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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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。
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
lr_base=1e-3epochs=10batch_size=4image_size=(256,256)
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
!unzip -qo dataset.zip
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)#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)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
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。
#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
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
#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]
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，
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。
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
在此基础上，作者构造了DenseNet，其结构如下
#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-124 [[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-124 [[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}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: 0.0124 - 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: 0.1124 - 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
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 1Predict begin... step 10/10 [==============================] - 153ms/step Predict samples: 10 [1 1 1 1 0 0 0 1 1 1]
!rm -r net_params/*
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