tf.einsum( equation, *inputs, **kwargs )
See the guide: Math > Reduction
A generalized contraction between tensors of arbitrary dimension.
This function returns a tensor whose elements are defined by
equation, which is written in a shorthand form inspired by the Einstein summation convention. As an example, consider multiplying two matrices A and B to form a matrix C. The elements of C are given by:
C[i,k] = sum_j A[i,j] * B[j,k]
In general, the
equation is obtained from the more familiar element-wise equation by 1. removing variable names, brackets, and commas, 2. replacing "*" with ",", 3. dropping summation signs, and 4. moving the output to the right, and replacing "=" with "->".
Many common operations can be expressed in this way. For example:
# Matrix multiplication >>> einsum('ij,jk->ik', m0, m1) # output[i,k] = sum_j m0[i,j] * m1[j, k] # Dot product >>> einsum('i,i->', u, v) # output = sum_i u[i]*v[i] # Outer product >>> einsum('i,j->ij', u, v) # output[i,j] = u[i]*v[j] # Transpose >>> einsum('ij->ji', m) # output[j,i] = m[i,j] # Batch matrix multiplication >>> einsum('aij,ajk->aik', s, t) # out[a,i,k] = sum_j s[a,i,j] * t[a, j, k]
This function behaves like
numpy.einsum, but does not support:
strdescribing the contraction, in the same format as
*inputs: the inputs to contract (each one a
Tensor), whose shapes should be consistent with
name: A name for the operation (optional).
Tensor, with shape determined by
equationdoes not match
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Code samples licensed under the Apache 2.0 License.