Keras函数式(functional)API的使用方式
目录
多层感知器(Multilayer Perceptron)
卷积神经网络(Convolutional Neural Network)
循环神经网络(Recurrent Neural Network)
Bidirectional recurrent neural network
共享输入层模型(Shared Input Layer Model)
Shared Feature Extraction Layer
多输入模型(Multiple Input Model)
多输出模型(Multiple Output Model)
总结
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：
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：
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：
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：
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：
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：
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：
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
总结以上为个人经验，希望能给大家一个参考，也希望大家多多支持软件开发网。
