Python 在Keras中使用具有重现层的时间分布

我想在每个批次的几个不同序列上运行一个LSTM，然后加入最后的输出。以下是我一直在尝试的：

from keras.layers import Dense, Input, LSTM, Embedding, TimeDistributed

num_sentences = 4
num_features = 3
num_time_steps = 5

inputs = Input([num_sentences, num_time_steps])
emb_layer = Embedding(10, num_features)
embedded = emb_layer(inputs)
lstm_layer = LSTM(4)

shape = [num_sentences, num_time_steps, num_features]
lstm_outputs = TimeDistributed(lstm_layer, input_shape=shape)(embedded)

这给了我以下错误：

Traceback (most recent call last):
  File "test.py", line 12, in <module>
    lstm_outputs = TimeDistributed(lstm_layer, input_shape=shape)(embedded)
  File "/Users/erick/anaconda2/lib/python2.7/site-packages/keras/engine/topology.py", line 546, in __call__
    self.build(input_shapes[0])
  File "/Users/erick/anaconda2/lib/python2.7/site-packages/keras/layers/wrappers.py", line 94, in build
    self.layer.build(child_input_shape)
  File "/Users/erick/anaconda2/lib/python2.7/site-packages/keras/layers/recurrent.py", line 702, in build
    self.input_dim = input_shape[2]
IndexError: tuple index out of range

我试图在TimeDistributed中省略input_形状参数，但它没有改变任何事情。

input_形状需要是LSTM层的参数，而不是作为包装器的TimeDistributed。通过省略它，我觉得一切都很好：

from keras.layers import Dense, Input, LSTM, Embedding, TimeDistributed

num_sentences = 4
num_features = 3
num_time_steps = 5

inputs = Input([num_sentences, num_time_steps])
emb_layer = Embedding(10, num_features)
embedded = emb_layer(inputs)
lstm_layer = LSTM(4)

shape = [num_sentences, num_time_steps, num_features]
lstm_outputs = TimeDistributed(lstm_layer)(embedded)


#OUTPUT:
Using TensorFlow backend.
[Finished in 1.5s]

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在尝试了米切托努的答案并犯了同样的错误后，我意识到我的keras版本可能已经过时了。事实上，它运行的是keras 1.2，代码在2.0上运行良好