用于两个 3dim 矩阵的 numpy einsum 的 Theano 版本
Theano version of a numpy einsum for two 3dim matrices
我有两个 3dim numpy 矩阵,我想根据一个轴做一个点积,而不用在 theano 中使用循环。带有示例数据的 numpy 解决方案如下:
a=[ [[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[ [ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[ [ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0.]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]]]
b=[[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]]]
dt = np.dtype(np.float32)
a=np.asarray(a,dtype=dt)
b=np.asarray(b,dtype=dt)
print(a.shape)
print(b.shape)
其中 "a" 具有 (7, 4, 15) 的形状,"b" 具有 (7, 4, 5) 的形状。 "c",定义为 "a" 和 "b":
的点积
c = np.einsum('ijk,ijl->ilk',a,b)
我正在寻找这个例子的 theano 实现来计算 "c"。
有什么想法吗?
完成本题:
import theano as th
import then.Tensor as T
ta = T.tensor3('a')
tb = T.tensor3('b')
tc = T.batched_tensordot(ta, tb, axes=[[1],[1]])
......
我有两个 3dim numpy 矩阵,我想根据一个轴做一个点积,而不用在 theano 中使用循环。带有示例数据的 numpy 解决方案如下:
a=[ [[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[ [ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[ [ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0.]],
[[ 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0],
[ 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[ 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[ 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0]]]
b=[[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]],
[[ 0, 0, 1, 0, 0.],
[ 1, 0, 0, 0, 0.],
[ 0, 0, 0, 0, 0.],
[ 0, 1, 0, 0, 0.]]]
dt = np.dtype(np.float32)
a=np.asarray(a,dtype=dt)
b=np.asarray(b,dtype=dt)
print(a.shape)
print(b.shape)
其中 "a" 具有 (7, 4, 15) 的形状,"b" 具有 (7, 4, 5) 的形状。 "c",定义为 "a" 和 "b":
的点积c = np.einsum('ijk,ijl->ilk',a,b)
我正在寻找这个例子的 theano 实现来计算 "c"。
有什么想法吗?
完成本题:
import theano as th
import then.Tensor as T
ta = T.tensor3('a')
tb = T.tensor3('b')
tc = T.batched_tensordot(ta, tb, axes=[[1],[1]])
......