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fix jacobian and hessian's docstring #53732

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May 12, 2023
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fix jacobian and hessian's docstring #53732

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d6efd9c
fix jacobian and hessian's docstring
HydrogenSulfate May 11, 2023
b665ae2
fix hessian's docstring
HydrogenSulfate May 11, 2023
0eace85
fix hessian's docstring
HydrogenSulfate May 11, 2023
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17 changes: 8 additions & 9 deletions python/paddle/autograd/autograd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -465,12 +465,12 @@ def jacobian(
The ``xs`` tuples are identical in one-to-one correspondence.

- When ``batch_axis=None``, only 0-dimensional Tensor or 1-dimensional Tensor is
supported, assuming the shape of ``xs`` is ``[N, ]``, the shape of ``ys`` is
``[M, ]``, then the output Jacobian matrix shape is ``[M, N]``.
supported, assuming the shape of ``xs`` is ``[N, ]``, the shape of ``ys`` is
``[M, ]``, then the output Jacobian matrix shape is ``[M, N]``.

- When ``batch_axis=0``, only 1-dimensional Tensor or 2-dimensional Tensor is
supported, assuming the shape of ``xs`` is ``[B, N]``, The shape of ``ys`` is
``[B, M]``, then the output Jacobian matrix shape is ``[B, M, N]``.
supported, assuming the shape of ``xs`` is ``[B, N]``, The shape of ``ys`` is
``[B, M]``, then the output Jacobian matrix shape is ``[B, M, N]``.

After the ``Jacobian`` object is created, the actual calculation process does not
occur, but the lazy evaluation method is used for calculation. It can be
Expand Down Expand Up @@ -553,15 +553,14 @@ def hessian(
``batch_axis`` means The position of the batch dimension of the parameter data.

When the input ``xs`` is a Tensor tuple, the returned result is a ``Hessian`` tuple,
assuming that the internal shape of the ``xs`` tuple is composed of
``([M1, ], [M2, ]) ``, the shape of the returned result consists of
``(([M1, M1], [M1, M2]), ([M2, M1], [M2, M2]))``
assuming that the internal shape of the ``xs`` tuple is composed of ``([M1, ], [M2, ])``, the shape of the returned
result consists of ``(([M1, M1], [M1, M2]), ([M2, M1], [M2, M2]))``

- When ``batch_axis=None``, only 0-dimensional Tensor or 1-dimensional Tensor is
supported, assuming that the shape of ``xs`` is ``[N, ]``, and the shape of ``ys`` is ``[ ]``(0-dimensional Tensor), the final output is a single Hessian matrix whose shape is ``[N, N]``.
supported, assuming that the shape of ``xs`` is ``[N, ]``, and the shape of ``ys`` is ``[ ]`` (0-dimensional Tensor), the final output is a single Hessian matrix whose shape is ``[N, N]``.

- When ``batch_axis=0``, only 1-dimensional Tensor or 2-dimensional Tensor is
supported, assuming that the shape of ``xs`` is ``[B, N]``, and the shape of ``ys`` is `` [B, ]``, the final output Jacobian matrix shape is ``[B, N, N]``.
supported, assuming that the shape of ``xs`` is ``[B, N]``, and the shape of ``ys`` is ``[B, ]``, the final output Jacobian matrix shape is ``[B, N, N]``.

After the ``Hessian`` object is created, the complete calculation process does not
occur, but a partial lazy evaluation method is used for calculation. It can be
Expand Down