在keras下实现多个模型的融合

在keras下实现多个模型的融合

小风风12580 2019-09-30 10:42:00 1105 收藏 7

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在网上搜过发现关于keras下的模型融合框架其实很简单，奈何网上说了一大堆，这个东西官方文档上就有，自己写了个demo：

# Function：基于keras框架下实现，多个独立任务分类

# Writer: PQF

# Time: 2019/9/29

import numpy as np

from keras.layers import Input, Dense

from keras.models import Model

import tensorflow as tf

# 生成训练集

dataset_size = 128*3

rdm = np.random.RandomState(1)

X = rdm.rand(dataset_size,2)

Y1 = [[int(x1+x2<1)] for (x1,x2) in X]

Y2 = [[int(x1+x2*x2<0.5)] for (x1,x2) in X]

X_train = X[:-2]

Y_train1 = Y1[:-2]

Y_train2 = Y2[:-2]

X_test = X[-2:dataset_size]

Y_test1 = Y1[-2:dataset_size]

Y_test2 = Y2[-2:dataset_size]

#网络一

input = Input(shape=(2,))

x = Dense(units=16,activation='relu')(input)

output = Dense(units=1,activation='sigmoid',name='output1')(x)

#网络二

input2 = Input(shape=(2,))

x2 = Dense(units=16,activation='relu')(input2)

output2 = Dense(units=1,activation='sigmoid',name='output2')(x2)

#模型合并

model = Model(inputs=[input,input2],outputs=[output,output2])

model.summary()

model.compile(optimizer='rmsprop',loss='binary_crossentropy',loss_weights=[1.0,1.0])

model.fit([X_train,X_train],[Y_train1,Y_train2],batch_size=48,epochs=200)

print('x_test is :\n')

print(X_test)

print('y_test1 is :\n')

print(Y_test1)

print('y_test2 is :\n')

print(Y_test2)

predict = model.predict([X_test,X_test])

print('prediction is : \n')

print(predict[0])

print(predict[1])

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原文链接：https://blog.csdn.net/weixin_43392276/java/article/details/101757173