将3D张量输入到keras或tensorflow中的嵌入层？

Question

I want to build a network which takes in sentences as input to predict the sentiment. So my input looks something like (num of samples x num of sentences x num of words). I then want to feed this in an embedding layer to learn the word vectors which can be then summed to get sentence vector. Is this type of architecture possible in keras? or Tensorflow? From the documentation Keras's embedding layer only takes in input (nb_samples, sequence_length). Is there any work around possible?

Answer 1

I guess this class resolves for Keras:

class AnyShapeEmbedding(Embedding):
    '''
    This Embedding works with inputs of any number of dimensions.
    This can be accomplished by simply changing the output shape computation.
    '''
    #@overrides
    def compute_output_shape(self, input_shape):
        return input_shape + (self.output_dim,)