keras的三种模型实现与区别说明

前言

一、keras提供了三种定义模型的方式

1. 序列式(Sequential) API

序贯(sequential)API允许你为大多数问题逐层堆叠创建模型。虽然说对很多的应用来说，这样的一个手法很简单也解决了很多深度学习网络结构的构建，但是它也有限制－它不允许你创建模型有共享层或有多个输入或输出的网络。

2. 函数式(Functional) API

Keras函数式(functional)API为构建网络模型提供了更为灵活的方式。

它允许你定义多个输入或输出模型以及共享图层的模型。除此之外，它允许你定义动态(ad-hoc)的非周期性(acyclic)网络图。

模型是通过创建层的实例(layer instances)并将它们直接相互连接成对来定义的，然后定义一个模型(model)来指定那些层是要作为这个模型的输入和输出。

3.子类(Subclassing) API

补充知识：keras pytorch 构建模型对比

使用CIFAR10数据集，用三种框架构建Residual_Network作为例子，比较框架间的异同。

数据集格式

pytorch的数据集格式

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返回的数据结构
# 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]) 可见经过DataLoader后，labels由list变成了pytorch内置的tensor格式
"""
# 一般使用的话是下面这种
# Actual usage of the data loader is as below.
for images, labels in train_loader:
  # Training code should be written here.
  pass

keras的数据格式

import keras
from keras.datasets import cifar10

(train_x, train_y) , (test_x, test_y) = cifar10.load_data()
print(train_x.shape) # ndarray 类型： (50000, 32, 32, 3)
print(train_y.shape) # (50000, 1)

输入网络的数据格式不同

"""
1: pytorch 都是内置torch.xxTensor输入网络，而keras的则是原生ndarray类型
2: 对于multi-class的其中一种loss，即cross-entropy loss 而言，
  pytorch的api为 CorssEntropyLoss, 但y_true不能用one-hoe编码！这与keras，tensorflow	    都不同。tensorflow相应的api为softmax_cross_entropy
  他们的api都仅限于multi-class classification
3*: 其实上面提到的api都属于categorical cross-entropy loss,
  又叫 softmax loss，是函数内部先进行了 softmax 激活，再经过cross-entropy loss。
  这个loss是cross-entropy loss的变种，
  cross-entropy loss又叫logistic loss 或 multinomial logistic loss。
  实现这种loss的函数不包括激活函数，需要自定义。
  pytorch对应的api为BCEloss(仅限于 binary classification),
  tensorflow 对应的api为 log_loss。
  cross-entropy loss的第二个变种是 binary cross-entropy loss 又叫 sigmoid cross-  entropy loss。
  函数内部先进行了sigmoid激活，再经过cross-entropy loss。
  pytorch对应的api为BCEWithLogitsLoss,
  tensorflow对应的api为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)
    # 对于multi-class cross-entropy loss
    # 输入y_true不需要one-hot编码
    loss = criterion(outputs, labels)
...

# keras
# 对于multi-class cross-entropy loss
# 输入y_true需要one-hot编码
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)
...

整体流程

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)
# 保存整个网络
model.save("cifar10model.h5")
"""
# https://blog.csdn.net/jiandanjinxin/article/details/77152530
# 使用
# keras.models.load_model("cifar10model.h5")

# 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.h5')
#需要在代码中初始化一个完全相同的模型
# model.load_weights('my_model_weights.h5')
#需要加载权重到不同的网络结构（有些层一样）中，例如fine-tune或transfer-learning，可以通过层名字来加载模型
# model.load_weights('my_model_weights.h5', 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
		# 将上次迭代计算的梯度值清0
    optimizer.zero_grad()
    # 反向传播，计算梯度值
    loss.backward()
    # 更新权值参数
    optimizer.step()

# model.eval()，让model变成测试模式，对dropout和batch normalization的操作在训练和测试的时候是不一样的
# eval（）时，pytorch会自动把BN和DropOut固定住，不会取平均，而是用训练好的值。
# 不然的话，一旦test的batch_size过小，很容易就会被BN层导致生成图片颜色失真极大。
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
# 这是只保存了weights
torch.save(model.state_dict(), 'resnet.ckpt')
"""
# 使用
# myModel.load_state_dict(torch.load('params.ckpt'))
# 若想保存整个网络（architecture + weights)
# torch.save(resnet, 'model.ckpt')
# 使用
#model = torch.load('model.ckpt')
"""

对比流程

#https://blog.csdn.net/dss_dssssd/article/details/83892824
"""
1: 准备数据(注意数据格式不同）
2: 定义网络结构model
3: 定义损失函数
4: 定义优化算法 optimizer
5: 训练-keras
	5.1:编译模型（传入loss function和optimizer等）
	5.2:训练模型（fit or fit_generator，传入数据)
5: 训练-pytorch
迭代训练：
	5.1:准备好tensor形式的输入数据和标签(可选)
	5.2:前向传播计算网络输出output和计算损失函数loss
	5.3:反向传播更新参数
		以下三句话一句也不能少：
		5.3.1:将上次迭代计算的梯度值清0
			optimizer.zero_grad()
		5.3.2:反向传播，计算梯度值
			loss.backward()
		5.3.3:更新权值参数
			optimizer.step()
6: 在测试集上测试-keras
	model.evaluate
6: 在测试集上测试-pytorch
  遍历测试集，自定义metric
7: 保存网络（可选） 具体实现参考上面代码
"""

构建网络

对比网络

1、对于keras，不需要input_channels，函数内部会自动获得，而pytorch则需要显示声明input_channels

2、对于pytorch Conv2d需要指定padding，而keras的则是same和valid两种选项（valid即padding=0）

3、keras的Flatten操作可以视作pytorch中的view

4、keras的dimension一般顺序是（H, W, C) (tensorflow 为backend的话），而pytorch的顺序则是（ C, H, W)

5、具体的变换可以参照下方，但由于没有学过pytorch，keras也刚入门，不能保证正确，日后学的更深入了之后再来看看。

pytorch 构建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 对应的网络构建部分

"""
#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()会首先执行softmax计算
  # 当换成keras时，没有tf类似的softmax_cross_entropy
  # 自带的categorical_crossentropy不会执行激活操作，因此得在Dense层加上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
__________________________________________________________________________________________________

以上这篇keras的三种模型实现与区别说明就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。