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文章来源: Keras中CNN联合LSTM进行分类