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這篇文章主要介紹keras提供了三種定義模型方式分別是什么，文中介紹的非常詳細，具有一定的參考價值，感興趣的小伙伴們一定要看完！
前言
一、keras提供了三種定義模型的方式
1. 序列式(Sequential) API
序貫(sequential)API允許你為大多數問題逐層堆疊創建模型。雖然說對很多的應用來說，這樣的一個手法很簡單也解決了很多深度學習網絡結構的構建，但是它也有限制－它不允許你創建模型有共享層或有多個輸入或輸出的網絡。
2. 函數式(Functional) API
Keras函數式(functional)API為構建網絡模型提供了更為靈活的方式。
它允許你定義多個輸入或輸出模型以及共享圖層的模型。除此之外，它允許你定義動態(ad-hoc)的非周期性(acyclic)網絡圖。
模型是通過創建層的實例(layer instances)并將它們直接相互連接成對來定義的，然后定義一個模型(model)來指定那些層是要作為這個模型的輸入和輸出。
3.子類(Subclassing) API
補充知識：keras pytorch 構建模型對比
使用CIFAR10數據集，用三種框架構建Residual_Network作為例子，比較框架間的異同。
數據集格式
pytorch的數據集格式
import torch
import torch.nn as nn
import torchvision
# Download and construct CIFAR-10 dataset.
train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
train=True,
download=True)
# Fetch one data pair (read data from disk).
image, label = train_dataset[0]
print (image.size()) # torch.Size([3, 32, 32])
print (label) # 6
print (train_dataset.data.shape) # (50000, 32, 32, 3)
# type(train_dataset.targets)==list
print (len(train_dataset.targets)) # 50000
# Data loader (this provides queues and threads in a very simple way).
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=64,
shuffle=True)
"""
# 演示DataLoader返回的數據結構
# When iteration starts, queue and thread start to load data from files.
data_iter = iter(train_loader)
# Mini-batch images and labels.
images, labels = data_iter.next()
print(images.shape) # torch.Size([100, 3, 32, 32])
print(labels.shape)
# torch.Size([100]) 可見經過DataLoader后，labels由list變成了pytorch內置的tensor格式
"""
# 一般使用的話是下面這種
# Actual usage of the data loader is as below.
for images, labels in train_loader:
# Training code should be written here.
passkeras的數據格式
import keras from keras.datasets import cifar10 (train_x, train_y) , (test_x, test_y) = cifar10.load_data() print(train_x.shape) # ndarray 類型： (50000, 32, 32, 3) print(train_y.shape) # (50000, 1)
輸入網絡的數據格式不同
"""
1: pytorch 都是內置torch.xxTensor輸入網絡，而keras的則是原生ndarray類型
2: 對于multi-class的其中一種loss，即cross-entropy loss 而言，
pytorch的api為 CorssEntropyLoss, 但y_true不能用one-hoe編碼！這與keras，tensorflow 都不同。tensorflow相應的api為softmax_cross_entropy
他們的api都僅限于multi-class classification
3*: 其實上面提到的api都屬于categorical cross-entropy loss,
又叫 softmax loss，是函數內部先進行了 softmax 激活，再經過cross-entropy loss。
這個loss是cross-entropy loss的變種，
cross-entropy loss又叫logistic loss 或 multinomial logistic loss。
實現這種loss的函數不包括激活函數，需要自定義。
pytorch對應的api為BCEloss(僅限于 binary classification),
tensorflow 對應的api為 log_loss。
cross-entropy loss的第二個變種是 binary cross-entropy loss 又叫 sigmoid cross- entropy loss。
函數內部先進行了sigmoid激活，再經過cross-entropy loss。
pytorch對應的api為BCEWithLogitsLoss,
tensorflow對應的api為sigmoid_cross_entropy
"""
# pytorch
criterion = nn.CrossEntropyLoss()
...
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
# 對于multi-class cross-entropy loss
# 輸入y_true不需要one-hot編碼
loss = criterion(outputs, labels)
...
# keras
# 對于multi-class cross-entropy loss
# 輸入y_true需要one-hot編碼
train_y = keras.utils.to_categorical(train_y,10)
...
model.fit_generator(datagen.flow(train_x, train_y, batch_size=128),
validation_data=[test_x,test_y],
epochs=epochs,steps_per_epoch=steps_per_epoch, verbose=1)
...整體流程
keras 流程
model = myModel()
model.compile(optimizer=Adam(0.001),loss="categorical_crossentropy",metrics=["accuracy"])
model.fit_generator(datagen.flow(train_x, train_y, batch_size=128),
validation_data=[test_x,test_y],
epochs=epochs,steps_per_epoch=steps_per_epoch, verbose=1, workers=4)
#Evaluate the accuracy of the test dataset
accuracy = model.evaluate(x=test_x,y=test_y,batch_size=128)
# 保存整個網絡
model.save("cifar10model.h6")
"""
# https://blog.csdn.net/jiandanjinxin/article/details/77152530
# 使用
# keras.models.load_model("cifar10model.h6")
# 只保存architecture
# json_string = model.to_json()
# open('my_model_architecture.json','w').write(json_string)
# 使用
# from keras.models import model_from_json
#model = model_from_json(open('my_model_architecture.json').read())
# 只保存weights
# model.save_weights('my_model_weights.h6')
#需要在代碼中初始化一個完全相同的模型
# model.load_weights('my_model_weights.h6')
#需要加載權重到不同的網絡結構（有些層一樣）中，例如fine-tune或transfer-learning，可以通過層名字來加載模型
# model.load_weights('my_model_weights.h6', by_name=True)
"""pytorch 流程
model = myModel()
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
# 將上次迭代計算的梯度值清0
optimizer.zero_grad()
# 反向傳播，計算梯度值
loss.backward()
# 更新權值參數
optimizer.step()
# model.eval()，讓model變成測試模式，對dropout和batch normalization的操作在訓練和測試的時候是不一樣的
# eval（）時，pytorch會自動把BN和DropOut固定住，不會取平均，而是用訓練好的值。
# 不然的話，一旦test的batch_size過小，很容易就會被BN層導致生成圖片顏色失真極大。
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
# Save the model checkpoint
# 這是只保存了weights
torch.save(model.state_dict(), 'resnet.ckpt')
"""
# 使用
# myModel.load_state_dict(torch.load('params.ckpt'))
# 若想保存整個網絡（architecture + weights)
# torch.save(resnet, 'model.ckpt')
# 使用
#model = torch.load('model.ckpt')
"""對比流程
#https://blog.csdn.net/dss_dssssd/article/details/83892824 """ 1: 準備數據(注意數據格式不同） 2: 定義網絡結構model 3: 定義損失函數 4: 定義優化算法 optimizer 5: 訓練-keras 5.1:編譯模型（傳入loss function和optimizer等） 5.2:訓練模型（fit or fit_generator，傳入數據) 5: 訓練-pytorch 迭代訓練： 5.1:準備好tensor形式的輸入數據和標簽(可選) 5.2:前向傳播計算網絡輸出output和計算損失函數loss 5.3:反向傳播更新參數 以下三句話一句也不能少： 5.3.1:將上次迭代計算的梯度值清0 optimizer.zero_grad() 5.3.2:反向傳播，計算梯度值 loss.backward() 5.3.3:更新權值參數 optimizer.step() 6: 在測試集上測試-keras model.evaluate 6: 在測試集上測試-pytorch 遍歷測試集，自定義metric 7: 保存網絡（可選） 具體實現參考上面代碼 """
構建網絡
對比網絡
1、對于keras，不需要input_channels，函數內部會自動獲得，而pytorch則需要顯示聲明input_channels
2、對于pytorch Conv2d需要指定padding，而keras的則是same和valid兩種選項（valid即padding=0）
3、keras的Flatten操作可以視作pytorch中的view
4、keras的dimension一般順序是（H, W, C) (tensorflow 為backend的話），而pytorch的順序則是（ C, H, W)
5、具體的變換可以參照下方，但由于沒有學過pytorch，keras也剛入門，不能保證正確，日后學的更深入了之后再來看看。
pytorch 構建Residual-network
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
num_epochs = 80
learning_rate = 0.001
# Image preprocessing modules
transform = transforms.Compose([
transforms.Pad(4),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32),
transforms.ToTensor()])
# CIFAR-10 dataset
# train_dataset.data.shape
#Out[31]: (50000, 32, 32, 3)
# train_dataset.targets list
# len(list)=5000
train_dataset = torchvision.datasets.CIFAR10(root='./data/',
train=True,
transform=transform,
download=True)
test_dataset = torchvision.datasets.CIFAR10(root='../../data/',
train=False,
transform=transforms.ToTensor())
# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=100,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=100,
shuffle=False)
# 3x3 convolution
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
# Residual block
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# ResNet
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 16
self.conv = conv3x3(3, 16)
self.bn = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self.make_layer(block, 16, layers[0])
self.layer2 = self.make_layer(block, 32, layers[1], 2)
self.layer3 = self.make_layer(block, 64, layers[2], 2)
self.avg_pool = nn.AvgPool2d(8)
self.fc = nn.Linear(64, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
conv3x3(self.in_channels, out_channels, stride=stride),
nn.BatchNorm2d(out_channels))
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(block(out_channels, out_channels))
# [*[1,2,3]]
# Out[96]: [1, 2, 3]
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv(x) # out.shape:torch.Size([100, 16, 32, 32])
out = self.bn(out)
out = self.relu(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
model = ResNet(ResidualBlock, [2, 2, 2]).to(device)
# pip install torchsummary or
# git clone https://github.com/sksq96/pytorch-summary
from torchsummary import summary
# input_size=(C,H,W)
summary(model, input_size=(3, 32, 32))
images,labels = iter(train_loader).next()
outputs = model(images)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# For updating learning rate
def update_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Train the model
total_step = len(train_loader)
curr_lr = learning_rate
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}"
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# Decay learning rate
if (epoch+1) % 20 == 0:
curr_lr /= 3
update_lr(optimizer, curr_lr)
# Test the model
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
# Save the model checkpoint
torch.save(model.state_dict(), 'resnet.ckpt')keras 對應的網絡構建部分
"""
#pytorch
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
"""
def conv3x3(x,out_channels, stride=1):
#out = spatial_2d_padding(x,padding=((1, 1), (1, 1)), data_format="channels_last")
return Conv2D(filters=out_channels, kernel_size=[3,3], strides=(stride,stride),padding="same")(x)
"""
# pytorch
# Residual block
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
"""
def ResidualBlock(x, out_channels, stride=1, downsample=False):
residual = x
out = conv3x3(x, out_channels,stride)
out = BatchNormalization()(out)
out = Activation("relu")(out)
out = conv3x3(out, out_channels)
out = BatchNormalization()(out)
if downsample:
residual = conv3x3(residual, out_channels, stride=stride)
residual = BatchNormalization()(residual)
out = keras.layers.add([residual,out])
out = Activation("relu")(out)
return out
"""
#pytorch
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
conv3x3(self.in_channels, out_channels, stride=stride),
nn.BatchNorm2d(out_channels))
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(block(out_channels, out_channels))
# [*[1,2,3]]
# Out[96]: [1, 2, 3]
return nn.Sequential(*layers)
"""
def make_layer(x, out_channels, blocks, stride=1):
# tf backend: x.output_shape[-1]==out_channels
#print("x.shape[-1] ",x.shape[-1])
downsample = False
if (stride != 1) or (out_channels != x.shape[-1]):
downsample = True
out = ResidualBlock(x, out_channels, stride, downsample)
for i in range(1, blocks):
out = ResidualBlock(out, out_channels)
return out
def KerasResidual(input_shape):
images = Input(input_shape)
out = conv3x3(images,16) # out.shape=(None, 32, 32, 16)
out = BatchNormalization()(out)
out = Activation("relu")(out)
layer1_out = make_layer(out, 16, layers[0])
layer2_out = make_layer(layer1_out, 32, layers[1], 2)
layer3_out = make_layer(layer2_out, 64, layers[2], 2)
out = AveragePooling2D(pool_size=(8,8))(layer3_out)
out = Flatten()(out)
# pytorch 的nn.CrossEntropyLoss()會首先執行softmax計算
# 當換成keras時，沒有tf類似的softmax_cross_entropy
# 自帶的categorical_crossentropy不會執行激活操作，因此得在Dense層加上activation
out = Dense(units=10, activation="softmax")(out)
model = Model(inputs=images,outputs=out)
return model
input_shape=(32, 32, 3)
layers=[2, 2, 2]
mymodel = KerasResidual(input_shape)
mymodel.summary()pytorch model summary
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 16, 32, 32] 432
BatchNorm2d-2 [-1, 16, 32, 32] 32
ReLU-3 [-1, 16, 32, 32] 0
Conv2d-4 [-1, 16, 32, 32] 2,304
BatchNorm2d-5 [-1, 16, 32, 32] 32
ReLU-6 [-1, 16, 32, 32] 0
Conv2d-7 [-1, 16, 32, 32] 2,304
BatchNorm2d-8 [-1, 16, 32, 32] 32
ReLU-9 [-1, 16, 32, 32] 0
ResidualBlock-10 [-1, 16, 32, 32] 0
Conv2d-11 [-1, 16, 32, 32] 2,304
BatchNorm2d-12 [-1, 16, 32, 32] 32
ReLU-13 [-1, 16, 32, 32] 0
Conv2d-14 [-1, 16, 32, 32] 2,304
BatchNorm2d-15 [-1, 16, 32, 32] 32
ReLU-16 [-1, 16, 32, 32] 0
ResidualBlock-17 [-1, 16, 32, 32] 0
Conv2d-18 [-1, 32, 16, 16] 4,608
BatchNorm2d-19 [-1, 32, 16, 16] 64
ReLU-20 [-1, 32, 16, 16] 0
Conv2d-21 [-1, 32, 16, 16] 9,216
BatchNorm2d-22 [-1, 32, 16, 16] 64
Conv2d-23 [-1, 32, 16, 16] 4,608
BatchNorm2d-24 [-1, 32, 16, 16] 64
ReLU-25 [-1, 32, 16, 16] 0
ResidualBlock-26 [-1, 32, 16, 16] 0
Conv2d-27 [-1, 32, 16, 16] 9,216
BatchNorm2d-28 [-1, 32, 16, 16] 64
ReLU-29 [-1, 32, 16, 16] 0
Conv2d-30 [-1, 32, 16, 16] 9,216
BatchNorm2d-31 [-1, 32, 16, 16] 64
ReLU-32 [-1, 32, 16, 16] 0
ResidualBlock-33 [-1, 32, 16, 16] 0
Conv2d-34 [-1, 64, 8, 8] 18,432
BatchNorm2d-35 [-1, 64, 8, 8] 128
ReLU-36 [-1, 64, 8, 8] 0
Conv2d-37 [-1, 64, 8, 8] 36,864
BatchNorm2d-38 [-1, 64, 8, 8] 128
Conv2d-39 [-1, 64, 8, 8] 18,432
BatchNorm2d-40 [-1, 64, 8, 8] 128
ReLU-41 [-1, 64, 8, 8] 0
ResidualBlock-42 [-1, 64, 8, 8] 0
Conv2d-43 [-1, 64, 8, 8] 36,864
BatchNorm2d-44 [-1, 64, 8, 8] 128
ReLU-45 [-1, 64, 8, 8] 0
Conv2d-46 [-1, 64, 8, 8] 36,864
BatchNorm2d-47 [-1, 64, 8, 8] 128
ReLU-48 [-1, 64, 8, 8] 0
ResidualBlock-49 [-1, 64, 8, 8] 0
AvgPool2d-50 [-1, 64, 1, 1] 0
Linear-51 [-1, 10] 650
================================================================
Total params: 195,738
Trainable params: 195,738
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 3.63
Params size (MB): 0.75
Estimated Total Size (MB): 4.38
----------------------------------------------------------------keras model summary
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_26 (InputLayer) (None, 32, 32, 3) 0
__________________________________________________________________________________________________
conv2d_103 (Conv2D) (None, 32, 32, 16) 448 input_26[0][0]
__________________________________________________________________________________________________
batch_normalization_99 (BatchNo (None, 32, 32, 16) 64 conv2d_103[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, 32, 32, 16) 0 batch_normalization_99[0][0]
__________________________________________________________________________________________________
conv2d_104 (Conv2D) (None, 32, 32, 16) 2320 activation_87[0][0]
__________________________________________________________________________________________________
batch_normalization_100 (BatchN (None, 32, 32, 16) 64 conv2d_104[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, 32, 32, 16) 0 batch_normalization_100[0][0]
__________________________________________________________________________________________________
conv2d_105 (Conv2D) (None, 32, 32, 16) 2320 activation_88[0][0]
__________________________________________________________________________________________________
batch_normalization_101 (BatchN (None, 32, 32, 16) 64 conv2d_105[0][0]
__________________________________________________________________________________________________
add_34 (Add) (None, 32, 32, 16) 0 activation_87[0][0]
batch_normalization_101[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 32, 32, 16) 0 add_34[0][0]
__________________________________________________________________________________________________
conv2d_106 (Conv2D) (None, 32, 32, 16) 2320 activation_89[0][0]
__________________________________________________________________________________________________
batch_normalization_102 (BatchN (None, 32, 32, 16) 64 conv2d_106[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, 32, 32, 16) 0 batch_normalization_102[0][0]
__________________________________________________________________________________________________
conv2d_107 (Conv2D) (None, 32, 32, 16) 2320 activation_90[0][0]
__________________________________________________________________________________________________
batch_normalization_103 (BatchN (None, 32, 32, 16) 64 conv2d_107[0][0]
__________________________________________________________________________________________________
add_35 (Add) (None, 32, 32, 16) 0 activation_89[0][0]
batch_normalization_103[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, 32, 32, 16) 0 add_35[0][0]
__________________________________________________________________________________________________
conv2d_108 (Conv2D) (None, 16, 16, 32) 4640 activation_91[0][0]
__________________________________________________________________________________________________
batch_normalization_104 (BatchN (None, 16, 16, 32) 128 conv2d_108[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, 16, 16, 32) 0 batch_normalization_104[0][0]
__________________________________________________________________________________________________
conv2d_110 (Conv2D) (None, 16, 16, 32) 4640 activation_91[0][0]
__________________________________________________________________________________________________
conv2d_109 (Conv2D) (None, 16, 16, 32) 9248 activation_92[0][0]
__________________________________________________________________________________________________
batch_normalization_106 (BatchN (None, 16, 16, 32) 128 conv2d_110[0][0]
__________________________________________________________________________________________________
batch_normalization_105 (BatchN (None, 16, 16, 32) 128 conv2d_109[0][0]
__________________________________________________________________________________________________
add_36 (Add) (None, 16, 16, 32) 0 batch_normalization_106[0][0]
batch_normalization_105[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, 16, 16, 32) 0 add_36[0][0]
__________________________________________________________________________________________________
conv2d_111 (Conv2D) (None, 16, 16, 32) 9248 activation_93[0][0]
__________________________________________________________________________________________________
batch_normalization_107 (BatchN (None, 16, 16, 32) 128 conv2d_111[0][0]
__________________________________________________________________________________________________
activation_94 (Activation) (None, 16, 16, 32) 0 batch_normalization_107[0][0]
__________________________________________________________________________________________________
conv2d_112 (Conv2D) (None, 16, 16, 32) 9248 activation_94[0][0]
__________________________________________________________________________________________________
batch_normalization_108 (BatchN (None, 16, 16, 32) 128 conv2d_112[0][0]
__________________________________________________________________________________________________
add_37 (Add) (None, 16, 16, 32) 0 activation_93[0][0]
batch_normalization_108[0][0]
__________________________________________________________________________________________________
activation_95 (Activation) (None, 16, 16, 32) 0 add_37[0][0]
__________________________________________________________________________________________________
conv2d_113 (Conv2D) (None, 8, 8, 64) 18496 activation_95[0][0]
__________________________________________________________________________________________________
batch_normalization_109 (BatchN (None, 8, 8, 64) 256 conv2d_113[0][0]
__________________________________________________________________________________________________
activation_96 (Activation) (None, 8, 8, 64) 0 batch_normalization_109[0][0]
__________________________________________________________________________________________________
conv2d_115 (Conv2D) (None, 8, 8, 64) 18496 activation_95[0][0]
__________________________________________________________________________________________________
conv2d_114 (Conv2D) (None, 8, 8, 64) 36928 activation_96[0][0]
__________________________________________________________________________________________________
batch_normalization_111 (BatchN (None, 8, 8, 64) 256 conv2d_115[0][0]
__________________________________________________________________________________________________
batch_normalization_110 (BatchN (None, 8, 8, 64) 256 conv2d_114[0][0]
__________________________________________________________________________________________________
add_38 (Add) (None, 8, 8, 64) 0 batch_normalization_111[0][0]
batch_normalization_110[0][0]
__________________________________________________________________________________________________
activation_97 (Activation) (None, 8, 8, 64) 0 add_38[0][0]
__________________________________________________________________________________________________
conv2d_116 (Conv2D) (None, 8, 8, 64) 36928 activation_97[0][0]
__________________________________________________________________________________________________
batch_normalization_112 (BatchN (None, 8, 8, 64) 256 conv2d_116[0][0]
__________________________________________________________________________________________________
activation_98 (Activation) (None, 8, 8, 64) 0 batch_normalization_112[0][0]
__________________________________________________________________________________________________
conv2d_117 (Conv2D) (None, 8, 8, 64) 36928 activation_98[0][0]
__________________________________________________________________________________________________
batch_normalization_113 (BatchN (None, 8, 8, 64) 256 conv2d_117[0][0]
__________________________________________________________________________________________________
add_39 (Add) (None, 8, 8, 64) 0 activation_97[0][0]
batch_normalization_113[0][0]
__________________________________________________________________________________________________
activation_99 (Activation) (None, 8, 8, 64) 0 add_39[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 1, 1, 64) 0 activation_99[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 64) 0 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 10) 650 flatten_2[0][0]
==================================================================================================
Total params: 197,418
Trainable params: 196,298
Non-trainable params: 1,120
__________________________________________________________________________________________________以上是keras提供了三種定義模型方式分別是什么的所有內容，感謝各位的閱讀！希望分享的內容對大家有幫助，更多相關知識，歡迎關注億速云行業資訊頻道！
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