from keras.datasets import boston_housing
from keras.models import Sequential
from keras.layers import Dense,BatchNormalization,Dropout,Reshape,Flatten
from keras.layers.convolutional import Conv2D,MaxPooling2D,Conv1D,MaxPooling1D
import numpy as np
import os

(x_train, y_train), (x_test, y_test) = boston_housing.load_data()

 
def build0():
    model=Sequential(name=‘boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

def build1():
    model=Sequential(name=‘boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Dense(13))
    model.add(Dropout(
 
0.5))
    model.add(Dense(1))
    return model

def build2():
    model=Sequential(name=‘boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Dense(13,activation=‘tanh‘))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

def build3():
    model=Sequential(name=‘
 
boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Dense(13,activation=‘sigmoid‘))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

def build4():
    model=Sequential(name=‘boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Reshape((13,1)))
    model.add(Conv1D(filters=13,strides=1,padding=‘same‘,kernel_size=2,activation=‘sigmoid‘))
    model.add(Conv1D(filters=26, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(MaxPooling1D(pool_size=2,strides=1,padding=‘same‘))
    model.add(Conv1D(filters=52, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(Conv1D(filters=104, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(MaxPooling1D(pool_size=2, strides=1, padding=‘same‘))
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

def build5():
    model = Sequential(name=‘boston‘)
    model.add(BatchNormalization(input_shape=(13,)))
    model.add(Reshape((13, 1,1)))
    model.add(Conv2D(filters=13, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(Conv2D(filters=26, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(MaxPooling2D(pool_size=2, strides=1, padding=‘same‘))
    model.add(Conv2D(filters=52, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(Conv2D(filters=104, strides=1, padding=‘same‘, kernel_size=2, activation=‘sigmoid‘))
    model.add(MaxPooling2D(pool_size=2, strides=1, padding=‘same‘))
    model.add(Flatten())
    model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

for i in range(6):
    model=eval("build"+str(i)+"()")
    model.compile(‘adam‘,‘mae‘)#3.0895
    history=model.fit(x_train,y_train,batch_size=404,epochs=10000,verbose=1,validation_data=(x_test,y_test))
    print(history.history)

    f=open("result.txt",‘a‘)
    f.write(str(history.history[‘val_loss‘][-1])+"\n")
    f.close()

其中build4方法預測最準確mae loss最小

[ML]keras波士頓房價預測模型