Question

所以我正在尝试建立一个cnn网络。我有一个热编码的“ scipy.sparse.coo.coo_matrix”，大小为“（109248，101）”。我需要使用给定的数据构建一个两层的conv1D模型，并与另一个LSTM层连接以进一步处理。我没有得到构建conv1D层的部分任何帮助将不胜感激。...

我尝试使用以下方式来构建网络文档。我也尝试了功能性的方式来构建网络，但似乎我做错了

所以我尝试了这个：

from keras.layers import Conv1D


# input_tensor = Input(shape=(None, 101))

model = Sequential()
model.add(Conv1D(input_shape=(101, 1),
                 filters=16,
                 kernel_size=4,
                 padding='same'))

model.add(Conv1D(filters=16, kernel_size=4))
model.add(Flatten())

和这个

x_rest = Conv1D(input_shape=(101,1), filters=16, kernel_size=4, padding='same')

x2 = Conv1D(input_shape=(101,1), filters=16, kernel_size=4, padding='same')(x_rest)



out2 = Flatten()(x2)

他们似乎都不起作用

总是会出现

这样的错误

层concatenate_4的输入不是符号张量。收到的类型：。全输入：[，]。该层的所有输入都应为张量。

这是我要构建的架构

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
main_input (InputLayer)         (None, 150)          0                                            
__________________________________________________________________________________________________
rest_input (InputLayer)         (None, 101, 1)       0                                            
__________________________________________________________________________________________________
embedding_3 (Embedding)         (None, 150, 300)     16873200    main_input[0][0]                 
__________________________________________________________________________________________________
conv1d_24 (Conv1D)              (None, 99, 64)       256         rest_input[0][0]                 
__________________________________________________________________________________________________
lstm_3 (LSTM)                   (None, 150, 32)      42624       embedding_3[0][0]                
__________________________________________________________________________________________________
conv1d_25 (Conv1D)              (None, 97, 64)       12352       conv1d_24[0][0]                  
__________________________________________________________________________________________________
flatten_5 (Flatten)             (None, 4800)         0           lstm_3[0][0]                     
__________________________________________________________________________________________________
flatten_7 (Flatten)             (None, 6208)         0           conv1d_25[0][0]                  
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, 11008)        0           flatten_5[0][0]                  
                                                                 flatten_7[0][0]                  
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 1)            11009       concatenate_3[0][0]              
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 1)            0           dense_7[0][0]                    
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 1)            2           dropout_3[0][0]                  
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 1)            2           dense_8[0][0]                    
__________________________________________________________________________________________________
main_output (Dense)             (None, 1)            2           dense_9[0][0]                    
==================================================================================================

Answer 1

您的代码的第一个版本似乎正在运行。这是它构建的模型：

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1d_3 (Conv1D)            (None, 101, 16)           80        
_________________________________________________________________
conv1d_4 (Conv1D)            (None, 98, 16)            1040      
_________________________________________________________________
flatten_1 (Flatten)          (None, 1568)              0         
=================================================================
Total params: 1,120
Trainable params: 1,120
Non-trainable params: 0
_________________________________________________________________

问题似乎与您接下来要使用的LSTM层有关（尽管由于您未提供代码的这一部分，所以我无法为您提供帮助）。您可能会找到解决方法here。

如何在喀拉拉邦创建功能性的CONV1D图层？

1 个答案: