Comment faire le produit intérieur du lot dans tensorflow?

Question

J'ai deux a:[batch_size, dim] de b:[batch_size, dim] tenseur. Je veux faire le produit intérieur pour chaque paire dans le lot, générant c:[batch_size, 1], où c[i,0]=a[i,:].T*b[i,:]. Comment?

Answer 1

There is no native .dot_product method. However, a dot product between two vectors is just element-wise multiply summed, so the following example works:

import tensorflow as tf

# Arbitrarity, we'll use placeholders and allow batch size to vary,
# but fix vector dimensions.
# You can change this as you see fit
a = tf.placeholder(tf.float32, shape=(None, 3))
b = tf.placeholder(tf.float32, shape=(None, 3))

c = tf.reduce_sum( tf.multiply( a, b ), 1, keep_dims=True )

with tf.Session() as session:
    print( c.eval(
        feed_dict={ a: [[1,2,3],[4,5,6]], b: [[2,3,4],[5,6,7]] }
    ) )

The output is:

[[ 20.]
 [ 92.]]

Answer 2

Another option worth checking out is [tf.einsum][1] - it's essentially a simplified version of Einstein Notation.

Following along with Neil and dumkar's examples:

import tensorflow as tf

a = tf.placeholder(tf.float32, shape=(None, 3))
b = tf.placeholder(tf.float32, shape=(None, 3))

c = tf.einsum('ij,ij->i', a, b)

with tf.Session() as session:
    print( c.eval(
        feed_dict={ a: [[1,2,3],[4,5,6]], b: [[2,3,4],[5,6,7]] }
    ) )

The first argument to einsum is an equation representing the axes to be multiplied and summed over. The basic rules for an equation are:

1. Input-tensors are described by a comma-separated string of dimension-labels
2. Repeated labels indicate that the corresponding dimensions will be multiplied
3. The output-tensor is described by another string of dimension-labels representing corresponding inputs (or products)
4. Labels that are missing from the output string are summed over

In our case, ij,ij->i means that our inputs will be 2 matrices of equal shape (i,j), and our output will be a vector of shape (i,).

Once you get the hang of it, you'll find that einsum generalizes a huge number of other operations:

X = [[1, 2]]
Y = [[3, 4], [5, 6]]

einsum('ab->ba', X) == [[1],[2]]   # transpose
einsum('ab->a',  X) ==  [3]        # sum over last dimension
einsum('ab->',   X) ==   3         # sum over both dimensions

einsum('ab,bc->ac',  X, Y) == [[13,16]]          # matrix multiply
einsum('ab,bc->abc', X, Y) == [[[3,4],[10,12]]]  # multiply and broadcast

Unfortunately, einsum takes a pretty hefty performance hit when compared to a manual multiply+reduce. Where performance is critical, I'd definitely recommend sticking with Neil's solution.

Answer 3

Taking the diagonal of tf.tensordot also does what you want, if you set axis to e.g.

[[1], [1]]

I have adapted Neil Slater's example:

import tensorflow as tf

# Arbitrarity, we'll use placeholders and allow batch size to vary,
# but fix vector dimensions.
# You can change this as you see fit
a = tf.placeholder(tf.float32, shape=(None, 3))
b = tf.placeholder(tf.float32, shape=(None, 3))

c = tf.diag_part(tf.tensordot( a, b, axes=[[1],[1]]))

with tf.Session() as session:
    print( c.eval(
        feed_dict={ a: [[1,2,3],[4,5,6]], b: [[2,3,4],[5,6,7]] }
    ) )

which now also gives:

[ 20.  92.]

This might be suboptimal for large matrices though (see discussion here)