tensorflow 中的 tf.matmul(X,weight) 与 tf.matmul(X,tf.traspose(weight))

Question

In standard ANN for fully connected layers we are using the following formula: tf.matmul(X,weight) + bias . Which is clear to me, as we use matrix multiplication in order to connect input with th hidden layer.

But in GloVe implementation( https://nlp.stanford.edu/projects/glove/ ) we are using the following formula for embeddings multiplication: tf.matmul(W, tf.transpose(U)) what confuses me is tf.transpose(U) part. Why do we use tf.matmul(W, tf.transpose(U)) instead of tf.matmul(W, U) ?

Answer 1

It has to do with the choice of column vs row orientation for the vectors.

Note that weight is the second parameter here:

tf.matmul(X, weight)

But the first parameter, W , here:

tf.matmul(W, tf.transpose(U))

So what you are seeing is a practical application of the following matrix transpose identity:

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To bring it back to your example, let's assume 10 inputs and 20 outputs.

The first approach uses row vectors. A single input X would be a 1x10 matrix, called a row vector because it has a single row. To match, the weight matrix needs to be 10x20 to produce an output of size 20 .

But in the second approach the multiplication is reversed. That is a hint that everything is using column vectors. If the multiplication is reversed, then everything gets a transpose. So this example is using column vectors, so named because they have a single column.

That's why the transpose is there. The way they GLoVe authors have done their notation, with the multiplication reversed, the weight matrix W must already be transposed to 20x10 instead of 10x20 . And they must be expecting a 20x1 column vector for the output.

So if the input vector U is naturally a 1x10 row vector, it also has to be transposed, to a 10x1 column vector, to fit in with everything else.

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Basically you should pick row vectors or column vectors, all the time, and then the order of multiplications and the transposition of the weights is determined for you.

Personally I think that column vectors, as used by GloVe, are awkward and unnatural compared to row vectors. It's better to have the multiplication ordering follow the data flow ordering.