功能API链接前馈网络和卷积神经网络

Question

现在，我有两个网络f和g，第一个在任务1上受过训练，第二个在任务2上受过训练。我将数据标记为属于任务1或任务2。我如何构建以下（可训练的）自定义建筑：

x->确定是1还是2->相应地传递给f或g？

我以前从未使用过这样的分支体系结构...

Answer 1

我试图通过下面显示的Sample Code来演示您的需求。如果您不是您所需要的，请告诉我，并提供更多详细信息，我们将竭诚为您服务。

根据问题，我们正在尝试实现2个任务，Task 1 --> Regression（前馈神经网络）和Task 2 --> CNN。我们将基于标签从现有数据集中形成2个数据集，无论它属于Task 1 --> Data_T1和Task 2 --> Data_T2。

然后使用Functional API，我们可以传递Multiple Inputs，我们可以获得Multiple Outputs。

代码如下所示：

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten
import pandas as pd

F1 = [1,2,3,4,5,6,7,8,9,10]
F2 = [1,2,3,4,5,6,7,8,9,10]
F3 = [1,2,3,4,5,6,7,8,9,10]
Task = ['t1', 't1', 't2', 't1', 't2', 't2', 't2', 't1', 't1', 't2']

Dict = {'F1': F1, 'F2':F2, 'F3':F3, 'Task':Task} # Column Task tells us whether the Data belongs to Task1 or Task2

Data = pd.DataFrame(Dict) #Create a Dummy Data Frame

Data_T1 = Data[Data['Task']=='t1']
Data_T1 = Data_T1.drop(columns = ['Task'])

Data_T2 = Data[Data['Task']=='t2']
Data_T2 = Data_T2.drop(columns = ['Task'])

Input1 = ...
Input2 = ...

Number_Of_Classes = 3
# Regression Model
D1 = Dense(10, activation = 'relu')(Input1)
Out_Task1 = Dense(1, activation = 'linear') 
# CNN Model
Conv1 = Conv2D(16, (3,3), activation = 'relu')(Input2)
Conv2 = Conv2D(32, (3,3, activation = 'relu'))(Conv1)
Flatten = Flatten()(Conv2)
D2_1 = Dense(10, activation = 'relu')
Out_Task2 = Dense(Number_Of_Classes, activation = 'softmax')

model = Model(inputs = [Input1, Input2], outputs = [Out_Task1, Out_Task2])

model.compile....

model.fit([Data_T1, Data_T2], .....)