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Tensor contraction over specified indices and outer product.
See Migration guide for more details.
tf.einsum( equation, *inputs, **kwargs )
Einsum allows defining Tensors by defining their element-wise computation. This computation is defined by
equation, a shorthand form based on Einstein summation. 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, to convert the element-wise equation into the
equation string, use the following procedure (intermediate strings for matrix multiplication example provided in parentheses):
ik = sum_j ij * jk)
ik = sum_j ij , jk)
ik = ij, jk)
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] # Trace einsum('ii', m) # output[j,i] = trace(m) = sum_i m[i, i] # Batch matrix multiplication einsum('aij,ajk->aik', s, t) # out[a,i,k] = sum_j s[a,i,j] * t[a, j, k]
To enable and control broadcasting, use an ellipsis. For example, to perform batch matrix multiplication with NumPy-style broadcasting across the batch dimensions, use:
einsum('...ij,...jk->...ik', u, v)
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.