如何改变keras中Conv1D卷积误差的输入维的形状?

Question

我有一个二元分类问题。我想包括一个Conv1D层，但我有一个麻烦的输入形状从2D从3D改变输入形状(https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)。

所以我的代码是

#Hyperparameters
EMBEDDING_DIM = 50
MAXLEN = 500 #1000, 1400
VOCAB_SIZE =  33713
DENSE1_DIM = 64
DENSE2_DIM = 32
LSTM1_DIM = 32 
LSTM2_DIM = 16
WD = 0.001
FILTERS = 64  
KERNEL_SIZE = 5
# Stacked hybrid model
model_lstm = tf.keras.Sequential([
tf.keras.layers.Embedding(VOCAB_SIZE+1, EMBEDDING_DIM, input_length=MAXLEN,weights=[EMBEDDINGS_MATRIX], trainable=False),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM1_DIM, dropout=0.5, kernel_regularizer = regularizers.l2(WD), return_sequences=True)), 
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM2_DIM, dropout=0.5, kernel_regularizer = regularizers.l2(WD))), 
tf.keras.layers.Dense(DENSE2_DIM, activation='relu'),
#    tf.keras.layers.Conv1D(FILTERS, KERNEL_SIZE, activation='relu'),
#    tf.keras.layers.Dropout(0.1),
#    tf.keras.layers.GlobalAveragePooling1D(), 
#    tf.keras.layers.Dense(1, activation='sigmoid')
])
...

给出了这个摘要

Model: "sequential_6"
_________________________________________________________________
Layer (type)                Output Shape              Param #   
=================================================================
embedding_10 (Embedding)    (None, 500, 50)           1685700   

bidirectional_19 (Bidirecti  (None, 500, 64)          21248     
onal)                                                           

bidirectional_20 (Bidirecti  (None, 32)               10368     
onal)                                                           

dense_11 (Dense)            (None, 32)                1056      

=================================================================
Total params: 1,718,372
Trainable params: 32,672
Non-trainable params: 1,685,700

如果我使用Conv1D图层，我会得到这个错误:

ValueError: Input 0 of layer "conv1d_4" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 32)

我已经尝试过，例如，input_shape = (None, 16,32)作为Conv1D层的参数，但它不这样工作…

谢谢。

Answer 1

可以通过添加tf.keras.layers.Reshape层来改变数据的形状。

EMBEDDING_DIM = 50
MAXLEN = 500 #1000, 1400
VOCAB_SIZE =  33713
DENSE1_DIM = 64
DENSE2_DIM = 32
LSTM1_DIM = 32 
LSTM2_DIM = 16
WD = 0.001
FILTERS = 64  
KERNEL_SIZE = 5
EMBEDDINGS_MATRIX = np.zeros((VOCAB_SIZE+1, EMBEDDING_DIM))
# Stacked hybrid model
model_lstm = tf.keras.Sequential([
tf.keras.layers.Embedding(VOCAB_SIZE+1, EMBEDDING_DIM, input_length=MAXLEN,weights=[EMBEDDINGS_MATRIX], trainable=False),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM1_DIM, dropout=0.5, kernel_regularizer = tf.keras.regularizers.L1(WD), return_sequences=True)), 
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(LSTM2_DIM, dropout=0.5, kernel_regularizer = tf.keras.regularizers.L1(WD))), 
tf.keras.layers.Dense(DENSE2_DIM, activation='relu'),
tf.keras.layers.Reshape((32,1)),
tf.keras.layers.Conv1D(FILTERS, KERNEL_SIZE, activation='relu'),
tf.keras.layers.Dropout(0.1),
tf.keras.layers.GlobalAveragePooling1D(), 
tf.keras.layers.Dense(1, activation='sigmoid')
])
model_lstm.summary()

输出如下:

Model: "sequential_14"
_________________________________________________________________
Layer (type)                Output Shape              Param #   
=================================================================
embedding_16 (Embedding)    (None, 500, 50)           1685700   

bidirectional_8 (Bidirectio  (None, 500, 64)          21248     
nal)                                                            

bidirectional_9 (Bidirectio  (None, 32)               10368     
nal)                                                            

dense_15 (Dense)            (None, 32)                1056      

reshape_3 (Reshape)         (None, 32, 1)             0         

conv1d (Conv1D)             (None, 28, 64)            384       

=================================================================
Total params: 1,718,756
Trainable params: 33,056
Non-trainable params: 1,685,700
_________________________________________________________________