!8310 fix example of categorical and rnntloss

From: @yanzhenxiang2020
Reviewed-by: @c_34
Signed-off-by: @c_34
pull/8310/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
commit d6f6269ff1

@ -4154,8 +4154,8 @@ class Meshgrid(PrimitiveWithInfer):
Args:
indexing (str): Either 'xy' or 'ij'. Default: 'xy'.
When the indexing argument is set to 'xy' (the default),
the broadcasting instructions for the first two dimensions are swapped.
When the indexing argument is set to 'xy' (the default), the broadcasting
instructions for the first two dimensions are swapped.
Inputs:
- **input_x** (Union[tuple, list]) - A Tuple or list of N 1-D Tensor objects.
@ -4170,7 +4170,8 @@ class Meshgrid(PrimitiveWithInfer):
>>> z = np.array([8, 9, 0, 1, 2]).astype(np.int32)
>>> inputs = (x, y, z)
>>> meshgrid = ops.Meshgrid(indexing="xy")
>>> meshgrid(inputs)
>>> output = meshgrid(inputs)
>>> print(output)
(Tensor(shape=[3, 4, 6], dtype=UInt32, value=
[[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],

@ -2261,8 +2261,17 @@ class RNNTLoss(PrimitiveWithInfer):
>>> labels = np.array([[1, 2]]).astype(np.int32)
>>> input_length = np.array([T] * B).astype(np.int32)
>>> label_length = np.array([len(l) for l in labels]).astype(np.int32)
>>> rnnt_loss = ops.RNNTLoss(blank_label=blank)
>>> rnnt_loss = ops.RNNTLoss(blank_label=0)
>>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
>>> print(costs)
[-3.5036912]
>>> print(grads)
[[[[-0.35275543 -0.64724463 0. 0. 0. ]
[-0.19174816 0. -0.45549652 0. 0. ]
[-0.45549664 0. 0. 0. 0. ]]
[[0. -0.35275543 0. 0. 0. ]
[0. 0. -0.5445037 0. 0. ]
[-1.00000002 0. 0. 0. 0. ]]]]
"""
@prim_attr_register

@ -52,7 +52,7 @@ def test_net_assert():
out_expect0 = np.array([0, 0, 0, 1, 1, 0]).reshape(3, 2)
out_expect1 = np.array([0, 1, 1])
out_expect2 = np.array([2, 2])
out_expect3 = np.array([-0.7443749, 0.18251707]).reshape(2, 1)
out_expect3 = np.array([-0.7443749, 0.18251707]).astype(np.float32).reshape(2, 1)
assert np.array_equal(output[0].asnumpy(), out_expect0)
assert np.array_equal(output[1].asnumpy(), out_expect1)
assert np.array_equal(output[2].asnumpy(), out_expect2)

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