Keras函数式(functional)API的使用方式

多层感知器(Multilayer Perceptron)

定义了用于二分类的多层感知器模型。

模型输入32维特征，经过三个全连接层，每层使用relu线性激活函数，并且在输出层中使用sigmoid激活函数，最后用于二分类。

##------ Multilayer Perceptron ------##

from keras.models import Model

from keras.layers import Input, Dense

from keras import backend as K

K.clear_session() 



# MLP model

x = Input(shape=(32,))

hidden1 = Dense(10, activation='relu')(x)

hidden2 = Dense(20, activation='relu')(hidden1)

hidden3 = Dense(10, activation='relu')(hidden2)

output = Dense(1, activation='sigmoid')(hidden3)

model = Model(inputs=x, outputs=output)



# summarize layers

model.summary()

模型的结构和参数如下：

卷积神经网络(Convolutional Neural Network)

定义用于图像分类的卷积神经网络。

该模型接收3通道的64×64图像作为输入，然后经过两个卷积和池化层的序列作为特征提取器，接着过一个全连接层，最后输出层过softmax激活函数进行10个类别的分类。

##------ Convolutional Neural Network ------##

from keras.models import Model

from keras.layers import Input

from keras.layers import Dense, Flatten

from keras.layers import Conv2D, MaxPooling2D

from keras import backend as K

K.clear_session()



# CNN model

x = Input(shape=(64,64,3))

conv1 = Conv2D(16, (5,5), activation='relu')(x)

pool1 = MaxPooling2D((2,2))(conv1)

conv2 = Conv2D(32, (3,3), activation='relu')(pool1)

pool2 = MaxPooling2D((2,2))(conv2)

conv3 = Conv2D(32, (3,3), activation='relu')(pool2)

pool3 = MaxPooling2D((2,2))(conv3)

flat = Flatten()(pool3)

hidden1 = Dense(512, activation='relu')(flat)

output = Dense(10, activation='softmax')(hidden1)

model = Model(inputs=x, outputs=output)



# summarize layers

model.summary()

模型的结构和参数如下：

循环神经网络(Recurrent Neural Network)

定义一个用于文本序列分类的LSTM网络。

该模型需要100个时间步长作为输入，然后经过一个Embedding层，每个时间步变成128维特征表示，然后经过一个LSTM层，LSTM输出过一个全连接层，最后输出用sigmoid激活函数用于进行二分类预测。

##------ Recurrent Neural Network ------##

from keras.models import Model

from keras.layers import Input

from keras.layers import Dense, LSTM, Embedding

from keras import backend as K

K.clear_session()



VOCAB_SIZE = 10000

EMBED_DIM = 128



x = Input(shape=(100,), dtype='int32')

embedding = Embedding(VOCAB_SIZE, EMBED_DIM, mask_zero=True)(x)

hidden1 = LSTM(64)(embedding)

hidden2 = Dense(32, activation='relu')(hidden1)

output = Dense(1, activation='sigmoid')(hidden2)

model = Model(inputs=x, outputs=output)



# summarize layers

model.summary()

模型的结构和参数如下：

Bidirectional recurrent neural network

定义一个双向循环神经网络，可以用来完成序列标注等任务，相比上面的LSTM网络，多了一个反向的LSTM，其它设置一样。

##------ Bidirectional recurrent neural network ------##

from keras.models import Model

from keras.layers import Input, Embedding

from keras.layers import Dense, LSTM, Bidirectional

from keras import backend as K

K.clear_session()



VOCAB_SIZE = 10000

EMBED_DIM = 128

HIDDEN_SIZE = 64

# input layer

x = Input(shape=(100,), dtype='int32')

# embedding layer

embedding = Embedding(VOCAB_SIZE, EMBED_DIM, mask_zero=True)(x)

# BiLSTM layer

hidden = Bidirectional(LSTM(HIDDEN_SIZE, return_sequences=True))(embedding)

# prediction layer

output = Dense(10, activation='softmax')(hidden)

model = Model(inputs=x, outputs=output)



model.summary()

模型的结构和参数如下：

共享输入层模型(Shared Input Layer Model)

定义了具有不同大小内核的多个卷积层来解释图像输入。

该模型采用尺寸为64×64像素的3通道图像。

有两个共享此输入的CNN特征提取子模型; 第一个内核大小为5x5，第二个内核大小为3x3。

把提取的特征展平为向量然后拼接成一个长向量，然后过一个全连接层，最后输出层完成10分类。

##------ Shared Input Layer Model ------##



from keras.models import Model

from keras.layers import Input

from keras.layers import Dense, Flatten

from keras.layers import Conv2D, MaxPooling2D, Concatenate

from keras import backend as K

K.clear_session()



# input layer

x = Input(shape=(64,64,3))

# first feature extractor

conv1 = Conv2D(32, (3,3), activation='relu')(x)

pool1 = MaxPooling2D((2,2))(conv1)

flat1 = Flatten()(pool1)

# second feature extractor 

conv2 = Conv2D(16, (5,5), activation='relu')(x)

pool2 = MaxPooling2D((2,2))(conv2)

flat2 = Flatten()(pool2)

# merge feature

merge = Concatenate()([flat1, flat2])

# interpretation layer

hidden1 = Dense(128, activation='relu')(merge)

# prediction layer

output = Dense(10, activation='softmax')(merge)

model = Model(inputs=x, outputs=output)



model.summary()

模型的结构和参数如下：

Shared Feature Extraction Layer

定义一个共享特征抽取层的模型，这里共享的是LSTM层的输出，具体共享参见代码

##------ Shared Feature Extraction Layer ------##

from keras.models import Model

from keras.layers import Input, Embedding

from keras.layers import Dense, LSTM, Concatenate

from keras import backend as K

K.clear_session()



# input layer

x = Input(shape=(100,32))

# feature extraction

extract1 = LSTM(64)(x)

# first interpretation model

interp1 = Dense(32, activation='relu')(extract1)

# second interpretation model

interp11 = Dense(64, activation='relu')(extract1)

interp12 = Dense(32, activation='relu')(interp11)

# merge interpretation

merge = Concatenate()([interp1, interp12])

# output layer

output = Dense(10, activation='softmax')(merge)

model = Model(inputs=x, outputs=output)



model.summary()

模型的结构和参数如下：

多输入模型(Multiple Input Model)

定义有两个输入的模型，这里测试的是输入两张图片，一个输入是单通道的64x64，另一个是3通道的32x32，两个经过卷积层、池化层后，展平拼接，最后进行二分类。

##------ Multiple Input Model  ------##

from keras.models import Model

from keras.layers import Input

from keras.layers import Dense, Flatten

from keras.layers import Conv2D, MaxPooling2D, Concatenate

from keras import backend as K

K.clear_session()



# first input model

input1 = Input(shape=(64,64,1))

conv11 = Conv2D(32, (5,5), activation='relu')(input1)

pool11 = MaxPooling2D(pool_size=(2,2))(conv11)

conv12 = Conv2D(16, (3,3), activation='relu')(pool11)

pool12 = MaxPooling2D(pool_size=(2,2))(conv12)

flat1 = Flatten()(pool12)

# second input model

input2 = Input(shape=(32,32,3))

conv21 = Conv2D(32, (5,5), activation='relu')(input2)

pool21 = MaxPooling2D(pool_size=(2,2))(conv21)

conv22 = Conv2D(16, (3,3), activation='relu')(pool21)

pool22 = MaxPooling2D(pool_size=(2,2))(conv22)

flat2 = Flatten()(pool22)

# merge input models

merge = Concatenate()([flat1, flat2])

# interpretation model

hidden1 = Dense(20, activation='relu')(merge)

output = Dense(1, activation='sigmoid')(hidden1)

model = Model(inputs=[input1, input2], outputs=output)



model.summary()

模型的结构和参数如下：

多输出模型(Multiple Output Model)

定义有多个输出的模型，以文本序列输入LSTM网络为例，一个输出是对文本的分类，另外一个输出是对文本进行序列标注。

##------ Multiple Output Model ------ ##

from keras.models import Model

from keras.layers import Input

from keras.layers import Dense, Flatten, TimeDistributed, LSTM

from keras.layers import Conv2D, MaxPooling2D, Concatenate

from keras import backend as K

K.clear_session()



x = Input(shape=(100,1))

extract = LSTM(10, return_sequences=True)(x)



class11 = LSTM(10)(extract)

class12 = Dense(10, activation='relu')(class11)

output1 = Dense(1, activation='sigmoid')(class12)

output2 = TimeDistributed(Dense(1, activation='linear'))(extract)

model = Model(inputs=x, outputs=[output1, output2])



model.summary()

模型的结构和参数如下：

参考

[1] https://machinelearningmastery.com/keras-functional-api-deep-learning/

[2] https://keras.io/getting-started/functional-api-guide/

[3] https://tensorflow.google.cn/alpha/guide/keras/functional

总结