numpy - Python: Using Tensordot for tensor x matrix multiplication -

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hi im tying multiply tensor matrix in following fashion:

dimensions

w: a x b x c

v: a x c

i want z such that

z[i]=dot(w[i],v[i])

z of dimension a x ( (b x c) . (c x 1)), (a x b)

ive tried numpy.tensordot havent been able to. can want? if not how can without loops.

basically equivalent of

def f(w,v):         z=[]         in range(len(w)):             z.append(dot(w[i],v[i]))         return z

thanks

edit: achievable tensordot?

np.einsum("abc,ac -> ab", w, v):

import numpy np  def z_loop(w,v): # define check `einsum()` gives necessary result     z = np.empty(w.shape[:-1], dtype=w.dtype)     in range(z.shape[0]):         z[i,:] = np.dot(w[i,:], v[i,:])     return z  w = np.random.uniform(size=(3,4,5)) v = np.random.uniform(size=w.shape[::2]) assert np.allclose(z_loop(w, v), np.einsum('abc,ac -> ab', w, v))

there might simpler variants (via dot(), .reshape()) einsum() obvious task description.

def z_dot(w, v):     z = np.dot(w, v[:,...,np.newaxis])     z = z.reshape(z.shape[:-1])     return np.diagonal(z, axis2=-1).t  assert np.allclose(z_dot(w, v), np.einsum('abc,ac -> ab', w, v))

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