Tensor contraction of a and b along specified axes.

# Aliases:

• tf.compat.v1.linalg.tensordot
• tf.compat.v1.tensordot
• tf.compat.v2.linalg.tensordot
• tf.compat.v2.tensordot
• tf.linalg.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 aaxes and baxes. The lists aaxes and baxes specify those pa``irs of axes along which to contract the tensors. The axis aaxes[i] of a must have the same dimension as axis baxes[i] of b for all i in range(0, len(aaxes)). The lists aaxes and b_axes must have identical length and consist of unique integers that specify valid axes for each of the tensors. 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: Suppose that and represent two tensors of order 3. Then, contract(a, b, [[0], [2]]) is the order 4 tensor whose entry corresponding to the indices is given by: . 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 and b must be equal.
• 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.