我就廢話不多說，大家還是直接看程式碼吧~

def get_model():
  n_classes = 6
  inp=Input(shape=(40,80))
  reshape=Reshape((1,40,80))(inp)
 #  pre=ZeroPadding2D(padding=(1,1))(reshape)
  # 1
  conv1=Convolution2D(32,3,border_mode='same',init='glorot_uniform')(reshape)
  #model.add(Activation('relu'))
  l1=LeakyReLU(alpha=0.33)(conv1)
 
  conv2=ZeroPadding2D(padding=(1,1))(l1)
  conv2=Convolution2D(32,init='glorot_uniform')(conv2)
  #model.add(Activation('relu'))
  l2=LeakyReLU(alpha=0.33)(conv2)
 
  m2=MaxPooling2D((3,3),strides=(3,3))(l2)
  d2=Dropout(0.25)(m2)
  # 2
  conv3=ZeroPadding2D(padding=(1,1))(d2)
  conv3=Convolution2D(64,init='glorot_uniform')(conv3)
  #model.add(Activation('relu'))
  l3=LeakyReLU(alpha=0.33)(conv3)
 
  conv4=ZeroPadding2D(padding=(1,1))(l3)
  conv4=Convolution2D(64,init='glorot_uniform')(conv4)
  #model.add(Activation('relu'))
  l4=LeakyReLU(alpha=0.33)(conv4)
 
  m4=MaxPooling2D((3,3))(l4)
  d4=Dropout(0.25)(m4)
  # 3
  conv5=ZeroPadding2D(padding=(1,1))(d4)
  conv5=Convolution2D(128,init='glorot_uniform')(conv5)
  #model.add(Activation('relu'))
  l5=LeakyReLU(alpha=0.33)(conv5)
 
  conv6=ZeroPadding2D(padding=(1,1))(l5)
  conv6=Convolution2D(128,init='glorot_uniform')(conv6)
  #model.add(Activation('relu'))
  l6=LeakyReLU(alpha=0.33)(conv6)
 
  m6=MaxPooling2D((3,3))(l6)
  d6=Dropout(0.25)(m6)
  # 4
  conv7=ZeroPadding2D(padding=(1,1))(d6)
  conv7=Convolution2D(256,init='glorot_uniform')(conv7)
  #model.add(Activation('relu'))
  l7=LeakyReLU(alpha=0.33)(conv7)
 
  conv8=ZeroPadding2D(padding=(1,1))(l7)
  conv8=Convolution2D(256,init='glorot_uniform')(conv8)
  #model.add(Activation('relu'))
  l8=LeakyReLU(alpha=0.33)(conv8)
  g=GlobalMaxPooling2D()(l8)
  print("g=",g)
  #g1=Flatten()(g)
  lstm1=LSTM(
    input_shape=(40,80),output_dim=256,activation='tanh',return_sequences=False)(inp)
  dl1=Dropout(0.3)(lstm1)
  
  den1=Dense(200,activation="relu")(dl1)
  #model.add(Activation('relu'))
  #l11=LeakyReLU(alpha=0.33)(d11)
  dl2=Dropout(0.3)(den1)
 
#   lstm2=LSTM(
#     256,#     return_sequences=False)(lstm1)
#   dl2=Dropout(0.5)(lstm2)
  print("dl2=",dl1)
  g2=concatenate([g,dl2],axis=1)
  d10=Dense(1024)(g2)
  #model.add(Activation('relu'))
  l10=LeakyReLU(alpha=0.33)(d10)
  l10=Dropout(0.5)(l10)
  l11=Dense(n_classes,activation='softmax')(l10)
 
  model=Model(input=inp,outputs=l11)
  model.summary()
  #編譯model
  adam = keras.optimizers.Adam(lr = 0.0005,beta_1=0.95,beta_2=0.999,epsilon=1e-08)
  #adam = keras.optimizers.Adam(lr = 0.001,epsilon=1e-08)
  #sgd = keras.optimizers.SGD(lr = 0.001,decay = 1e-06,momentum = 0.9,nesterov = False)
 
  #reduce_lr = ReduceLROnPlateau(monitor = 'loss',factor = 0.1,patience = 2,verbose = 1,min_lr = 0.00000001,mode = 'min')
  model.compile(loss='categorical_crossentropy',optimizer=adam,metrics=['accuracy'])
  
  return model

補充知識：keras中如何將不同的模型聯合起來（以cnn/lstm為例）

可能會遇到多種模型需要揉在一起，如cnn和lstm，而我一般在keras框架下開局就是一句

model = Sequential()

然後model.add ，model.add,......到最後

model.compile(loss=["mae"],optimizer='adam',metrics=[mape])

這突然要把模型加起來，這可怎麼辦？

以下示例程式碼是將cnn和lstm聯合起來，先是由cnn模型卷積池化得到特徵，再輸入到lstm模型中得到最終輸出

import os
import keras
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras.models import Model
from keras.layers import *
from matplotlib import pyplot
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from keras.layers import Dense,Dropout,Activation,Convolution2D,MaxPooling2D,Flatten
from keras.layers import LSTM
def design_model():
  # design network
  inp=Input(shape=(11,5))
  reshape=Reshape((11,5,1))(inp)
  conv1=Convolution2D(32,init='glorot_uniform')(reshape)
  print(conv1)
  l1=Activation('relu')(conv1)
  conv2=Convolution2D(64,)(l1)
  l2=Activation('relu')(conv2)
  print(l2)
  m2=MaxPooling2D(pool_size=(2,2),border_mode='valid')(l2)
  print(m2)
  reshape1=Reshape((10,64))(m2)
  lstm1=LSTM(input_shape=(10,64),output_dim=30,return_sequences=False)(reshape1)
  dl1=Dropout(0.3)(lstm1)
  # den1=Dense(100,activation="relu")(dl1)
  den2=Dense(1,activation="relu")(dl1)
  model=Model(input=inp,outputs=den2)
  model.summary() #打印出模型概況
  adam = keras.optimizers.Adam(lr = 0.001,epsilon=1e-08)
  model.compile(loss=["mae"],metrics=['mape'])
  return model
model=design_model()
history = model.fit(train_x,train_y,epochs=epochs,batch_size=batch_size,validation_data=[test_x,test_y],verbose=2,shuffle=True)
# #save LeNet_model_files after train
model.save('model_trained.h5')

以上示例程式碼中cnn和lstm是串聯即cnn輸出作為lstm的輸入，一條路線到底

如果想實現並聯，即分開再彙總到一起

可用concatenate函式把cnn的輸出端和lstm的輸出端合併起來，後面再接上其他層，完成整個模型圖的構建。

g2=concatenate([g,axis=1)

總結一下：

這是keras框架下除了Sequential另一種函式式構建模型的方式，更有靈活性，主要是在模型最後通過 model=Model(input=inp,outputs=den2)來確定整個模型的輸入和輸出

以上這篇在Keras中CNN聯合LSTM進行分類例項就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。