我就废话不多说，大家还是直接看代码吧~

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, 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, 3, 3, border_mode='same',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, 3, 3, border_mode='same',init='glorot_uniform')(conv3) #model.add(Activation('relu')) l3=LeakyReLU(alpha=0.33)(conv3) conv4=ZeroPadding2D(padding=(1, 1))(l3) conv4=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv4) #model.add(Activation('relu')) l4=LeakyReLU(alpha=0.33)(conv4) m4=MaxPooling2D((3, 3), strides=(3, 3))(l4) d4=Dropout(0.25)(m4) # 3 conv5=ZeroPadding2D(padding=(1, 1))(d4) conv5=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv5) #model.add(Activation('relu')) l5=LeakyReLU(alpha=0.33)(conv5) conv6=ZeroPadding2D(padding=(1, 1))(l5) conv6=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv6) #model.add(Activation('relu')) l6=LeakyReLU(alpha=0.33)(conv6) m6=MaxPooling2D((3, 3), strides=(3, 3))(l6) d6=Dropout(0.25)(m6) # 4 conv7=ZeroPadding2D(padding=(1, 1))(d6) conv7=Convolution2D(256, 3, 3, border_mode='same',init='glorot_uniform')(conv7) #model.add(Activation('relu')) l7=LeakyReLU(alpha=0.33)(conv7) conv8=ZeroPadding2D(padding=(1, 1))(l7) conv8=Convolution2D(256, 3, 3, border_mode='same',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,activation='tanh',# 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() #编译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, beta_1=0.95, beta_2=0.999,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

补充知识：keras中如何将不同的模型联合起来（以cnn/lstm为例）

可能会遇到多种模型需要揉在一起，如cnn和lstm，而我一般在keras框架下开局就是一句

model = Sequential()

然后model.add ，model.add , ......到最后

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

这突然要把模型加起来，这可怎么办？

以下示例代码是将cnn和lstm联合起来，先是由cnn模型卷积池化得到特征，再输入到lstm模型中得到最终输出

import osimport kerasos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'from keras.models import Modelfrom keras.layers import *from matplotlib import pyplotos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'from keras.layers import Dense,Dropout,Activation,Convolution2D,MaxPooling2D,Flattenfrom keras.layers import LSTMdef design_model(): # design network inp=Input(shape=(11,5)) reshape=Reshape((11,5,1))(inp) conv1=Convolution2D(32,3,3,border_mode='same',init='glorot_uniform')(reshape) print(conv1) l1=Activation('relu')(conv1) conv2=Convolution2D(64,3,3, border_mode='same',)(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,activation='tanh',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() #打印出模型概况 adam = keras.optimizers.Adam(lr = 0.001, beta_1=0.95, beta_2=0.999,epsilon=1e-08) model.compile(loss=["mae"], optimizer=adam,metrics=['mape']) return modelmodel=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 trainmodel.save('model_trained.h5')

以上示例代码中cnn和lstm是串联即cnn输出作为lstm的输入，一条路线到底

如果想实现并联，即分开再汇总到一起

可用concatenate函数把cnn的输出端和lstm的输出端合并起来，后面再接上其他层，完成整个模型图的构建。

g2=concatenate([g,dl2],axis=1)

总结一下：

这是keras框架下除了Sequential另一种函数式构建模型的方式，更有灵活性，主要是在模型最后通过 model=Model(input=inp,outputs=den2)来确定整个模型的输入和输出

以上这篇在Keras中CNN联合LSTM进行分类实例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。