提交 95b896ce 编写于 作者: C chengduoZH

update python interface

上级 259858b4
......@@ -1702,8 +1702,9 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
"""
Applies matrix multipication to two tensors. Currently only rank 1 to rank
3 input tensors are supported.
Applies matrix multiplication to two tensors. Currently, the input
tensors' rank can be any, but when the rank of anyone inputs is
bigger than 3, this two inputs' rank should be equal.
The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
......@@ -1715,17 +1716,17 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
:math:`[1, D]` in transposed form.
- After transpose, the two tensors are 2-D or 3-D and matrix multipication
- After transpose, the two tensors are 2-D or n-D and matrix multiplication
performs in the following way.
- If both are 2-D, they are multiplied like conventional matrices.
- If either is 3-D, it is treated as a stack of matrices residing in the
- If either is n-D, it is treated as a stack of matrices residing in the
last two dimensions and a batched matrix multiply supporting broadcast
applies on the two tensors.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
removed after matrix multipication.
removed after matrix multiplication.
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
......@@ -1742,6 +1743,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
.. code-block:: python
# Examples to clarify shapes of the inputs and output
# x: [B, ..., M, K], y: [B, ..., K, N]
fluid.layers.matmul(x, y) # out: [B, ..., M, N]
# x: [B, M, K], y: [B, K, N]
fluid.layers.matmul(x, y) # out: [B, M, N]
# x: [B, M, K], y: [K, N]
......@@ -1757,9 +1760,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
fluid.layers.matmul(x, y, True, True) # out: [M, N]
"""
helper = LayerHelper('matmul', **locals())
assert max(
len(x.shape), len(y.shape)
) <= 3, 'Currently only rank 1 to rank 3 input tensors are supported.'
assert max(len(x.shape), len(y.shape)) <= 3 or len(x.shape) == len(
y.
shape), 'Inputs\' rank should be equal or their rank should be less 4.'
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='matmul',
......
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