Difference in matrix multiplication tensorflow vs numpy

Question

I have a case where matrix multiplication of two matrices with certain dimensions work in numpy, but doesn't work in tensorflow.

x = np.ndarray(shape=(10,20,30), dtype = float)
y = np.ndarray(shape=(30,40), dtype = float)
z = np.matmul(x,y)
print("np shapes: %s x %s = %s" % (np.shape(x), np.shape(y), np.shape(z)))

This works as expected and prints:

np shapes: (10, 20, 30) x (30, 40) = (10, 20, 40)

However in tensorflow when I try to multiply placeholder and variable of the same shapes as the numpy arrays above I get an error

x = tf.placeholder(tf.float32, shape=(10,20,30))
y = tf.Variable(tf.truncated_normal([30,40], name='w'))
print("tf shapes: %s x %s" % (x.get_shape(), y.get_shape()))
tf.matmul(x,y)

Results in

tf shapes: (10, 20, 30) x (30, 40)
InvalidArgumentError: 
Shape must be rank 2 but is rank 3 for 'MatMul_12' 
(op: 'MatMul') with input shapes: [10,20,30], [30,40].

Why does this operation fail?

Answer 1

Don't know why tf.matmul does not support this kind of multiplication (may be one of the core developers could provide a meaningful answer).

But if you just want to be able to multiply tensors in this way, take a look at tf.einsum function. It could operate with tensors of arbitrary rank.