python cnn 实例_在Keras中CNN联合LSTM进行分类实例

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脚本之家。

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