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Tensor contraction of a and b along specified axes and outer product.
tf.tensordot(
a, b, axes, name=None
)
Tensordot (also known as tensor contraction) sums the product of elements
from a
and b
over the indices specified by axes
.
This operation corresponds to numpy.tensordot(a, b, axes)
.
Example 1: When a
and b
are matrices (order 2), the case axes=1
is equivalent to matrix multiplication.
Example 2: When a
and b
are matrices (order 2), the case
axes = [[1], [0]]
is equivalent to matrix multiplication.
Example 3: When a
and b
are matrices (order 2), the case axes=0
gives
the outer product, a tensor of order 4.
Example 4: Suppose that \(a_{ijk}\) and \(b_{lmn}\) represent two
tensors of order 3. Then, contract(a, b, [[0], [2]])
is the order 4 tensor
\(c_{jklm}\) whose entry
corresponding to the indices \((j,k,l,m)\) is given by:
\( c_{jklm} = \sum_i a_{ijk} b_{lmi} \).
In general, order(c) = order(a) + order(b)  2*len(axes[0])
.
Returns  

A Tensor with the same type as a .

Raises  

ValueError

If the shapes of a , b , and axes are incompatible.

IndexError

If the values in axes exceed the rank of the corresponding tensor. 