I am trying to make an Embedding layer in Keras.
My input size is 3d: (batch, 8, 6), and I want to have an embedding for the last dimension.
So the embedding should work as (batch*8, 6) -> embedding output
But I don't want to keep this batchsize for all the learning step, just for the embedding layer.
I think one of the solution is seperating 8 inputs and applying the embedding to each input.
But then this embedding layer is not the same as one big embedding layer.
Is there any possible solution? Thanks!
The solution is very simple:
input_shape = (8,6)
And pass through embedding. You will get exactly what you want.
from keras.layers import *
from keras.models import *
ins = Input((8,6))
out = Embedding(10, 15)(ins)
model = Model(ins, out)
model.summary()
Where 10 is the dictionary size (number of words or similars) and 15 is the embedding size (the resulting dimension).
Resulting summary:
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 8, 6) 0
_________________________________________________________________
embedding_1 (Embedding) (None, 8, 6, 15) 150
=================================================================
Total params: 150
Trainable params: 150
Non-trainable params: 0
_________________________________________________________________