model.py 文件源码

def model(X_train, Y_train, X_test, Y_test):
    W_maxnorm = 3
    DROPOUT = {{choice([0.3,0.5,0.7])}}

    model = Sequential()
    model.add(Convolution2D(64, 1, 5, border_mode='same', input_shape=(4, 1, DATASIZE),activation='relu',W_constraint=maxnorm(W_maxnorm)))
    model.add(MaxPooling2D(pool_size=(1, 5),strides=(1,3)))
    model.add(Flatten())

    model.add(Dense(32,activation='relu'))
    model.add(Dropout(DROPOUT))
    model.add(Dense(32,activation='relu'))
    model.add(Dropout(DROPOUT))
    model.add(Dense(2))
    model.add(Activation('softmax'))

    myoptimizer = RMSprop(lr={{choice([0.01,0.001,0.0001])}}, rho=0.9, epsilon=1e-06)
    mylossfunc = 'categorical_crossentropy'
    model.compile(loss=mylossfunc, optimizer=myoptimizer,metrics=['accuracy'])
    model.fit(X_train, Y_train, batch_size=100, nb_epoch=5,validation_split=0.1)

    score, acc = model.evaluate(X_test,Y_test)
    model_arch = 'MODEL_ARCH'
    bestaccfile = join('TOPDIR',model_arch,model_arch+'_hyperbestacc')
    reportAcc(acc,score,bestaccfile)

    return {'loss': score, 'status': STATUS_OK,'model':(model.to_json(),myoptimizer,mylossfunc)}