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 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(32,3,3,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 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。