Embedding layer is a fully connected layer that projects the sparse representation to a dense vector. code example

Example: embedding dimensionality in Rnn

[[6, 2], [3, 1], [7, 4], [8, 1], [9], [10], [5, 4], [11, 3], [5, 1], [12, 13, 2, 14]]
 
[[ 6  2  0  0]
 [ 3  1  0  0]
 [ 7  4  0  0]
 [ 8  1  0  0]
 [ 9  0  0  0]
 [10  0  0  0]
 [ 5  4  0  0]
 [11  3  0  0]
 [ 5  1  0  0]
 [12 13  2 14]]
 
Loaded 400000 word vectors.
 
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
embedding_1 (Embedding)      (None, 4, 100)            1500
_________________________________________________________________
flatten_1 (Flatten)          (None, 400)               0
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 401
=================================================================
Total params: 1,901
Trainable params: 401
Non-trainable params: 1,500
_________________________________________________________________
 
 
Accuracy: 100.000000