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Keras函数式(functional)API的使用方式

2023-02-22 08:27:13 作者：黄然大悟

这篇文章主要介绍了Keras函数式(functional)API的使用方式，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教

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：

Multilayer Perceptron

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：

CNN

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：

LSTM

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：

BiLSTM

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-Input-Layer

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：

Shared-Feature-Extraction-Layer

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：

Multiple-Input-Model

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：

Multiple-Output-Model