):
            self.layers.append(Resblock(512,512))
        self.layers.append(nn.Sequential(nn.Flatten(),nn.Linear(100352,2)))    
    def forward(self,x):
        for layer in self.layers:
            x=layer(x)        return x

network=ResNet()
paddle.summary(network,(16,3,224,224))#使用paddle API验证结构正确性

---------------------------------------------------------------------------
 Layer (type)       Input Shape          Output Shape         Param #    
===========================================================================
   Conv2D-59    [[16, 3, 224, 224]]   [16, 64, 110, 110]       9,472     
   Conv2D-60    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
   Conv2D-61    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
  BatchNorm-1   [[16, 64, 110, 110]]  [16, 64, 110, 110]        256      
  Resblock-1    [[16, 64, 110, 110]]  [16, 64, 110, 110]         0       
   Conv2D-63    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
   Conv2D-64    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
  BatchNorm-2   [[16, 64, 110, 110]]  [16, 64, 110, 110]        256      
  Resblock-2    [[16, 64, 110, 110]]  [16, 64, 110, 110]         0       
   Conv2D-66    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
   Conv2D-67    [[16, 64, 110, 110]]  [16, 64, 110, 110]      36,928     
  BatchNorm-3   [[16, 64, 110, 110]]  [16, 64, 110, 110]        256      
  Resblock-3    [[16, 64, 110, 110]]  [16, 64, 110, 110]         0       
   Conv2D-69    [[16, 64, 110, 110]]  [16, 128, 55, 55]       73,856     
   Conv2D-70    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
  BatchNorm-4   [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
   Conv2D-71    [[16, 64, 110, 110]]  [16, 128, 55, 55]        8,320     
  Resblock-4    [[16, 64, 110, 110]]  [16, 128, 55, 55]          0       
   Conv2D-72    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
   Conv2D-73    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
  BatchNorm-5   [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
  Resblock-5    [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
   Conv2D-75    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
   Conv2D-76    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
  BatchNorm-6   [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
  Resblock-6    [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
   Conv2D-78    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
   Conv2D-79    [[16, 128, 55, 55]]   [16, 128, 55, 55]       147,584    
  BatchNorm-7   [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
  Resblock-7    [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
   Conv2D-81    [[16, 128, 55, 55]]   [16, 256, 28, 28]       295,168    
   Conv2D-82    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
  BatchNorm-8   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
   Conv2D-83    [[16, 128, 55, 55]]   [16, 256, 28, 28]       33,024     
  Resblock-8    [[16, 128, 55, 55]]   [16, 256, 28, 28]          0       
   Conv2D-84    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
   Conv2D-85    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
  BatchNorm-9   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
  Resblock-9    [[16, 256, 28, 28]]   [16, 256, 28, 28]          0       
   Conv2D-87    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
   Conv2D-88    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
 BatchNorm-10   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
  Resblock-10   [[16, 256, 28, 28]]   [16, 256, 28, 28]          0       
   Conv2D-90    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
   Conv2D-91    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
 BatchNorm-11   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
  Resblock-11   [[16, 256, 28, 28]]   [16, 256, 28, 28]          0       
   Conv2D-93    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
   Conv2D-94    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
 BatchNorm-12   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
  Resblock-12   [[16, 256, 28, 28]]   [16, 256, 28, 28]          0       
   Conv2D-96    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
   Conv2D-97    [[16, 256, 28, 28]]   [16, 256, 28, 28]       590,080    
 BatchNorm-13   [[16, 256, 28, 28]]   [16, 256, 28, 28]        1,024     
  Resblock-13   [[16, 256, 28, 28]]   [16, 256, 28, 28]          0       
   Conv2D-99    [[16, 256, 28, 28]]   [16, 512, 14, 14]      1,180,160   
  Conv2D-100    [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
 BatchNorm-14   [[16, 512, 14, 14]]   [16, 512, 14, 14]        2,048     
  Conv2D-101    [[16, 256, 28, 28]]   [16, 512, 14, 14]       131,584    
  Resblock-14   [[16, 256, 28, 28]]   [16, 512, 14, 14]          0       
  Conv2D-102    [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
  Conv2D-103    [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
 BatchNorm-15   [[16, 512, 14, 14]]   [16, 512, 14, 14]        2,048     
  Resblock-15   [[16, 512, 14, 14]]   [16, 512, 14, 14]          0       
  Conv2D-105    [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
  Conv2D-106    [[16, 512, 14, 14]]   [16, 512, 14, 14]      2,359,808   
 BatchNorm-16   [[16, 512, 14, 14]]   [16, 512, 14, 14]        2,048     
  Resblock-16   [[16, 512, 14, 14]]   [16, 512, 14, 14]          0       
  Flatten-21    [[16, 512, 14, 14]]      [16, 100352]            0       
   Linear-34       [[16, 100352]]          [16, 2]            200,706    
===========================================================================
Total params: 21,491,970
Trainable params: 21,476,866
Non-trainable params: 15,104
---------------------------------------------------------------------------
Input size (MB): 9.19
Forward/backward pass size (MB): 2816.42
Params size (MB): 81.99
Estimated Total Size (MB): 2907.59
---------------------------------------------------------------------------

{'total_params': 21491970, 'trainable_params': 21476866}

在模型训练和验证部分，验证结果如下

Predict begin...step 10/10 [==============================] - 13ms/step        Predict samples: 10[1 1 0 1 0 0 0 1 0 1]

DenseNet

DenseNet于2017被提出，作者没有根据业内的潮流对网络进行deeper/wider结构的改造，是对feature特征入手，在网络连通的前提下，对所有特征层进行了连接，在核心组件DenseBlock中，这一层的输入是之前所有层的输出，如图所示自己动手搭建模型：图像分类篇 - php中文网

在此基础上，作者构造了DenseNet，其结构如下自己动手搭建模型：图像分类篇 - php中文网

In [17]

#DenseNetclass DenseLayer(nn.Layer):
    
    def __init__(self,in_c,growth_rate,bn_size):
        super().__init__()
        out_c = growth_rate * bn_size
        self.layers=nn.Sequential(nn.BatchNorm2D(in_c),nn.ReLU(),nn.Conv2D(in_c,out_c,1),nn.BatchNorm2D(out_c),nn.ReLU(),nn.Conv2D(out_c,growth_rate,3,padding=1))    def forward(self,x):
        y = self.layers(x)        return yclass DenseBlock(nn.Layer):

    def __init__(self,num_layers,in_c,growth_rate,bn_size):
        super().__init__()
        self.layers=nn.LayerList()        for ind in range(num_layers):
            self.layers.append(
                DenseLayer(in_c=in_c+ind*growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size)
            )    
    def forward(self,x):
        features=[x]        for layer in self.layers:
            new_x=layer(paddle.concat(features,axis=1))
            features.append(new_x)        return paddle.concat(features,axis=1)class Transition(nn.Layer):

    def __init__(self,in_c,out_c):
        super().__init__()
        self.layers=nn.Sequential(nn.BatchNorm2D(in_c),nn.ReLU(),nn.Conv2D(in_c,out_c,1),nn.AvgPool2D(2,2))    
    def forward(self,x):
        return self.layers(x)class DenseNet(nn.Layer):
    
    def __init__(self,num_classes,growth_rate=32,block=(6,12,24,16),bn_size=4,out_c=64):
        super().__init__()
        self.conv_pool=nn.Sequential(nn.Conv2D(3,out_c,7,stride=2,padding=3),nn.MaxPool2D(3,2))
        self.blocks=nn.LayerList()
        in_c=out_c        for ind,n in enumerate(block):
            self.blocks.append(DenseBlock(n,in_c,growth_rate,bn_size))
            in_c+=growth_rate*n            if ind!=len(block)-1:
                self.blocks.append(Transition(in_c,in_c//2))
                in_c//=2
        self.blocks.append(nn.Sequential(nn.BatchNorm2D(in_c),nn.ReLU(),nn.AdaptiveAvgPool2D((1,1)),nn.Flatten()))
        self.cls=nn.Linear(in_c,num_classes)    
    def forward(self,x):
        x=self.conv_pool(x)        for layer in self.blocks:
            x=layer(x)
        x=self.cls(x)        return xdef _DenseNet(arch, block_cfg, batch_norm, pretrained, **kwargs):
    model = DenseNet(block=block_cfg,**kwargs)    if pretrained:        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
            arch)
        weight_path = get_weights_path_from_url(model_urls[arch][0],
                                                model_urls[arch][1])

        param = paddle.load(weight_path)
        model.load_dict(param)    return modeldef DenseNet121(pretrained=False, batch_norm=False, **kwargs):
    model_name = 'DenseNet121'
    if batch_norm:
        model_name += ('_bn')    return _DenseNet(model_name, (6,12,24,16), batch_norm, pretrained, **kwargs)def DenseNet161(pretrained=False, batch_norm=False, **kwargs):
    model_name = 'DenseNet161'
    if batch_norm:
        model_name += ('_bn')    return _DenseNet(model_name, (6,12,32,32), batch_norm, pretrained, **kwargs)def DenseNet169(pretrained=False, batch_norm=False, **kwargs):
    model_name = 'DenseNet169'
    if batch_norm:
        model_name += ('_bn')    return _DenseNet(model_name, (6,12,48,32), batch_norm, pretrained, **kwargs)def DenseNet201(pretrained=False, batch_norm=False, **kwargs):
    model_name = 'DenseNet201'
    if batch_norm:
        model_name += ('_bn')    return _DenseNet(model_name, (6,12,64,48), batch_norm, pretrained, **kwargs)


network=DenseNet(2)
paddle.summary(network,(16,3,224,224))#使用paddle API验证结构正确性

-------------------------------------------------------------------------------
   Layer (type)         Input Shape          Output Shape         Param #    
===============================================================================
    Conv2D-108      [[16, 3, 224, 224]]   [16, 64, 112, 112]       9,472     
   MaxPool2D-36     [[16, 64, 112, 112]]   [16, 64, 55, 55]          0       
   BatchNorm2D-1     [[16, 64, 55, 55]]    [16, 64, 55, 55]         256      
      ReLU-81        [[16, 64, 55, 55]]    [16, 64, 55, 55]          0       
    Conv2D-109       [[16, 64, 55, 55]]   [16, 128, 55, 55]        8,320     
   BatchNorm2D-2    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-82       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-110      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-1      [[16, 64, 55, 55]]    [16, 32, 55, 55]          0       
   BatchNorm2D-3     [[16, 96, 55, 55]]    [16, 96, 55, 55]         384      
      ReLU-83        [[16, 96, 55, 55]]    [16, 96, 55, 55]          0       
    Conv2D-111       [[16, 96, 55, 55]]   [16, 128, 55, 55]       12,416     
   BatchNorm2D-4    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-84       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-112      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-2      [[16, 96, 55, 55]]    [16, 32, 55, 55]          0       
   BatchNorm2D-5    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-85       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-113      [[16, 128, 55, 55]]   [16, 128, 55, 55]       16,512     
   BatchNorm2D-6    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-86       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-114      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-3     [[16, 128, 55, 55]]    [16, 32, 55, 55]          0       
   BatchNorm2D-7    [[16, 160, 55, 55]]   [16, 160, 55, 55]         640      
      ReLU-87       [[16, 160, 55, 55]]   [16, 160, 55, 55]          0       
    Conv2D-115      [[16, 160, 55, 55]]   [16, 128, 55, 55]       20,608     
   BatchNorm2D-8    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-88       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-116      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-4     [[16, 160, 55, 55]]    [16, 32, 55, 55]          0       
   BatchNorm2D-9    [[16, 192, 55, 55]]   [16, 192, 55, 55]         768      
      ReLU-89       [[16, 192, 55, 55]]   [16, 192, 55, 55]          0       
    Conv2D-117      [[16, 192, 55, 55]]   [16, 128, 55, 55]       24,704     
  BatchNorm2D-10    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-90       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-118      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-5     [[16, 192, 55, 55]]    [16, 32, 55, 55]          0       
  BatchNorm2D-11    [[16, 224, 55, 55]]   [16, 224, 55, 55]         896      
      ReLU-91       [[16, 224, 55, 55]]   [16, 224, 55, 55]          0       
    Conv2D-119      [[16, 224, 55, 55]]   [16, 128, 55, 55]       28,800     
  BatchNorm2D-12    [[16, 128, 55, 55]]   [16, 128, 55, 55]         512      
      ReLU-92       [[16, 128, 55, 55]]   [16, 128, 55, 55]          0       
    Conv2D-120      [[16, 128, 55, 55]]    [16, 32, 55, 55]       36,896     
   DenseLayer-6     [[16, 224, 55, 55]]    [16, 32, 55, 55]          0       
   DenseBlock-1      [[16, 64, 55, 55]]   [16, 256, 55, 55]          0       
  BatchNorm2D-13    [[16, 256, 55, 55]]   [16, 256, 55, 55]        1,024     
      ReLU-93       [[16, 256, 55, 55]]   [16, 256, 55, 55]          0       
    Conv2D-121      [[16, 256, 55, 55]]   [16, 128, 55, 55]       32,896     
    AvgPool2D-1     [[16, 128, 55, 55]]   [16, 128, 27, 27]          0       
   Transition-1     [[16, 256, 55, 55]]   [16, 128, 27, 27]          0       
  BatchNorm2D-14    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
      ReLU-94       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-122      [[16, 128, 27, 27]]   [16, 128, 27, 27]       16,512     
  BatchNorm2D-15    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
      ReLU-95       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-123      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-7     [[16, 128, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-16    [[16, 160, 27, 27]]   [16, 160, 27, 27]         640      
      ReLU-96       [[16, 160, 27, 27]]   [16, 160, 27, 27]          0       
    Conv2D-124124      [[16, 160, 27, 27]]   [16, 128, 27, 27]       20,608     
  BatchNorm2D-17    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
      ReLU-97       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-125      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-8     [[16, 160, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-18    [[16, 192, 27, 27]]   [16, 192, 27, 27]         768      
      ReLU-98       [[16, 192, 27, 27]]   [16, 192, 27, 27]          0       
    Conv2D-126      [[16, 192, 27, 27]]   [16, 128, 27, 27]       24,704     
  BatchNorm2D-19    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
      ReLU-99       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-127      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-9     [[16, 192, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-20    [[16, 224, 27, 27]]   [16, 224, 27, 27]         896      
     ReLU-100       [[16, 224, 27, 27]]   [16, 224, 27, 27]          0       
    Conv2D-128      [[16, 224, 27, 27]]   [16, 128, 27, 27]       28,800     
  BatchNorm2D-21    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-101       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-129      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-10    [[16, 224, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-22    [[16, 256, 27, 27]]   [16, 256, 27, 27]        1,024     
     ReLU-102       [[16, 256, 27, 27]]   [16, 256, 27, 27]          0       
    Conv2D-130      [[16, 256, 27, 27]]   [16, 128, 27, 27]       32,896     
  BatchNorm2D-23    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-103       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-131      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-11    [[16, 256, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-24    [[16, 288, 27, 27]]   [16, 288, 27, 27]        1,152     
     ReLU-104       [[16, 288, 27, 27]]   [16, 288, 27, 27]          0       
    Conv2D-132      [[16, 288, 27, 27]]   [16, 128, 27, 27]       36,992     
  BatchNorm2D-25    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-105       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-133      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-12    [[16, 288, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-26    [[16, 320, 27, 27]]   [16, 320, 27, 27]        1,280     
     ReLU-106       [[16, 320, 27, 27]]   [16, 320, 27, 27]          0       
    Conv2D-134      [[16, 320, 27, 27]]   [16, 128, 27, 27]       41,088     
  BatchNorm2D-27    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-107       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-135      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-13    [[16, 320, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-28    [[16, 352, 27, 27]]   [16, 352, 27, 27]        1,408     
     ReLU-108       [[16, 352, 27, 27]]   [16, 352, 27, 27]          0       
    Conv2D-136      [[16, 352, 27, 27]]   [16, 128, 27, 27]       45,184     
  BatchNorm2D-29    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-109       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-137      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-14    [[16, 352, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-30    [[16, 384, 27, 27]]   [16, 384, 27, 27]        1,536     
     ReLU-110       [[16, 384, 27, 27]]   [16, 384, 27, 27]          0       
    Conv2D-138      [[16, 384, 27, 27]]   [16, 128, 27, 27]       49,280     
  BatchNorm2D-31    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-111       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-139      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-15    [[16, 384, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-32    [[16, 416, 27, 27]]   [16, 416, 27, 27]        1,664     
     ReLU-112       [[16, 416, 27, 27]]   [16, 416, 27, 27]          0       
    Conv2D-140      [[16, 416, 27, 27]]   [16, 128, 27, 27]       53,376     
  BatchNorm2D-33    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-113       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-141      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-16    [[16, 416, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-34    [[16, 448, 27, 27]]   [16, 448, 27, 27]        1,792     
     ReLU-114       [[16, 448, 27, 27]]   [16, 448, 27, 27]          0       
    Conv2D-142      [[16, 448, 27, 27]]   [16, 128, 27, 27]       57,472     
  BatchNorm2D-35    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-115       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-143      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-17    [[16, 448, 27, 27]]    [16, 32, 27, 27]          0       
  BatchNorm2D-36    [[16, 480, 27, 27]]   [16, 480, 27, 27]        1,920     
     ReLU-116       [[16, 480, 27, 27]]   [16, 480, 27, 27]          0       
    Conv2D-144      [[16, 480, 27, 27]]   [16, 128, 27, 27]       61,568     
  BatchNorm2D-37    [[16, 128, 27, 27]]   [16, 128, 27, 27]         512      
     ReLU-117       [[16, 128, 27, 27]]   [16, 128, 27, 27]          0       
    Conv2D-145      [[16, 128, 27, 27]]    [16, 32, 27, 27]       36,896     
   DenseLayer-18    [[16, 480, 27, 27]]    [16, 32, 27, 27]          0       
   DenseBlock-2     [[16, 128, 27, 27]]   [16, 512, 27, 27]          0       
  BatchNorm2D-38    [[16, 512, 27, 27]]   [16, 512, 27, 27]        2,048     
     ReLU-118       [[16, 512, 27, 27]]   [16, 512, 27, 27]          0       
    Conv2D-146      [[16, 512, 27, 27]]   [16, 256, 27, 27]       131,328    
    AvgPool2D-2     [[16, 256, 27, 27]]   [16, 256, 13, 13]          0       
   Transition-2     [[16, 512, 27, 27]]   [16, 256, 13, 13]          0       
  BatchNorm2D-39    [[16, 256, 13, 13]]   [16, 256, 13, 13]        1,024     
     ReLU-119       [[16, 256, 13, 13]]   [16, 256, 13, 13]          0       
    Conv2D-147      [[16, 256, 13, 13]]   [16, 128, 13, 13]       32,896     
  BatchNorm2D-40    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-120       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-148      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-19    [[16, 256, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-41    [[16, 288, 13, 13]]   [16, 288, 13, 13]        1,152     
     ReLU-121       [[16, 288, 13, 13]]   [16, 288, 13, 13]          0       
    Conv2D-149      [[16, 288, 13, 13]]   [16, 128, 13, 13]       36,992     
  BatchNorm2D-42    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-122       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-150      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-20    [[16, 288, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-43    [[16, 320, 13, 13]]   [16, 320, 13, 13]        1,280     
     ReLU-123       [[16, 320, 13, 13]]   [16, 320, 13, 13]          0       
    Conv2D-151      [[16, 320, 13, 13]]   [16, 128, 13, 13]       41,088     
  BatchNorm2D-44    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-124124       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-152      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-21    [[16, 320, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-45    [[16, 352, 13, 13]]   [16, 352, 13, 13]        1,408     
     ReLU-125       [[16, 352, 13, 13]]   [16, 352, 13, 13]          0       
    Conv2D-153      [[16, 352, 13, 13]]   [16, 128, 13, 13]       45,184     
  BatchNorm2D-46    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-126       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-154      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-22    [[16, 352, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-47    [[16, 384, 13, 13]]   [16, 384, 13, 13]        1,536     
     ReLU-127       [[16, 384, 13, 13]]   [16, 384, 13, 13]          0       
    Conv2D-155      [[16, 384, 13, 13]]   [16, 128, 13, 13]       49,280     
  BatchNorm2D-48    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-128       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-156      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-23    [[16, 384, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-49    [[16, 416, 13, 13]]   [16, 416, 13, 13]        1,664     
     ReLU-129       [[16, 416, 13, 13]]   [16, 416, 13, 13]          0       
    Conv2D-157      [[16, 416, 13, 13]]   [16, 128, 13, 13]       53,376     
  BatchNorm2D-50    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-130       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-158      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-24    [[16, 416, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-51    [[16, 448, 13, 13]]   [16, 448, 13, 13]        1,792     
     ReLU-131       [[16, 448, 13, 13]]   [16, 448, 13, 13]          0       
    Conv2D-159      [[16, 448, 13, 13]]   [16, 128, 13, 13]       57,472     
  BatchNorm2D-52    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-132       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-160      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-25    [[16, 448, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-53    [[16, 480, 13, 13]]   [16, 480, 13, 13]        1,920     
     ReLU-133       [[16, 480, 13, 13]]   [16, 480, 13, 13]          0       
    Conv2D-161      [[16, 480, 13, 13]]   [16, 128, 13, 13]       61,568     
  BatchNorm2D-54    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-134       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-162      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-26    [[16, 480, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-55    [[16, 512, 13, 13]]   [16, 512, 13, 13]        2,048     
     ReLU-135       [[16, 512, 13, 13]]   [16, 512, 13, 13]          0       
    Conv2D-163      [[16, 512, 13, 13]]   [16, 128, 13, 13]       65,664     
  BatchNorm2D-56    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-136       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-164      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-27    [[16, 512, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-57    [[16, 544, 13, 13]]   [16, 544, 13, 13]        2,176     
     ReLU-137       [[16, 544, 13, 13]]   [16, 544, 13, 13]          0       
    Conv2D-165      [[16, 544, 13, 13]]   [16, 128, 13, 13]       69,760     
  BatchNorm2D-58    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-138       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-166      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-28    [[16, 544, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-59    [[16, 576, 13, 13]]   [16, 576, 13, 13]        2,304     
     ReLU-139       [[16, 576, 13, 13]]   [16, 576, 13, 13]          0       
    Conv2D-167      [[16, 576, 13, 13]]   [16, 128, 13, 13]       73,856     
  BatchNorm2D-60    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-140       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-168      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-29    [[16, 576, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-61    [[16, 608, 13, 13]]   [16, 608, 13, 13]        2,432     
     ReLU-141       [[16, 608, 13, 13]]   [16, 608, 13, 13]          0       
    Conv2D-169      [[16, 608, 13, 13]]   [16, 128, 13, 13]       77,952     
  BatchNorm2D-62    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-142       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-170      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-30    [[16, 608, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-63    [[16, 640, 13, 13]]   [16, 640, 13, 13]        2,560     
     ReLU-143       [[16, 640, 13, 13]]   [16, 640, 13, 13]          0       
    Conv2D-171      [[16, 640, 13, 13]]   [16, 128, 13, 13]       82,048     
  BatchNorm2D-64    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-144       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-172      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-31    [[16, 640, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-65    [[16, 672, 13, 13]]   [16, 672, 13, 13]        2,688     
     ReLU-145       [[16, 672, 13, 13]]   [16, 672, 13, 13]          0       
    Conv2D-173      [[16, 672, 13, 13]]   [16, 128, 13, 13]       86,144     
  BatchNorm2D-66    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-146       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-174      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-32    [[16, 672, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-67    [[16, 704, 13, 13]]   [16, 704, 13, 13]        2,816     
     ReLU-147       [[16, 704, 13, 13]]   [16, 704, 13, 13]          0       
    Conv2D-175      [[16, 704, 13, 13]]   [16, 128, 13, 13]       90,240     
  BatchNorm2D-68    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-148       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-176      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-33    [[16, 704, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-69    [[16, 736, 13, 13]]   [16, 736, 13, 13]        2,944     
     ReLU-149       [[16, 736, 13, 13]]   [16, 736, 13, 13]          0       
    Conv2D-177      [[16, 736, 13, 13]]   [16, 128, 13, 13]       94,336     
  BatchNorm2D-70    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-150       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-178      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-34    [[16, 736, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-71    [[16, 768, 13, 13]]   [16, 768, 13, 13]        3,072     
     ReLU-151       [[16, 768, 13, 13]]   [16, 768, 13, 13]          0       
    Conv2D-179      [[16, 768, 13, 13]]   [16, 128, 13, 13]       98,432     
  BatchNorm2D-72    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-152       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-180      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-35    [[16, 768, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-73    [[16, 800, 13, 13]]   [16, 800, 13, 13]        3,200     
     ReLU-153       [[16, 800, 13, 13]]   [16, 800, 13, 13]          0       
    Conv2D-181      [[16, 800, 13, 13]]   [16, 128, 13, 13]       102,528    
  BatchNorm2D-74    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-154       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-182      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-36    [[16, 800, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-75    [[16, 832, 13, 13]]   [16, 832, 13, 13]        3,328     
     ReLU-155       [[16, 832, 13, 13]]   [16, 832, 13, 13]          0       
    Conv2D-183      [[16, 832, 13, 13]]   [16, 128, 13, 13]       106,624    
  BatchNorm2D-76    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-156       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-184      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-37    [[16, 832, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-77    [[16, 864, 13, 13]]   [16, 864, 13, 13]        3,456     
     ReLU-157       [[16, 864, 13, 13]]   [16, 864, 13, 13]          0       
    Conv2D-185      [[16, 864, 13, 13]]   [16, 128, 13, 13]       110,720    
  BatchNorm2D-78    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-158       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-186      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-38    [[16, 864, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-79    [[16, 896, 13, 13]]   [16, 896, 13, 13]        3,584     
     ReLU-159       [[16, 896, 13, 13]]   [16, 896, 13, 13]          0       
    Conv2D-187      [[16, 896, 13, 13]]   [16, 128, 13, 13]       114,816    
  BatchNorm2D-80    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-160       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-188      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-39    [[16, 896, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-81    [[16, 928, 13, 13]]   [16, 928, 13, 13]        3,712     
     ReLU-161       [[16, 928, 13, 13]]   [16, 928, 13, 13]          0       
    Conv2D-189      [[16, 928, 13, 13]]   [16, 128, 13, 13]       118,912    
  BatchNorm2D-82    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-162       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-190      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-40    [[16, 928, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-83    [[16, 960, 13, 13]]   [16, 960, 13, 13]        3,840     
     ReLU-163       [[16, 960, 13, 13]]   [16, 960, 13, 13]          0       
    Conv2D-191      [[16, 960, 13, 13]]   [16, 128, 13, 13]       123,008    
  BatchNorm2D-84    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-164       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-192      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-41    [[16, 960, 13, 13]]    [16, 32, 13, 13]          0       
  BatchNorm2D-85    [[16, 992, 13, 13]]   [16, 992, 13, 13]        3,968     
     ReLU-165       [[16, 992, 13, 13]]   [16, 992, 13, 13]          0       
    Conv2D-193      [[16, 992, 13, 13]]   [16, 128, 13, 13]       127,104    
  BatchNorm2D-86    [[16, 128, 13, 13]]   [16, 128, 13, 13]         512      
     ReLU-166       [[16, 128, 13, 13]]   [16, 128, 13, 13]          0       
    Conv2D-194      [[16, 128, 13, 13]]    [16, 32, 13, 13]       36,896     
   DenseLayer-42    [[16, 992, 13, 13]]    [16, 32, 13, 13]          0       
   DenseBlock-3     [[16, 256, 13, 13]]   [16, 1024, 13, 13]         0       
  BatchNorm2D-87    [[16, 1024, 13, 13]]  [16, 1024, 13, 13]       4,096     
     ReLU-167       [[16, 1024, 13, 13]]  [16, 1024, 13, 13]         0       
    Conv2D-195      [[16, 1024, 13, 13]]  [16, 512, 13, 13]       524,800    
    AvgPool2D-3     [[16, 512, 13, 13]]    [16, 512, 6, 6]           0       
   Transition-3     [[16, 1024, 13, 13]]   [16, 512, 6, 6]           0       
  BatchNorm2D-88     [[16, 512, 6, 6]]     [16, 512, 6, 6]         2,048     
     ReLU-168        [[16, 512, 6, 6]]     [16, 512, 6, 6]           0       
    Conv2D-196       [[16, 512, 6, 6]]     [16, 128, 6, 6]        65,664     
  BatchNorm2D-89     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-169        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-197       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-43     [[16, 512, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-90     [[16, 544, 6, 6]]     [16, 544, 6, 6]         2,176     
     ReLU-170        [[16, 544, 6, 6]]     [16, 544, 6, 6]           0       
    Conv2D-198       [[16, 544, 6, 6]]     [16, 128, 6, 6]        69,760     
  BatchNorm2D-91     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-171        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-199       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-44     [[16, 544, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-92     [[16, 576, 6, 6]]     [16, 576, 6, 6]         2,304     
     ReLU-172        [[16, 576, 6, 6]]     [16, 576, 6, 6]           0       
    Conv2D-200       [[16, 576, 6, 6]]     [16, 128, 6, 6]        73,856     
  BatchNorm2D-93     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-173        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-201       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-45     [[16, 576, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-94     [[16, 608, 6, 6]]     [16, 608, 6, 6]         2,432     
     ReLU-174        [[16, 608, 6, 6]]     [16, 608, 6, 6]           0       
    Conv2D-202       [[16, 608, 6, 6]]     [16, 128, 6, 6]        77,952     
  BatchNorm2D-95     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-175        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-203       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-46     [[16, 608, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-96     [[16, 640, 6, 6]]     [16, 640, 6, 6]         2,560     
     ReLU-176        [[16, 640, 6, 6]]     [16, 640, 6, 6]           0       
    Conv2D-204       [[16, 640, 6, 6]]     [16, 128, 6, 6]        82,048     
  BatchNorm2D-97     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-177        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-205       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-47     [[16, 640, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-98     [[16, 672, 6, 6]]     [16, 672, 6, 6]         2,688     
     ReLU-178        [[16, 672, 6, 6]]     [16, 672, 6, 6]           0       
    Conv2D-206       [[16, 672, 6, 6]]     [16, 128, 6, 6]        86,144     
  BatchNorm2D-99     [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-179        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-207       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-48     [[16, 672, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-100    [[16, 704, 6, 6]]     [16, 704, 6, 6]         2,816     
     ReLU-180        [[16, 704, 6, 6]]     [16, 704, 6, 6]           0       
    Conv2D-208       [[16, 704, 6, 6]]     [16, 128, 6, 6]        90,240     
  BatchNorm2D-101    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-181        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-209       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-49     [[16, 704, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-102    [[16, 736, 6, 6]]     [16, 736, 6, 6]         2,944     
     ReLU-182        [[16, 736, 6, 6]]     [16, 736, 6, 6]           0       
    Conv2D-210       [[16, 736, 6, 6]]     [16, 128, 6, 6]        94,336     
  BatchNorm2D-103    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-183        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-211       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-50     [[16, 736, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-104    [[16, 768, 6, 6]]     [16, 768, 6, 6]         3,072     
     ReLU-184        [[16, 768, 6, 6]]     [16, 768, 6, 6]           0       
    Conv2D-212       [[16, 768, 6, 6]]     [16, 128, 6, 6]        98,432     
  BatchNorm2D-105    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-185        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-213       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-51     [[16, 768, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-106    [[16, 800, 6, 6]]     [16, 800, 6, 6]         3,200     
     ReLU-186        [[16, 800, 6, 6]]     [16, 800, 6, 6]           0       
    Conv2D-214       [[16, 800, 6, 6]]     [16, 128, 6, 6]        102,528    
  BatchNorm2D-107    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-187        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-215       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-52     [[16, 800, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-108    [[16, 832, 6, 6]]     [16, 832, 6, 6]         3,328     
     ReLU-188        [[16, 832, 6, 6]]     [16, 832, 6, 6]           0       
    Conv2D-216       [[16, 832, 6, 6]]     [16, 128, 6, 6]        106,624    
  BatchNorm2D-109    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-189        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-217       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-53     [[16, 832, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-110    [[16, 864, 6, 6]]     [16, 864, 6, 6]         3,456     
     ReLU-190        [[16, 864, 6, 6]]     [16, 864, 6, 6]           0       
    Conv2D-218       [[16, 864, 6, 6]]     [16, 128, 6, 6]        110,720    
  BatchNorm2D-111    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-191        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-219       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-54     [[16, 864, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-112    [[16, 896, 6, 6]]     [16, 896, 6, 6]         3,584     
     ReLU-192        [[16, 896, 6, 6]]     [16, 896, 6, 6]           0       
    Conv2D-220       [[16, 896, 6, 6]]     [16, 128, 6, 6]        114,816    
  BatchNorm2D-113    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-193        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-221       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-55     [[16, 896, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-114    [[16, 928, 6, 6]]     [16, 928, 6, 6]         3,712     
     ReLU-194        [[16, 928, 6, 6]]     [16, 928, 6, 6]           0       
    Conv2D-222       [[16, 928, 6, 6]]     [16, 128, 6, 6]        118,912    
  BatchNorm2D-115    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-195        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-223       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-56     [[16, 928, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-116    [[16, 960, 6, 6]]     [16, 960, 6, 6]         3,840     
     ReLU-196        [[16, 960, 6, 6]]     [16, 960, 6, 6]           0       
    Conv2D-224       [[16, 960, 6, 6]]     [16, 128, 6, 6]        123,008    
  BatchNorm2D-117    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-197        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-225       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-57     [[16, 960, 6, 6]]      [16, 32, 6, 6]           0       
  BatchNorm2D-118    [[16, 992, 6, 6]]     [16, 992, 6, 6]         3,968     
     ReLU-198        [[16, 992, 6, 6]]     [16, 992, 6, 6]           0       
    Conv2D-226       [[16, 992, 6, 6]]     [16, 128, 6, 6]        127,104    
  BatchNorm2D-119    [[16, 128, 6, 6]]     [16, 128, 6, 6]          512      
     ReLU-199        [[16, 128, 6, 6]]     [16, 128, 6, 6]           0       
    Conv2D-227       [[16, 128, 6, 6]]      [16, 32, 6, 6]        36,896     
   DenseLayer-58     [[16, 992, 6, 6]]      [16, 32, 6, 6]           0       
   DenseBlock-4      [[16, 512, 6, 6]]     [16, 1024, 6, 6]          0       
  BatchNorm2D-120    [[16, 1024, 6, 6]]    [16, 1024, 6, 6]        4,096     
     ReLU-200        [[16, 1024, 6, 6]]    [16, 1024, 6, 6]          0       
AdaptiveAvgPool2D-1  [[16, 1024, 6, 6]]    [16, 1024, 1, 1]          0       
    Flatten-23       [[16, 1024, 1, 1]]       [16, 1024]             0       
     Linear-35          [[16, 1024]]           [16, 2]             2,050     
===============================================================================
Total params: 7,049,538
Trainable params: 6,882,498
Non-trainable params: 167,040
-------------------------------------------------------------------------------
Input size (MB): 9.19
Forward/backward pass size (MB): 4472.80
Params size (MB): 26.89
Estimated Total Size (MB): 4508.88
-------------------------------------------------------------------------------

{'total_params': 7049538, 'trainable_params': 6882498}

模型训练

In [17]

model=paddle.Model(network)
lr=paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=lr_base,T_max=epochs)
opt=paddle.optimizer.Momentum(learning_rate=lr,parameters=model.parameters(),weight_decay=1e-2)
opt=paddle.optimizer.Adam(learning_rate=lr,parameters=model.parameters())
loss=paddle.nn.CrossEntropyLoss()#axis=1model.prepare(opt, loss,metrics=paddle.metric.Accuracy())
model.fit(train_loader, val_loader, epochs=epochs,verbose=2,save_dir="./net_params",log_freq=1)

The loss value printed in the log is the current step, and the metric is the average value of previous steps.
Epoch 1/10

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance.
  "When training, we now always track global mean and variance.")

step  1/40 - loss: 0.8786 - acc: 0.0000e+00 - 884ms/step
step  2/40 - loss: 1.3529 - acc: 0.3750 - 636ms/step
step  3/40 - loss: 4.8848 - acc: 0.3333 - 548ms/step
step  4/40 - loss: 0.6855 - acc: 0.4375 - 504ms/step
step  5/40 - loss: 1.3280 - acc: 0.4500 - 477ms/step
step  6/40 - loss: 1.4665 - acc: 0.4583 - 459ms/step
step  7/40 - loss: 0.2446 - acc: 0.5000 - 446ms/step
step  8/40 - loss: 0.1795 - acc: 0.5625 - 437ms/step
step  9/40 - loss: 0.1281 - acc: 0.6111 - 430ms/step
step 10/40 - loss: 0.7968 - acc: 0.6250 - 423ms/step
step 11/40 - loss: 2.5774 - acc: 0.5682 - 419ms/step
step 12/40 - loss: 0.2166 - acc: 0.6042 - 417ms/step
step 13/40 - loss: 2.4652 - acc: 0.5577 - 415ms/step
step 14/40 - loss: 1.1314 - acc: 0.5357 - 413ms/step
step 15/40 - loss: 0.6019 - acc: 0.5500 - 411ms/step
step 16/40 - loss: 1.0493 - acc: 0.5469 - 410ms/step
step 17/40 - loss: 0.3270 - acc: 0.5588 - 409ms/step
step 18/40 - loss: 0.1344 - acc: 0.5833 - 408ms/step
step 19/40 - loss: 0.9306 - acc: 0.5921 - 407ms/step
step 20/40 - loss: 0.0057 - acc: 0.6125 - 405ms/step
step 21/40 - loss: 0.1379 - acc: 0.6310 - 403ms/step
step 22/40 - loss: 0.0118 - acc: 0.6477 - 402ms/step
step 23/40 - loss: 0.0922 - acc: 0.6630 - 402ms/step
step 24/40 - loss: 0.0295 - acc: 0.6771 - 401ms/step
step 25/40 - loss: 0.8993 - acc: 0.6700 - 400ms/step
step 26/40 - loss: 0.6457 - acc: 0.6731 - 400ms/step
step 27/40 - loss: 0.0824 - acc: 0.6852 - 399ms/step
step 28/40 - loss: 0.1024 - acc: 0.6964 - 398ms/step
step 29/40 - loss: 0.1232 - acc: 0.7069 - 398ms/step
step 30/40 - loss: 0.5631 - acc: 0.7083 - 397ms/step
step 31/40 - loss: 0.0370 - acc: 0.7177 - 397ms/step
step 32/40 - loss: 0.2733 - acc: 0.7188 - 397ms/step
step 33/40 - loss: 6.6403 - acc: 0.7045 - 396ms/step
step 34/40 - loss: 0.1885 - acc: 0.7132 - 395ms/step
step 35/40 - loss: 0.0506 - acc: 0.7214 - 395ms/step
step 36/40 - loss: 3.8153 - acc: 0.7222 - 394ms/step
step 37/40 - loss: 5.5249 - acc: 0.7162 - 393ms/step
step 38/40 - loss: 0.5504 - acc: 0.7171 - 393ms/step
step 39/40 - loss: 0.9939 - acc: 0.7179 - 392ms/step
step 40/40 - loss: 0.9726 - acc: 0.7188 - 391ms/step
save checkpoint at /home/aistudio/net_params/0
Eval begin...
step  1/10 - loss: 0.8339 - acc: 0.7500 - 189ms/step
step  2/10 - loss: 3.0489 - acc: 0.5000 - 173ms/step
step  3/10 - loss: 6.1806 - acc: 0.4167 - 167ms/step
step  4/10 - loss: 2.5625 - acc: 0.4375 - 165ms/step
step  5/10 - loss: 6.9693 - acc: 0.4500 - 164ms/step
step  6/10 - loss: 7.5170 - acc: 0.4167 - 163ms/step
step  7/10 - loss: 2.3508 - acc: 0.3929 - 166ms/step
step  8/10 - loss: 4.7935 - acc: 0.4375 - 165ms/step
step  9/10 - loss: 9.8368 - acc: 0.3889 - 164ms/step
step 10/10 - loss: 4.2736 - acc: 0.4000 - 163ms/step
Eval samples: 40
Epoch 2/10
step  1/40 - loss: 0.1939 - acc: 1.0000 - 427ms/step
step  2/40 - loss: 0.1233 - acc: 1.0000 - 405ms/step
step  3/40 - loss: 0.1640 - acc: 1.0000 - 402ms/step
step  4/40 - loss: 0.2072 - acc: 1.0000 - 399ms/step
step  5/40 - loss: 0.0291 - acc: 1.0000 - 396ms/step
step  6/40 - loss: 0.5622 - acc: 0.9583 - 393ms/step
step  7/40 - loss: 1.7429 - acc: 0.8929 - 391ms/step
step  8/40 - loss: 0.0600 - acc: 0.9062 - 390ms/step
step  9/40 - loss: 0.0239 - acc: 0.9167 - 390ms/step
step 10/40 - loss: 0.0170 - acc: 0.9250 - 388ms/step
step 11/40 - loss: 0.4259 - acc: 0.9091 - 387ms/step
step 12/40 - loss: 0.3709 - acc: 0.8958 - 386ms/step
step 13/40 - loss: 0.2406 - acc: 0.9038 - 384ms/step
step 14/40 - loss: 0.0292 - acc: 0.9107 - 384ms/step
step 15/40 - loss: 0.8347 - acc: 0.9000 - 384ms/step
step 16/40 - loss: 0.0132 - acc: 0.9062 - 384ms/step
step 17/40 - loss: 0.1719 - acc: 0.9118 - 384ms/step
step 18/40 - loss: 0.1203 - acc: 0.9167 - 384ms/step
step 19/40 - loss: 0.1806 - acc: 0.9211 - 383ms/step
step 20/40 - loss: 0.0590 - acc: 0.9250 - 383ms/step
step 21/40 - loss: 0.7904 - acc: 0.9167 - 383ms/step
step 22/40 - loss: 0.7270 - acc: 0.9091 - 383ms/step
step 23/40 - loss: 0.0995 - acc: 0.9130 - 382ms/step
step 24/40 - loss: 0.6964 - acc: 0.9062 - 383ms/step
step 25/40 - loss: 0.0703 - acc: 0.9100 - 382ms/step
step 26/40 - loss: 0.2966 - acc: 0.9038 - 382ms/step
step 27/40 - loss: 0.0068 - acc: 0.9074 - 382ms/step
step 28/40 - loss: 3.3363 - acc: 0.9018 - 382ms/step
step 29/40 - loss: 0.0243 - acc: 0.9052 - 383ms/step
step 30/40 - loss: 0.0606 - acc: 0.9083 - 383ms/step
step 31/40 - loss: 0.5678 - acc: 0.9032 - 383ms/step
step 32/40 - loss: 0.0569 - acc: 0.9062 - 383ms/step
step 33/40 - loss: 0.0124124 - acc: 0.9091 - 383ms/step
step 34/40 - loss: 0.0041 - acc: 0.9118 - 383ms/step
step 35/40 - loss: 0.1410 - acc: 0.9143 - 383ms/step
step 36/40 - loss: 0.0177 - acc: 0.9167 - 382ms/step
step 37/40 - loss: 0.4238 - acc: 0.9122 - 382ms/step
step 38/40 - loss: 0.0226 - acc: 0.9145 - 382ms/step
step 39/40 - loss: 0.7144 - acc: 0.9103 - 381ms/step
step 40/40 - loss: 0.0323 - acc: 0.9125 - 381ms/step
save checkpoint at /home/aistudio/net_params/1
Eval begin...
step  1/10 - loss: 1.5320 - acc: 0.7500 - 190ms/step
step  2/10 - loss: 0.0654 - acc: 0.8750 - 173ms/step
step  3/10 - loss: 0.1166 - acc: 0.9167 - 167ms/step
step  4/10 - loss: 0.0883 - acc: 0.9375 - 164ms/step
step  5/10 - loss: 0.0637 - acc: 0.9500 - 162ms/step
step  6/10 - loss: 0.7424 - acc: 0.9167 - 160ms/step
step  7/10 - loss: 0.1827 - acc: 0.9286 - 159ms/step
step  8/10 - loss: 0.0102 - acc: 0.9375 - 159ms/step
step  9/10 - loss: 8.4945e-04 - acc: 0.9444 - 159ms/step
step 10/10 - loss: 0.0039 - acc: 0.9500 - 158ms/step
Eval samples: 40
Epoch 3/10
step  1/40 - loss: 0.0395 - acc: 1.0000 - 401ms/step
step  2/40 - loss: 0.1322 - acc: 1.0000 - 388ms/step
step  3/40 - loss: 0.0062 - acc: 1.0000 - 386ms/step
step  4/40 - loss: 0.0462 - acc: 1.0000 - 387ms/step
step  5/40 - loss: 1.5052 - acc: 0.9000 - 386ms/step
step  6/40 - loss: 0.0362 - acc: 0.9167 - 384ms/step
step  7/40 - loss: 0.1628 - acc: 0.9286 - 383ms/step
step  8/40 - loss: 0.1661 - acc: 0.9375 - 383ms/step
step  9/40 - loss: 1.4469 - acc: 0.9167 - 382ms/step
step 10/40 - loss: 0.4381 - acc: 0.9000 - 382ms/step
step 11/40 - loss: 0.4622 - acc: 0.8864 - 381ms/step
step 12/40 - loss: 1.9192 - acc: 0.8542 - 380ms/step
step 13/40 - loss: 0.1092 - acc: 0.8654 - 380ms/step
step 14/40 - loss: 0.0249 - acc: 0.8750 - 380ms/step
step 15/40 - loss: 0.1569 - acc: 0.8833 - 381ms/step
step 16/40 - loss: 0.6889 - acc: 0.8750 - 381ms/step
step 17/40 - loss: 1.1773 - acc: 0.8676 - 384ms/step
step 18/40 - loss: 0.1330 - acc: 0.8750 - 384ms/step
step 19/40 - loss: 0.0544 - acc: 0.8816 - 383ms/step
step 20/40 - loss: 0.1582 - acc: 0.8875 - 383ms/step
step 21/40 - loss: 0.1629 - acc: 0.8929 - 383ms/step
step 22/40 - loss: 0.2393 - acc: 0.8864 - 382ms/step
step 23/40 - loss: 0.2353 - acc: 0.8913 - 382ms/step
step 24/40 - loss: 0.1751 - acc: 0.8958 - 382ms/step
step 25/40 - loss: 0.0879 - acc: 0.9000 - 382ms/step
step 26/40 - loss: 0.0663 - acc: 0.9038 - 382ms/step
step 27/40 - loss: 1.2867 - acc: 0.8981 - 382ms/step
step 28/40 - loss: 0.0812 - acc: 0.9018 - 382ms/step
step 29/40 - loss: 0.0434 - acc: 0.9052 - 382ms/step
step 30/40 - loss: 0.9762 - acc: 0.9000 - 382ms/step
step 31/40 - loss: 0.9838 - acc: 0.8952 - 382ms/step
step 32/40 - loss: 0.0421 - acc: 0.8984 - 382ms/step
step 33/40 - loss: 0.0318 - acc: 0.9015 - 382ms/step
step 34/40 - loss: 0.0415 - acc: 0.9044 - 382ms/step
step 35/40 - loss: 0.7238 - acc: 0.9000 - 382ms/step
step 36/40 - loss: 0.0645 - acc: 0.9028 - 382ms/step
step 37/40 - loss: 0.2908 - acc: 0.8986 - 382ms/step
step 38/40 - loss: 0.0585 - acc: 0.9013 - 381ms/step
step 39/40 - loss: 0.1431 - acc: 0.9038 - 381ms/step
step 40/40 - loss: 0.2628 - acc: 0.9062 - 381ms/step
save checkpoint at /home/aistudio/net_params/2
Eval begin...
step  1/10 - loss: 0.6657 - acc: 0.7500 - 189ms/step
step  2/10 - loss: 0.2851 - acc: 0.8750 - 173ms/step
step  3/10 - loss: 0.1932 - acc: 0.9167 - 168ms/step
step  4/10 - loss: 0.1568 - acc: 0.9375 - 165ms/step
step  5/10 - loss: 0.2661 - acc: 0.9500 - 163ms/step
step  6/10 - loss: 0.3660 - acc: 0.9167 - 162ms/step
step  7/10 - loss: 0.1076 - acc: 0.9286 - 161ms/step
step  8/10 - loss: 0.3483 - acc: 0.9062 - 160ms/step
step  9/10 - loss: 0.0626 - acc: 0.9167 - 159ms/step
step 10/10 - loss: 2.5330 - acc: 0.9000 - 158ms/step
Eval samples: 40
Epoch 4/10
step  1/40 - loss: 0.0737 - acc: 1.0000 - 410ms/step
step  2/40 - loss: 0.1885 - acc: 1.0000 - 393ms/step
step  3/40 - loss: 0.2421 - acc: 1.0000 - 388ms/step
step  4/40 - loss: 0.3262 - acc: 1.0000 - 388ms/step
step  5/40 - loss: 0.1046 - acc: 1.0000 - 387ms/step
step  6/40 - loss: 0.3956 - acc: 0.9583 - 386ms/step
step  7/40 - loss: 0.0820 - acc: 0.9643 - 384ms/step
step  8/40 - loss: 0.0214 - acc: 0.9688 - 383ms/step
step  9/40 - loss: 2.7114 - acc: 0.9167 - 384ms/step
step 10/40 - loss: 0.3407 - acc: 0.9000 - 383ms/step
step 11/40 - loss: 0.1124124 - acc: 0.9091 - 383ms/step
step 12/40 - loss: 0.0579 - acc: 0.9167 - 383ms/step
step 13/40 - loss: 2.1480 - acc: 0.8846 - 383ms/step
step 14/40 - loss: 1.6564 - acc: 0.8393 - 385ms/step
step 15/40 - loss: 2.2699 - acc: 0.8167 - 390ms/step
step 16/40 - loss: 0.0320 - acc: 0.8281 - 390ms/step
step 17/40 - loss: 0.0675 - acc: 0.8382 - 390ms/step
step 18/40 - loss: 0.7838 - acc: 0.8333 - 391ms/step
step 19/40 - loss: 0.8567 - acc: 0.8289 - 391ms/step
step 20/40 - loss: 1.2302 - acc: 0.8125 - 392ms/step
step 21/40 - loss: 0.8249 - acc: 0.8095 - 392ms/step
step 22/40 - loss: 0.0762 - acc: 0.8182 - 391ms/step
step 23/40 - loss: 0.6335 - acc: 0.8152 - 391ms/step
step 24/40 - loss: 1.4432 - acc: 0.8125 - 390ms/step
step 25/40 - loss: 0.3524 - acc: 0.8100 - 390ms/step
step 26/40 - loss: 0.0775 - acc: 0.8173 - 389ms/step
step 27/40 - loss: 0.0042 - acc: 0.8241 - 389ms/step
step 28/40 - loss: 0.4740 - acc: 0.8214 - 389ms/step
step 29/40 - loss: 0.0156 - acc: 0.8276 - 389ms/step
step 30/40 - loss: 0.7265 - acc: 0.8167 - 389ms/step
step 31/40 - loss: 0.0151 - acc: 0.8226 - 389ms/step
step 32/40 - loss: 2.1609 - acc: 0.8047 - 389ms/step
step 33/40 - loss: 0.7140 - acc: 0.7955 - 388ms/step
step 34/40 - loss: 0.9281 - acc: 0.7794 - 388ms/step
step 35/40 - loss: 0.3517 - acc: 0.7786 - 388ms/step
step 36/40 - loss: 0.2083 - acc: 0.7778 - 387ms/step
step 37/40 - loss: 0.0585 - acc: 0.7838 - 387ms/step
step 38/40 - loss: 0.0416 - acc: 0.7895 - 386ms/step
step 39/40 - loss: 0.0436 - acc: 0.7949 - 386ms/step
step 40/40 - loss: 0.2235 - acc: 0.7937 - 386ms/step
save checkpoint at /home/aistudio/net_params/3
Eval begin...
step  1/10 - loss: 0.4787 - acc: 0.5000 - 193ms/step
step  2/10 - loss: 0.2900 - acc: 0.7500 - 176ms/step
step  3/10 - loss: 0.3595 - acc: 0.7500 - 169ms/step
step  4/10 - loss: 1.2558 - acc: 0.7500 - 166ms/step
step  5/10 - loss: 0.1971 - acc: 0.8000 - 164ms/step
step  6/10 - loss: 0.2971 - acc: 0.7917 - 162ms/step
step  7/10 - loss: 0.3777 - acc: 0.7857 - 160ms/step
step  8/10 - loss: 0.3725 - acc: 0.8125 - 159ms/step
step  9/10 - loss: 0.0464 - acc: 0.8333 - 159ms/step
step 10/10 - loss: 0.1397 - acc: 0.8500 - 159ms/step
Eval samples: 40
Epoch 5/10
step  1/40 - loss: 0.0323 - acc: 1.0000 - 419ms/step
step  2/40 - loss: 0.6679 - acc: 0.8750 - 409ms/step
step  3/40 - loss: 1.4082 - acc: 0.7500 - 405ms/step
step  4/40 - loss: 0.1318 - acc: 0.8125 - 403ms/step
step  5/40 - loss: 0.0193 - acc: 0.8500 - 401ms/step
step  6/40 - loss: 0.2944 - acc: 0.8333 - 397ms/step
step  7/40 - loss: 6.1585 - acc: 0.7857 - 394ms/step
step  8/40 - loss: 0.5659 - acc: 0.7500 - 392ms/step
step  9/40 - loss: 0.7713 - acc: 0.7500 - 390ms/step
step 10/40 - loss: 1.2466 - acc: 0.7250 - 389ms/step
step 11/40 - loss: 5.7933 - acc: 0.7045 - 388ms/step
step 12/40 - loss: 0.0074 - acc: 0.7292 - 388ms/step
step 13/40 - loss: 0.3855 - acc: 0.7308 - 389ms/step
step 14/40 - loss: 0.2039 - acc: 0.7500 - 389ms/step
step 15/40 - loss: 0.1062 - acc: 0.7667 - 388ms/step
step 16/40 - loss: 0.0897 - acc: 0.7812 - 388ms/step
step 17/40 - loss: 0.0531 - acc: 0.7941 - 388ms/step
step 18/40 - loss: 0.0057 - acc: 0.8056 - 387ms/step
step 19/40 - loss: 3.8333 - acc: 0.7763 - 387ms/step
step 20/40 - loss: 9.8705e-04 - acc: 0.7875 - 387ms/step
step 21/40 - loss: 0.0078 - acc: 0.7976 - 387ms/step
step 22/40 - loss: 0.1706 - acc: 0.8068 - 387ms/step
step 23/40 - loss: 0.0057 - acc: 0.8152 - 387ms/step
step 24/40 - loss: 0.7214 - acc: 0.8125 - 387ms/step
step 25/40 - loss: 2.2940 - acc: 0.8000 - 387ms/step
step 26/40 - loss: 0.1119 - acc: 0.8077 - 387ms/step
step 27/40 - loss: 0.1626 - acc: 0.8148 - 387ms/step
step 28/40 - loss: 0.1642 - acc: 0.8214 - 387ms/step
step 29/40 - loss: 1.4720 - acc: 0.8190 - 386ms/step
step 30/40 - loss: 0.1344 - acc: 0.8250 - 386ms/step
step 31/40 - loss: 0.0357 - acc: 0.8306 - 386ms/step
step 32/40 - loss: 0.9776 - acc: 0.8281 - 386ms/step
step 33/40 - loss: 0.0590 - acc: 0.8333 - 385ms/step
step 34/40 - loss: 0.0067 - acc: 0.8382 - 385ms/step
step 35/40 - loss: 0.0716 - acc: 0.8429 - 385ms/step
step 36/40 - loss: 1.0589 - acc: 0.8403 - 385ms/step
step 37/40 - loss: 1.0528 - acc: 0.8243 - 385ms/step
step 38/40 - loss: 0.1098 - acc: 0.8289 - 385ms/step
step 39/40 - loss: 1.0890 - acc: 0.8141 - 385ms/step
step 40/40 - loss: 0.0311 - acc: 0.8187 - 385ms/step
save checkpoint at /home/aistudio/net_params/4
Eval begin...
step  1/10 - loss: 0.6312 - acc: 0.7500 - 190ms/step
step  2/10 - loss: 0.0878 - acc: 0.8750 - 174ms/step
step  3/10 - loss: 0.3535 - acc: 0.8333 - 167ms/step
step  4/10 - loss: 0.6739 - acc: 0.8125 - 164ms/step
step  5/10 - loss: 0.4551 - acc: 0.8000 - 162ms/step
step  6/10 - loss: 0.2485 - acc: 0.8333 - 161ms/step
step  7/10 - loss: 0.0581 - acc: 0.8571 - 159ms/step
step  8/10 - loss: 1.1049 - acc: 0.8438 - 158ms/step
step  9/10 - loss: 0.7219 - acc: 0.8333 - 157ms/step
step 10/10 - loss: 0.0683 - acc: 0.8500 - 157ms/step
Eval samples: 40
Epoch 6/10
step  1/40 - loss: 0.0995 - acc: 1.0000 - 409ms/step
step  2/40 - loss: 0.0347 - acc: 1.0000 - 393ms/step
step  3/40 - loss: 0.0175 - acc: 1.0000 - 387ms/step
step  4/40 - loss: 0.0142 - acc: 1.0000 - 384ms/step
step  5/40 - loss: 0.0089 - acc: 1.0000 - 382ms/step
step  6/40 - loss: 0.0130 - acc: 1.0000 - 382ms/step
step  7/40 - loss: 0.0065 - acc: 1.0000 - 381ms/step
step  8/40 - loss: 0.0886 - acc: 1.0000 - 381ms/step
step  9/40 - loss: 0.0057 - acc: 1.0000 - 382ms/step
step 10/40 - loss: 0.4009 - acc: 0.9750 - 383ms/step
step 11/40 - loss: 0.0063 - acc: 0.9773 - 383ms/step
step 12/40 - loss: 0.1222 - acc: 0.9792 - 385ms/step
step 13/40 - loss: 0.2443 - acc: 0.9615 - 384ms/step
step 14/40 - loss: 0.0042 - acc: 0.9643 - 383ms/step
step 15/40 - loss: 0.1865 - acc: 0.9500 - 383ms/step
step 16/40 - loss: 0.2686 - acc: 0.9375 - 384ms/step
step 17/40 - loss: 0.0906 - acc: 0.9412 - 384ms/step
step 18/40 - loss: 2.0408 - acc: 0.9028 - 383ms/step
step 19/40 - loss: 2.7789 - acc: 0.8816 - 383ms/step
step 20/40 - loss: 1.5114 - acc: 0.8625 - 382ms/step
step 21/40 - loss: 0.0674 - acc: 0.8690 - 382ms/step
step 22/40 - loss: 0.2582 - acc: 0.8750 - 382ms/step
step 23/40 - loss: 0.2616 - acc: 0.8804 - 382ms/step
step 24/40 - loss: 0.9854 - acc: 0.8750 - 382ms/step
step 25/40 - loss: 0.5641 - acc: 0.8700 - 383ms/step
step 26/40 - loss: 0.2137 - acc: 0.8654 - 383ms/step
step 27/40 - loss: 1.1425 - acc: 0.8519 - 383ms/step
step 28/40 - loss: 2.1342 - acc: 0.8214 - 383ms/step
step 29/40 - loss: 0.1785 - acc: 0.8276 - 383ms/step
step 30/40 - loss: 0.1765 - acc: 0.8333 - 383ms/step
step 31/40 - loss: 0.3265 - acc: 0.8306 - 383ms/step
step 32/40 - loss: 0.1236 - acc: 0.8359 - 383ms/step
step 33/40 - loss: 0.0458 - acc: 0.8409 - 382ms/step
step 34/40 - loss: 0.1007 - acc: 0.8456 - 383ms/step
step 35/40 - loss: 0.0287 - acc: 0.8500 - 382ms/step
step 36/40 - loss: 0.6687 - acc: 0.8403 - 383ms/step
step 37/40 - loss: 0.1288 - acc: 0.8446 - 383ms/step
step 38/40 - loss: 0.3275 - acc: 0.8421 - 383ms/step
step 39/40 - loss: 0.0299 - acc: 0.8462 - 383ms/step
step 40/40 - loss: 0.0082 - acc: 0.8500 - 382ms/step
save checkpoint at /home/aistudio/net_params/5
Eval begin...
step  1/10 - loss: 0.6713 - acc: 0.7500 - 190ms/step
step  2/10 - loss: 0.0364 - acc: 0.8750 - 173ms/step
step  3/10 - loss: 0.1046 - acc: 0.9167 - 167ms/step
step  4/10 - loss: 0.0863 - acc: 0.9375 - 164ms/step
step  5/10 - loss: 0.0156 - acc: 0.9500 - 162ms/step
step  6/10 - loss: 0.3814 - acc: 0.9167 - 160ms/step
step  7/10 - loss: 0.1301 - acc: 0.9286 - 159ms/step
step  8/10 - loss: 0.1887 - acc: 0.9375 - 158ms/step
step  9/10 - loss: 5.1071e-04 - acc: 0.9444 - 157ms/step
step 10/10 - loss: 0.0056 - acc: 0.9500 - 156ms/step
Eval samples: 40
Epoch 7/10
step  1/40 - loss: 0.0386 - acc: 1.0000 - 405ms/step
step  2/40 - loss: 0.0222 - acc: 1.0000 - 390ms/step
step  3/40 - loss: 0.0136 - acc: 1.0000 - 385ms/step
step  4/40 - loss: 0.0808 - acc: 1.0000 - 383ms/step
step  5/40 - loss: 0.2800 - acc: 0.9500 - 382ms/step
step  6/40 - loss: 3.6075 - acc: 0.8750 - 382ms/step
step  7/40 - loss: 0.0278 - acc: 0.8929 - 381ms/step
step  8/40 - loss: 0.2057 - acc: 0.9062 - 382ms/step
step  9/40 - loss: 0.0269 - acc: 0.9167 - 383ms/step
step 10/40 - loss: 0.0028 - acc: 0.9250 - 384ms/step
step 11/40 - loss: 0.7834 - acc: 0.9091 - 384ms/step
step 12/40 - loss: 0.5458 - acc: 0.8958 - 384ms/step
step 13/40 - loss: 0.1274 - acc: 0.9038 - 383ms/step
step 14/40 - loss: 0.0356 - acc: 0.9107 - 383ms/step
step 15/40 - loss: 0.0309 - acc: 0.9167 - 383ms/step
step 16/40 - loss: 0.0779 - acc: 0.9219 - 384ms/step
step 17/40 - loss: 0.2377 - acc: 0.9118 - 384ms/step
step 18/40 - loss: 0.0049 - acc: 0.9167 - 385ms/step
step 19/40 - loss: 0.2741 - acc: 0.9079 - 386ms/step
step 20/40 - loss: 0.0322 - acc: 0.9125 - 386ms/step
step 21/40 - loss: 0.7266 - acc: 0.9048 - 387ms/step
step 22/40 - loss: 0.0301 - acc: 0.9091 - 387ms/step
step 23/40 - loss: 0.0418 - acc: 0.9130 - 390ms/step
step 24/40 - loss: 0.0183 - acc: 0.9167 - 390ms/step
step 25/40 - loss: 0.0116 - acc: 0.9200 - 389ms/step
step 26/40 - loss: 0.0014 - acc: 0.9231 - 390ms/step
step 27/40 - loss: 0.0010 - acc: 0.9259 - 389ms/step
step 28/40 - loss: 0.0080 - acc: 0.9286 - 389ms/step
step 29/40 - loss: 0.8775 - acc: 0.9224 - 389ms/step
step 30/40 - loss: 1.5853 - acc: 0.9167 - 389ms/step
step 31/40 - loss: 0.0105 - acc: 0.9194 - 389ms/step
step 32/40 - loss: 0.1602 - acc: 0.9219 - 389ms/step
step 33/40 - loss: 0.0230 - acc: 0.9242 - 388ms/step
step 34/40 - loss: 0.0125 - acc: 0.9265 - 388ms/step
step 35/40 - loss: 0.9842 - acc: 0.9214 - 388ms/step
step 36/40 - loss: 0.3961 - acc: 0.9167 - 388ms/step
step 37/40 - loss: 0.0035 - acc: 0.9189 - 388ms/step
step 38/40 - loss: 0.0021 - acc: 0.9211 - 388ms/step
step 39/40 - loss: 0.0378 - acc: 0.9231 - 388ms/step
step 40/40 - loss: 0.0681 - acc: 0.9250 - 388ms/step
save checkpoint at /home/aistudio/net_params/6
Eval begin...
step  1/10 - loss: 1.1088 - acc: 0.7500 - 191ms/step
step  2/10 - loss: 0.0657 - acc: 0.8750 - 175ms/step
step  3/10 - loss: 0.3504 - acc: 0.8333 - 169ms/step
step  4/10 - loss: 0.3822 - acc: 0.8125 - 166ms/step
step  5/10 - loss: 0.0095 - acc: 0.8500 - 164ms/step
step  6/10 - loss: 0.1725 - acc: 0.8750 - 163ms/step
step  7/10 - loss: 0.0494 - acc: 0.8929 - 162ms/step
step  8/10 - loss: 0.0705 - acc: 0.9062 - 162ms/step
step  9/10 - loss: 0.0017 - acc: 0.9167 - 162ms/step
step 10/10 - loss: 0.1170 - acc: 0.9250 - 161ms/step
Eval samples: 40
Epoch 8/10
step  1/40 - loss: 0.0471 - acc: 1.0000 - 414ms/step
step  2/40 - loss: 0.1036 - acc: 1.0000 - 405ms/step
step  3/40 - loss: 0.0255 - acc: 1.0000 - 398ms/step
step  4/40 - loss: 0.0952 - acc: 1.0000 - 396ms/step
step  5/40 - loss: 0.0220 - acc: 1.0000 - 392ms/step
step  6/40 - loss: 0.0714 - acc: 1.0000 - 390ms/step
step  7/40 - loss: 0.1415 - acc: 1.0000 - 388ms/step
step  8/40 - loss: 0.0573 - acc: 1.0000 - 387ms/step
step  9/40 - loss: 0.4687 - acc: 0.9722 - 388ms/step
step 10/40 - loss: 0.0601 - acc: 0.9750 - 388ms/step
step 11/40 - loss: 3.3628 - acc: 0.9318 - 388ms/step
step 12/40 - loss: 0.1056 - acc: 0.9375 - 387ms/step
step 13/40 - loss: 0.0047 - acc: 0.9423 - 387ms/step
step 14/40 - loss: 0.0695 - acc: 0.9464 - 386ms/step
step 15/40 - loss: 0.0321 - acc: 0.9500 - 385ms/step
step 16/40 - loss: 0.0046 - acc: 0.9531 - 385ms/step
step 17/40 - loss: 0.2005 - acc: 0.9559 - 385ms/step
step 18/40 - loss: 0.0053 - acc: 0.9583 - 384ms/step
step 19/40 - loss: 2.9423 - acc: 0.9342 - 384ms/step
step 20/40 - loss: 0.0326 - acc: 0.9375 - 384ms/step
step 21/40 - loss: 0.0439 - acc: 0.9405 - 384ms/step
step 22/40 - loss: 0.3001 - acc: 0.9318 - 386ms/step
step 23/40 - loss: 0.4708 - acc: 0.9239 - 386ms/step
step 24/40 - loss: 0.1299 - acc: 0.9271 - 386ms/step
step 25/40 - loss: 0.3625 - acc: 0.9200 - 385ms/step
step 26/40 - loss: 0.3287 - acc: 0.9135 - 385ms/step
step 27/40 - loss: 0.0549 - acc: 0.9167 - 385ms/step
step 28/40 - loss: 0.3235 - acc: 0.9107 - 384ms/step
step 29/40 - loss: 0.0694 - acc: 0.9138 - 384ms/step
step 30/40 - loss: 0.0509 - acc: 0.9167 - 384ms/step
step 31/40 - loss: 0.0484 - acc: 0.9194 - 384ms/step
step 32/40 - loss: 0.0848 - acc: 0.9219 - 384ms/step
step 33/40 - loss: 0.8888 - acc: 0.9091 - 384ms/step
step 34/40 - loss: 0.1689 - acc: 0.9118 - 383ms/step
step 35/40 - loss: 0.1079 - acc: 0.9143 - 384ms/step
step 36/40 - loss: 1.2262 - acc: 0.8958 - 384ms/step
step 37/40 - loss: 0.0499 - acc: 0.8986 - 384ms/step
step 38/40 - loss: 0.0687 - acc: 0.9013 - 383ms/step
step 39/40 - loss: 0.1622 - acc: 0.9038 - 383ms/step
step 40/40 - loss: 1.3731 - acc: 0.8938 - 383ms/step
save checkpoint at /home/aistudio/net_params/7
Eval begin...
step  1/10 - loss: 0.6370 - acc: 0.7500 - 189ms/step
step  2/10 - loss: 0.1336 - acc: 0.8750 - 173ms/step
step  3/10 - loss: 0.2266 - acc: 0.8333 - 166ms/step
step  4/10 - loss: 0.6489 - acc: 0.8125 - 163ms/step
step  5/10 - loss: 0.0253 - acc: 0.8500 - 161ms/step
step  6/10 - loss: 0.1293 - acc: 0.8750 - 160ms/step
step  7/10 - loss: 0.1190 - acc: 0.8929 - 158ms/step
step  8/10 - loss: 0.1791 - acc: 0.9062 - 157ms/step
step  9/10 - loss: 0.0681 - acc: 0.9167 - 157ms/step
step 10/10 - loss: 0.2486 - acc: 0.9000 - 156ms/step
Eval samples: 40
Epoch 9/10
step  1/40 - loss: 0.1473 - acc: 1.0000 - 412ms/step
step  2/40 - loss: 0.0333 - acc: 1.0000 - 395ms/step
step  3/40 - loss: 0.1590 - acc: 1.0000 - 392ms/step
step  4/40 - loss: 0.1155 - acc: 1.0000 - 388ms/step
step  5/40 - loss: 0.1285 - acc: 1.0000 - 385ms/step
step  6/40 - loss: 0.0225 - acc: 1.0000 - 383ms/step
step  7/40 - loss: 0.0925 - acc: 1.0000 - 382ms/step
step  8/40 - loss: 0.0555 - acc: 1.0000 - 383ms/step
step  9/40 - loss: 0.1942 - acc: 1.0000 - 383ms/step
step 10/40 - loss: 0.1221 - acc: 1.0000 - 385ms/step
step 11/40 - loss: 0.1720 - acc: 1.0000 - 385ms/step
step 12/40 - loss: 0.1029 - acc: 1.0000 - 385ms/step
step 13/40 - loss: 0.3487 - acc: 0.9808 - 385ms/step
step 14/40 - loss: 0.0366 - acc: 0.9821 - 384ms/step
step 15/40 - loss: 0.1332 - acc: 0.9833 - 384ms/step
step 16/40 - loss: 0.1053 - acc: 0.9844 - 383ms/step
step 17/40 - loss: 0.8791 - acc: 0.9706 - 384ms/step
step 18/40 - loss: 0.0092 - acc: 0.9722 - 383ms/step
step 19/40 - loss: 0.1165 - acc: 0.9737 - 384ms/step
step 20/40 - loss: 0.0231 - acc: 0.9750 - 384ms/step
step 21/40 - loss: 0.0517 - acc: 0.9762 - 386ms/step
step 22/40 - loss: 1.2441 - acc: 0.9545 - 386ms/step
step 23/40 - loss: 0.0475 - acc: 0.9565 - 386ms/step
step 24/40 - loss: 0.0250 - acc: 0.9583 - 385ms/step
step 25/40 - loss: 1.8250 - acc: 0.9300 - 385ms/step
step 26/40 - loss: 1.5704 - acc: 0.9135 - 385ms/step
step 27/40 - loss: 0.0336 - acc: 0.9167 - 385ms/step
step 28/40 - loss: 0.1095 - acc: 0.9196 - 385ms/step
step 29/40 - loss: 0.0183 - acc: 0.9224 - 385ms/step
step 30/40 - loss: 1.0537 - acc: 0.9000 - 384ms/step
step 31/40 - loss: 0.0366 - acc: 0.9032 - 384ms/step
step 32/40 - loss: 0.3328 - acc: 0.8984 - 384ms/step
step 33/40 - loss: 1.4123 - acc: 0.8864 - 384ms/step
step 34/40 - loss: 0.0306 - acc: 0.8897 - 384ms/step
step 35/40 - loss: 0.1041 - acc: 0.8929 - 384ms/step
step 36/40 - loss: 0.2017 - acc: 0.8889 - 384ms/step
step 37/40 - loss: 0.0764 - acc: 0.8919 - 384ms/step
step 38/40 - loss: 0.4936 - acc: 0.8816 - 384ms/step
step 39/40 - loss: 0.0707 - acc: 0.8846 - 384ms/step
step 40/40 - loss: 0.2644 - acc: 0.8812 - 384ms/step
save checkpoint at /home/aistudio/net_params/8
Eval begin...
step  1/10 - loss: 0.5388 - acc: 0.7500 - 191ms/step
step  2/10 - loss: 0.1467 - acc: 0.8750 - 174ms/step
step  3/10 - loss: 0.3554 - acc: 0.8333 - 167ms/step
step  4/10 - loss: 0.9144 - acc: 0.8125 - 164ms/step
step  5/10 - loss: 0.0390 - acc: 0.8500 - 162ms/step
step  6/10 - loss: 0.1069 - acc: 0.8750 - 161ms/step
step  7/10 - loss: 0.0991 - acc: 0.8929 - 160ms/step
step  8/10 - loss: 0.3669 - acc: 0.8750 - 159ms/step
step  9/10 - loss: 0.0068 - acc: 0.8889 - 158ms/step
step 10/10 - loss: 0.1100 - acc: 0.9000 - 158ms/step
Eval samples: 40
Epoch 10/10
step  1/40 - loss: 0.3692 - acc: 0.7500 - 411ms/step
step  2/40 - loss: 0.0414 - acc: 0.8750 - 396ms/step
step  3/40 - loss: 0.3528 - acc: 0.8333 - 390ms/step
step  4/40 - loss: 1.5622 - acc: 0.6875 - 389ms/step
step  5/40 - loss: 1.3839 - acc: 0.6500 - 387ms/step
step  6/40 - loss: 0.0628 - acc: 0.7083 - 386ms/step
step  7/40 - loss: 0.0734 - acc: 0.7500 - 386ms/step
step  8/40 - loss: 0.1471 - acc: 0.7812 - 386ms/step
step  9/40 - loss: 0.1939 - acc: 0.7778 - 385ms/step
step 10/40 - loss: 0.0424 - acc: 0.8000 - 385ms/step
step 11/40 - loss: 0.0248 - acc: 0.8182 - 385ms/step
step 12/40 - loss: 0.1522 - acc: 0.8333 - 384ms/step
step 13/40 - loss: 0.0541 - acc: 0.8462 - 383ms/step
step 14/40 - loss: 0.2955 - acc: 0.8571 - 384ms/step
step 15/40 - loss: 0.0316 - acc: 0.8667 - 384ms/step
step 16/40 - loss: 0.1238 - acc: 0.8750 - 384ms/step
step 17/40 - loss: 0.0661 - acc: 0.8824 - 383ms/step
step 18/40 - loss: 1.5378 - acc: 0.8472 - 383ms/step
step 19/40 - loss: 0.0806 - acc: 0.8553 - 383ms/step
step 20/40 - loss: 1.9089 - acc: 0.8250 - 384ms/step
step 21/40 - loss: 0.1667 - acc: 0.8333 - 385ms/step
step 22/40 - loss: 0.0567 - acc: 0.8409 - 386ms/step
step 23/40 - loss: 0.0559 - acc: 0.8478 - 386ms/step
step 24/40 - loss: 0.1250 - acc: 0.8542 - 386ms/step
step 25/40 - loss: 0.7509 - acc: 0.8300 - 387ms/step
step 26/40 - loss: 0.0650 - acc: 0.8365 - 387ms/step
step 27/40 - loss: 0.2121 - acc: 0.8426 - 388ms/step
step 28/40 - loss: 0.9168 - acc: 0.8304 - 388ms/step
step 29/40 - loss: 0.3841 - acc: 0.8276 - 388ms/step
step 30/40 - loss: 0.2089 - acc: 0.8333 - 388ms/step
step 31/40 - loss: 0.5309 - acc: 0.8306 - 387ms/step
step 32/40 - loss: 0.0864 - acc: 0.8359 - 387ms/step
step 33/40 - loss: 0.8717 - acc: 0.8258 - 386ms/step
step 34/40 - loss: 0.0905 - acc: 0.8309 - 387ms/step
step 35/40 - loss: 0.4231 - acc: 0.8214 - 386ms/step
step 36/40 - loss: 0.7544 - acc: 0.8125 - 386ms/step
step 37/40 - loss: 0.0584 - acc: 0.8176 - 386ms/step
step 38/40 - loss: 0.1380 - acc: 0.8224 - 386ms/step
step 39/40 - loss: 0.3090 - acc: 0.8269 - 386ms/step
step 40/40 - loss: 0.1033 - acc: 0.8313 - 385ms/step
save checkpoint at /home/aistudio/net_params/9
Eval begin...
step  1/10 - loss: 0.6522 - acc: 0.7500 - 191ms/step
step  2/10 - loss: 0.2859 - acc: 0.8750 - 174ms/step
step  3/10 - loss: 0.2603 - acc: 0.8333 - 168ms/step
step  4/10 - loss: 0.3827 - acc: 0.8125 - 165ms/step
step  5/10 - loss: 0.1566 - acc: 0.8500 - 163ms/step
step  6/10 - loss: 0.3308 - acc: 0.8333 - 161ms/step
step  7/10 - loss: 0.2705 - acc: 0.8571 - 160ms/step
step  8/10 - loss: 0.1988 - acc: 0.8750 - 159ms/step
step  9/10 - loss: 0.1806 - acc: 0.8889 - 159ms/step
step 10/10 - loss: 0.2267 - acc: 0.8750 - 159ms/step
Eval samples: 40
save checkpoint at /home/aistudio/net_params/final

模型验证

In [19]

test_set=DatasetN("test_list.txt",T.Compose([T.Normalize(data_format="CHW")]))
model.load("net_params/final")
test_result=model.predict(test_set)[0]print(np.argmax(test_result,axis=2).flatten())#1 1 0 1 0 0 0 1 0 1

Predict begin...
step 10/10 [==============================] - 153ms/step        
Predict samples: 10
[1 1 1 1 0 0 0 1 1 1]

In [20]

!rm -r net_params/*

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