I have some data represented by input_x. It is a tensor of unknown size (should be inputted by batch) and each item there is of size n. input_x undergoes tf..embedding_lookout, so that embed now has dimensions [?, n, m] where m is the embedding size and ? refers to the unknown batch size.
This is described here:
input_x = tf.placeholder(tf.int32, [None, n], name="input_x")
embed = tf..embedding_lookup(W, input_x)
I'm now trying to multiply each sample in my input data (which is now an embedding matrix) by a matrix variable, U, and I can't seem to get how to do that.
I first tried using tf.matmul but it gives an error due to mismatch in shapes. I then tried the following, by expanding the dimension of U and applying batch_matmul (I also tried the function from tf..math_ops., the result was the same):
U = tf.Variable( ... )
U1 = tf.expand_dims(U,0)
h=tf.batch_matmul(embed, U1)
This passes the initial compilation, but then when actual data is applied, I get the following error:
In[0].dim(0) and In[1].dim(0) must be the same: [64,58,128] vs [1,128,128]
I also know why this is happening - I replicated the dimension of U and it is now 1, but the minibatch size, 64, doesn't fit.
How can I do that matrix multiplication on my tensor-matrix input correctly (for unknown batch size)?
Thanks very much!