1、模型結果設計

2、程式碼

from keras import Input,Model
from keras.layers import Dense,Concatenate
import numpy as np
from keras.utils import plot_model
from numpy import random as rd

samples_n = 3000
samples_dim_01 = 2
samples_dim_02 = 2
# 樣本資料
x1 = rd.rand(samples_n,samples_dim_01)
x2 = rd.rand(samples_n,samples_dim_02)
y_1 = []
y_2 = []
y_3 = []
for x11,x22 in zip(x1,x2):
  y_1.append(np.sum(x11) + np.sum(x22))
  y_2.append(np.max([np.max(x11),np.max(x22)]))
  y_3.append(np.min([np.min(x11),np.min(x22)]))
y_1 = np.array(y_1)
y_1 = np.expand_dims(y_1,axis=1)
y_2 = np.array(y_2)
y_2 = np.expand_dims(y_2,axis=1)
y_3 = np.array(y_3)
y_3 = np.expand_dims(y_3,axis=1)

# 輸入層
inputs_01 = Input((samples_dim_01,),name='input_1')
inputs_02 = Input((samples_dim_02,name='input_2')
# 全連線層
dense_01 = Dense(units=3,name="dense_01",activation='softmax')(inputs_01)
dense_011 = Dense(units=3,name="dense_011",activation='softmax')(dense_01)
dense_02 = Dense(units=6,name="dense_02",activation='softmax')(inputs_02)
# 加入合併層
merge = Concatenate()([dense_011,dense_02])
# 分成兩類輸出 --- 輸出01
output_01 = Dense(units=6,activation="relu",name='output01')(merge)
output_011 = Dense(units=1,activation=None,name='output011')(output_01)
# 分成兩類輸出 --- 輸出02
output_02 = Dense(units=1,name='output02')(merge)
# 分成兩類輸出 --- 輸出03
output_03 = Dense(units=1,name='output03')(merge)
# 構造一個新模型
model = Model(inputs=[inputs_01,inputs_02],outputs=[output_011,output_02,output_03
                           ])
# 顯示模型情況
plot_model(model,show_shapes=True)
print(model.summary())
# # 編譯
# model.compile(optimizer="adam",loss='mean_squared_error',loss_weights=[1,#                                     0.8,#                                     0.8
#                                     ])
# # 訓練
# model.fit([x1,x2],[y_1,#           y_2,#           y_3
#           ],epochs=50,batch_size=32,validation_split=0.1)

# 以下的方法可靈活設定
model.compile(optimizer='adam',loss={'output011': 'mean_squared_error','output02': 'mean_squared_error','output03': 'mean_squared_error'},loss_weights={'output011': 1,'output02': 0.8,'output03': 0.8})
model.fit({'input_1': x1,'input_2': x2},{'output011': y_1,'output02': y_2,'output03': y_3},validation_split=0.1)

# 預測
test_x1 = rd.rand(1,2)
test_x2 = rd.rand(1,2)
test_y = model.predict(x=[test_x1,test_x2])
# 測試
print("測試結果：")
print("test_x1:",test_x1,"test_x2:",test_x2,"y:",test_y,np.sum(test_x1) + np.sum(test_x2))

補充知識：Keras多輸出（多工）如何設定fit_generator

在使用Keras的時候，因為需要考慮到效率問題，需要修改fit_generator來適應多輸出

# create model
model = Model(inputs=x_inp,outputs=[main_pred,aux_pred])
# complie model
model.compile(
  optimizer=optimizers.Adam(lr=learning_rate),loss={"main": weighted_binary_crossentropy(weights),"auxiliary":weighted_binary_crossentropy(weights)},loss_weights={"main": 0.5,"auxiliary": 0.5},metrics=[metrics.binary_accuracy],)
# Train model
model.fit_generator(
  train_gen,epochs=num_epochs,verbose=0,shuffle=True
)

看Keras官方文件：

generator: A generator or an instance of Sequence (keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either

a tuple (inputs,targets)

a tuple (inputs,targets,sample_weights).

Keras設計多輸出（多工）使用fit_generator的步驟如下：

根據官方文件，定義一個generator或者一個class繼承Sequence

class Batch_generator(Sequence):
 """
 用於產生batch_1,batch_2(記住是numpy.array格式轉換)
 """
 y_batch = {'main':batch_1,'auxiliary':batch_2}
 return X_batch,y_batch

# or in another way
def batch_generator():
 """
 用於產生batch_1,batch_2(記住是numpy.array格式轉換)
 """
 yield X_batch,{'main': batch_1,'auxiliary':batch_2}

重要的事情說三遍(親自採坑，搜了一大圈才發現滴)：

如果是多輸出（多工）的時候，這裡的target是字典型別

如果是多輸出（多工）的時候，這裡的target是字典型別

如果是多輸出（多工）的時候，這裡的target是字典型別

以上這篇Keras-多輸入多輸出例項(多工)就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。