如何在喀拉拉邦創建功能性的CONV1D圖層？

Question

因此，我正在嘗試建立一個CNN網絡。 我有一個熱編碼的“ scipy.sparse.coo.coo_matrix”，大小為“（109248，101）”。 我需要使用給定的數據構建一個兩層的conv1D模型，並與另一個LSTM層連接以進行進一步處理。我沒有准備構建conv1D層的任何部分。

我嘗試使用以下方式構建網絡文檔。我也嘗試了功能性方式構建網絡，但似乎我做錯了

所以我嘗試了這個：

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())

和這個

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)

他們兩個似乎都不起作用

總是會拋出錯誤，例如

層concatenate_4的調用不是符號張量。 收到的類型：。 全輸入：[，]。 該層的所有輸入應為張量。

這是我要建立的架構

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

您的代碼的第一個版本似乎正在運行。 這是它構建的模型：

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
_________________________________________________________________

看來問題與您接下來要使用的LSTM層有關（盡管由於您未提供代碼的這一部分，所以我無法為您提供幫助）。 您可以在此處找到解決方案。