from keras import backend as K
a = K.ones((3,4,5,2))
b = K.ones((2,5,3,7))
c = K.dot(a, b)
print(c.shape)

returns

ValueError: Dimensions must be equal, but are 2 and 3 for 'MatMul' (op: 'MatMul') with input shapes: [60,2], [3,70].

It looks like $60 = 3 \times 4 \times 5$ and $70 = 5 \times 3 \times 7$.

What’s happening ?

### Matrix multiplication when tensors are matrices

The matrix multiplication is performed with tf.matmul in Tensorflow or K.dot in Keras :

from keras import backend as K
a = K.ones((3,4))
b = K.ones((4,5))
c = K.dot(a, b)
print(c.shape)

or

import tensorflow as tf
a = tf.ones((3,4))
b = tf.ones((4,5))
c = tf.matmul(a, b)
print(c.shape)

returns a tensor of shape (3,5) in both cases. Simple.

### Keras dot

from keras import backend as K
a = K.ones((2,3,4))
b = K.ones((7,4,5))
c = K.dot(a, b)
print(c.shape)

returns a tensor of shape (2, 3, 7, 5).

The matrix multiplication is performed along the 4 values of :

• the last dimension of the first tensor

• the before-last dimension of the second tensor

from keras import backend as K
a = K.ones((1, 2, 3 , 4))
b = K.ones((8, 7, 4, 5))
c = K.dot(a, b)
print(c.shape)

returns a tensor of size

• a.shape minus last dimension => (1,2,3)

concatenated with

• b.shape minus the before last dimension => (8,7,5)

hence : (1, 2, 3, 8, 7, 5)

where each value is given by the formula :

Not very easy to visualize when ranks of tensors are above 2 :).

Note that this behavior is specific to Keras dot. It is a reproduction of Theano behavior.

In particular, it enables to perform a kind of dot product:

from keras import backend as K
a = K.ones((1, 2, 4))
b = K.ones((8, 7, 4, 5))
c = K.dot(a, b)
print(c.shape)

returns a tensor of shape (1, 2, 8, 7, 5).

### Batch Matrix Multiplication : tf.matmul or K.batch_dot

There is another operator, K.batch_dot that works the same as tf.matmul

from keras import backend as K
a = K.ones((9, 8, 7, 4, 2))
b = K.ones((9, 8, 7, 2, 5))
c = K.batch_dot(a, b)
print(c.shape)

or

import tensorflow as tf
a = tf.ones((9, 8, 7, 4, 2))
b = tf.ones((9, 8, 7, 2, 5))
c = tf.matmul(a, b)
print(c.shape)

returns a tensor of shape (9, 8, 7, 4, 5) in both cases.

So, here the multiplication has been performed considering (9,8,7) as the batch size or equivalent. That could be a position in the image (B,H,W) and for each position we’d like to multiply two matrices.

The tf.matmul enables you to transpose on the fly one of the matrices

import tensorflow as tf
a = tf.ones((9, 8, 7, 4, 2))
b = tf.ones((9, 8, 7, 5, 2))
c = tf.matmul(a, b, transpose_b=True)
print(c.shape)

or

import tensorflow as tf
a = tf.ones((9, 8, 7, 2, 4))
b = tf.ones((9, 8, 7, 2, 5))
c = tf.matmul(a, b, transpose_a=True)
print(c.shape)

also returns a tensor of shape (9, 8, 7, 4, 5) in both cases.

For the K.batch_dot, you can provide the axes:

from keras import backend as K
a = K.ones((9, 8, 7, 4, 2))
b = K.ones((9, 8, 7, 5, 2))
c = K.batch_dot(a, b, axes=4)
print(c.shape)

or

from keras import backend as K
a = K.ones((9, 8, 7, 4, 2))
b = K.ones((9, 8, 7, 2, 5))
c = K.batch_dot(a, b, axes=(4,3))
print(c.shape)

are all equivalent formulations. The K.batch_dot has also the ability to work with tensors of different ranks, simulating a dot product:

from keras import backend as K
a = K.ones((9, 8, 7, 2))
b = K.ones((9, 8, 7, 2, 5))
c = K.batch_dot(a, b, axes=(3,3))
print(c.shape)

returns a tensor of shape (9, 8, 7, 5).

### Sparse Dense Matrix multiplication

It is the operator tf.sparse_tensor_dense_matmul when the first matrix is a tf.SparseTensor object.

Note that a_is_sparse=True or b_is_sparse=True arguments are also available in tf.matmul function.

This is particularly useful for Embeddings in NLP.

Well done!