Keras - 使用双向 LSTM 的堆栈 Conv1D 中的错误答案

【问题标题】：Keras - Error in stack Conv1D with Bidirectional LSTMKeras - 使用双向 LSTM 的堆栈 Conv1D 中的错误
【发布时间】：2018-08-20 15:34:04
【问题描述】：

您好，我正在尝试使用嵌入进行多类分类，并使用双向 LSTM 堆叠 Conv1D，这是我的脚本：

embed_dim = 100
lstm_out = 128
max_features = 5000

model8 = Sequential()
model8.add(Embedding(max_features, embed_dim, input_length =    X.shape[1]))
model8.add(Dropout(0.2))
model8.add(Conv1D(filters=100, kernel_size=3, padding='same',  activation='relu'))
model8.add(MaxPooling1D(pool_size=2))
model8.add(Bidirectional(LSTM(lstm_out)))
model8.add(Dense(124,activation='softmax'))
model8.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print model8.summary()

我收到如下错误消息：

TypeErrorTraceback (most recent call last)
<ipython-input-51-6c831fc4581f> in <module>()
      9 model8.add(Embedding(max_features, embed_dim))
     10 model8.add(Dropout(0.2))
---> 11 model8.add(Conv1D(filters=100, kernel_size=3, padding='same', activation='relu'))
     12 model8.add(MaxPooling1D(pool_size=2))
     13 model8.add(Bidirectional(LSTM(lstm_out)))

/jupyter/local/lib/python2.7/site-packages/tensorflow/python/training/checkpointable/base.pyc in _method_wrapper(self, *args, **kwargs)
    362     self._setattr_tracking = False  # pylint: disable=protected-access
    363     try:
--> 364       method(self, *args, **kwargs)
    365     finally:
    366       self._setattr_tracking = previous_value  # pylint: disable=protected-access

/jupyter/local/lib/python2.7/site-packages/tensorflow/python/keras/engine/sequential.pyc in add(self, layer)
    128       raise TypeError('The added layer must be '
    129                       'an instance of class Layer. '
--> 130                       'Found: ' + str(layer))
    131     self.built = False
    132     if not self._layers:

TypeError: The added layer must be an instance of class Layer. Found: <keras.layers.convolutional.Conv1D object at 0x7f62907f8590>

我做错了什么？谢谢！

【问题讨论】：

您的代码在我的机器上运行没有任何错误。你使用的是什么版本的 Keras，即print(keras.__version__)？
我使用的是 Keras 2.2.2 和 Python 2.7
能否也将导入语句添加到您的帖子中？
from tensorflow.python.keras.preprocessing.sequence import pad_sequences from tensorflow.python.keras.models import Sequential from tensorflow.python.keras.layers import Dense, Embedding, LSTM, Dropout, SpatialDropout1D, Bidirectional from sklearn.model_selection import train_test_split from tensorflow.python.keras import utils from tensorflow.python.keras.utils.np_utils import to_categorical
我在导入的图层中没有看到Conv1D 和MaxPooling1D？！确保它们也是从同一个模块导入的，即tensorflow.python.keras.layers。

标签： python keras conv-neural-network lstm embedding

【解决方案1】：

from keras.layers import Dense, Embedding, Dropout, LSTM
from keras.models import Sequential
from keras.layers import Bidirectional
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D

embed_dim = 100
lstm_out = 128
max_features = 5000

model8 = Sequential()
model8.add(Embedding(max_features, embed_dim, input_length = X.shape[1]))
model8.add(Dropout(0.2))
model8.add(Conv1D(filters=100, kernel_size=3, padding='same',  activation='relu'))
model8.add(MaxPooling1D(pool_size=2))
model8.add(Bidirectional(LSTM(lstm_out)))
model8.add(Dense(124,activation='softmax'))
model8.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = 
['accuracy'])
print(model8.summary())

打印模型摘要没有任何错误：

 _________________________________________________________________
 Layer (type)                 Output Shape              Param #   
 =================================================================
 embedding_8 (Embedding)      (None, 100, 100)          500000    
 _________________________________________________________________
 dropout_5 (Dropout)          (None, 100, 100)          0         
 _________________________________________________________________
 conv1d_3 (Conv1D)            (None, 100, 100)          30100     
 _________________________________________________________________
 max_pooling1d_3 (MaxPooling1 (None, 50, 100)           0         
 _________________________________________________________________
 bidirectional_7 (Bidirection (None, 256)               234496    
 _________________________________________________________________
 dense_7 (Dense)              (None, 124)               31868     
 =================================================================
 Total params: 796,464
 Trainable params: 796,464
 Non-trainable params: 0
 _________________________________________________________________
 None

【讨论】：