tf.tensordot | TensorFlow v2.16.1 (original) (raw)
Tensor contraction of a and b along specified axes and outer product.
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Main aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.linalg.tensordot, tf.compat.v1.tensordot
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=1is equivalent to matrix multiplication.
Example 2: When a and b are matrices (order 2), the caseaxes = [[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]).
| Args | |
|---|---|
| a | Tensor of type float32 or float64. |
| b | Tensor with the same type as a. |
| axes | Either a scalar N, or a list or an int32 Tensor of shape [2, k]. If axes is a scalar, sum over the last N axes of a and the first N axes of b in order. If axes is a list or Tensor the first and second row contain the set of unique integers specifying axes along which the contraction is computed, for a and b, respectively. The number of axes for a andb must be equal. If axes=0, computes the outer product between a andb. |
| name | A name for the operation (optional). |
| 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. |