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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag.md new file mode 100644 index 0000000000..6e5458ba6c --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag.md @@ -0,0 +1,42 @@ +### `tf.batch_matrix_diag(diagonal, name=None)` {#batch_matrix_diag} + +Returns a batched diagonal tensor with a given batched diagonal values. + +Given a `diagonal`, this operation returns a tensor with the `diagonal` and +everything else padded with zeros. The diagonal is computed as follows: + +Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a +tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: + +`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`. + +For example: + +```prettyprint +# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] + +and diagonal.shape = (2, 4) + +tf.batch_matrix_diag(diagonal) ==> [[[1, 0, 0, 0] + [0, 2, 0, 0] + [0, 0, 3, 0] + [0, 0, 0, 4]], + [[5, 0, 0, 0] + [0, 6, 0, 0] + [0, 0, 7, 0] + [0, 0, 0, 8]]] + +which has shape (2, 4, 4) +``` + +##### Args: + + +* <b>`diagonal`</b>: A `Tensor`. Rank `k`, where `k >= 1`. +* <b>`name`</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `diagonal`. + Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`. + |