eval_fnc.py 文件源码

def prepare_model(ninputs=9600, n_feats=45,nclass=4,n_tfidf=10001):
    inp1 = Input(shape=(ninputs,))
    inp2 = Input(shape=(n_feats,))
    inp3 = Input(shape=(n_tfidf,))
    reg = 0.00005
    out_neurons1 = 500
    #out_neurons2 = 20
    #out_neurons2 = 10
    m1 = Dense(input_dim=ninputs, output_dim=out_neurons1,activation='sigmoid'\
                      ,kernel_regularizer=regularizers.l2(0.00000001))(inp1)
    m1 = Dropout(0.2)(m1)
    m1 = Dense(100,activation='sigmoid')(m1)
    #m1 = Dropout(0.2)(m1)
    #m1 = Dense(4, activation='sigmoid')(m1)

    #m2 = Dense(input_dim=n_feats, output_dim=n_feats,activation='relu')(inp2)
    m2 = Dense(50,activation='relu')(inp2)
    #m2=Dense(4,activation='relu')(m2)

    m3 = Dense(500, input_dim=n_tfidf, activation='relu',\
                    kernel_regularizer=regularizers.l2(reg))(inp3)

    m3 = Dropout(0.4)(m3)
    m3 = Dense(50, activation='relu')(m3)
    #m3 = Dropout(0.4)(m3)
    #m3 = Dense(4, activation='softmax')(m3)


    #m1 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='sigmoid')(m1)
    #m2 = Dense(input_dim=ninputs, output_dim=out_neurons2,activation='softmax')(m2)

    m = Merge(mode='concat')([m1,m2,m3])

    #mul = Multiply()([m1,m2])
    #add = Abs()([m1,m2])
    #m = Merge(mode='concat')([mul,add])

    score = Dense(output_dim=nclass,activation='softmax')(m)
    model = Model([inp1,inp2,inp3],score)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model