在隐藏层keras之后添加两层答案

【问题标题】：Add two layers after a hidden layer keras在隐藏层keras之后添加两层
【发布时间】：2020-02-02 14:17:04
【问题描述】：

我正在尝试获得这样的神经网络模型：

     input
       |
     hidden
   /       \
hidden   output2
   |
output1

下面是一个简单的代码示例：

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # from here I would like to add a new neural network
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

如何得到预期的模型？

如果我问了一个愚蠢的问题，请见谅，我是人工智能的初学者。

【问题讨论】：

我认为您必须从顺序模型转移到执行此操作。您的代码很可能类似于：x = Layer1(input); x = Layer2(x)...

标签： machine-learning keras neural-network artificial-intelligence conv-neural-network

【解决方案1】：

您可以使用 keras 函数式 API 而不是顺序 API 来完成它，如下所示：

from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D

num_classes = 10
inp= Input(shape=input_shape)
conv1 = Conv2D(32, kernel_size=(3,3), activation='relu')(inp)
conv2 = Conv2D(64, (3, 3), activation='relu')(conv1)
max_pool = MaxPooling2D(pool_size=(2, 2))(conv2)
flat = Flatten()(max_pool)

hidden1 = Dense(128, activation='relu')(flat)
output1 = Dense(num_classes, activation='softmax')(hidden1)

hidden2 = Dense(10, activation='relu')(flat)  #specify the number of hidden units
output2 = Dense(3, activation='softmax')(hidden2) #specify the number of classes

model = Model(inputs=inp, outputs=[output1 ,output2])

您的网络如下所示：

Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_7 (InputLayer)            (None, 64, 256, 256) 0                                            
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 62, 254, 32)  73760       input_7[0][0]                    
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 60, 252, 64)  18496       conv2d_10[0][0]                  
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)  (None, 30, 126, 64)  0           conv2d_11[0][0]                  
__________________________________________________________________________________________________
flatten_4 (Flatten)             (None, 241920)       0           max_pooling2d_4[0][0]            
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 128)          30965888    flatten_4[0][0]                  
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 10)           2419210     flatten_4[0][0]                  
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 10)           1290        dense_6[0][0]                    
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 3)            33          dense_8[0][0]                    
==================================================================================================
Total params: 33,478,677
Trainable params: 33,478,677
Non-trainable params: 0

希望这会有所帮助！

【讨论】：