<p>这里有一个通用的解决方案,可以使用一些<code>reshaping</code>和<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html" rel="nofollow">^{<cd2>}</a>来覆盖所有类型的事例/任意形状。<code>einsum</code>在这里有帮助,因为我们需要沿着输入数组的第一个轴对齐,并沿着最后一个轴进行缩减。实现应该是这样的-</p>
<pre><code>def dotprod_axis0(A,B):
N,nA,nB = A.shape[0], A.shape[-1], B.shape[1]
Ar = A.reshape(N,-1,nA)
Br = B.reshape(N,nB,-1)
return np.squeeze(np.einsum('ijk,ikl->ijl',Ar,Br))
</code></pre>
<h2>案例</h2>
<p><strong>I.A:2D,B:2D</strong></p>
^{pr2}$
<p><strong>二。A:3D,B:3D</strong></p>
<pre><code>In [122]: # Inputs
...: A = np.random.randint(0,9,(2,3,4))
...: B = np.random.randint(0,9,(2,4,5))
...:
In [123]: for i in range(A.shape[0]):
...: print np.dot(A[i], B[i])
...:
[[ 74 70 53 118 43]
[ 47 43 29 95 30]
[ 41 37 26 23 15]]
[[ 50 86 33 35 82]
[ 78 126 40 124 140]
[ 67 88 35 47 83]]
In [124]: dotprod_axis0(A,B)
Out[124]:
array([[[ 74, 70, 53, 118, 43],
[ 47, 43, 29, 95, 30],
[ 41, 37, 26, 23, 15]],
[[ 50, 86, 33, 35, 82],
[ 78, 126, 40, 124, 140],
[ 67, 88, 35, 47, 83]]])
</code></pre>
<p><strong>III.A:3D,B:2D</strong></p>
^{4}$
<p><strong>IV.A:2D,B:3D</strong></p>
<pre><code>In [128]: # Inputs
...: A = np.random.randint(0,9,(2,4))
...: B = np.random.randint(0,9,(2,4,5))
...:
In [129]: for i in range(A.shape[0]):
...: print np.dot(A[i], B[i])
...:
[76 93 31 75 16]
[ 33 98 49 117 111]
In [130]: dotprod_axis0(A,B)
Out[130]:
array([[ 76, 93, 31, 75, 16],
[ 33, 98, 49, 117, 111]])
</code></pre>