forked from mindspore-Ecosystem/mindspore
fix example of categorical and rnntloss
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@ -4151,8 +4151,8 @@ class Meshgrid(PrimitiveWithInfer):
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Args:
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indexing (str): Either 'xy' or 'ij'. Default: 'xy'.
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When the indexing argument is set to 'xy' (the default),
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the broadcasting instructions for the first two dimensions are swapped.
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When the indexing argument is set to 'xy' (the default), the broadcasting
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instructions for the first two dimensions are swapped.
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Inputs:
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- **input_x** (Union[tuple, list]) - A Tuple or list of N 1-D Tensor objects.
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@ -4167,7 +4167,8 @@ class Meshgrid(PrimitiveWithInfer):
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>>> z = np.array([8, 9, 0, 1, 2]).astype(np.int32)
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>>> inputs = (x, y, z)
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>>> meshgrid = ops.Meshgrid(indexing="xy")
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>>> meshgrid(inputs)
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>>> output = meshgrid(inputs)
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>>> print(output)
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(Tensor(shape=[3, 4, 6], dtype=UInt32, value=
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[[[1, 1, 1, 1, 1],
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[2, 2, 2, 2, 2],
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@ -2261,8 +2261,17 @@ class RNNTLoss(PrimitiveWithInfer):
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>>> labels = np.array([[1, 2]]).astype(np.int32)
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>>> input_length = np.array([T] * B).astype(np.int32)
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>>> label_length = np.array([len(l) for l in labels]).astype(np.int32)
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>>> rnnt_loss = ops.RNNTLoss(blank_label=blank)
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>>> rnnt_loss = ops.RNNTLoss(blank_label=0)
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>>> costs, grads = rnnt_loss(Tensor(acts), Tensor(labels), Tensor(input_length), Tensor(label_length))
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>>> print(costs)
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[-3.5036912]
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>>> print(grads)
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[[[[-0.35275543 -0.64724463 0. 0. 0. ]
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[-0.19174816 0. -0.45549652 0. 0. ]
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[-0.45549664 0. 0. 0. 0. ]]
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[[0. -0.35275543 0. 0. 0. ]
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[0. 0. -0.5445037 0. 0. ]
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[-1.00000002 0. 0. 0. 0. ]]]]
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"""
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@prim_attr_register
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@ -52,7 +52,7 @@ def test_net_assert():
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out_expect0 = np.array([0, 0, 0, 1, 1, 0]).reshape(3, 2)
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out_expect1 = np.array([0, 1, 1])
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out_expect2 = np.array([2, 2])
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out_expect3 = np.array([-0.7443749, 0.18251707]).reshape(2, 1)
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out_expect3 = np.array([-0.7443749, 0.18251707]).astype(np.float32).reshape(2, 1)
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assert np.array_equal(output[0].asnumpy(), out_expect0)
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assert np.array_equal(output[1].asnumpy(), out_expect1)
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assert np.array_equal(output[2].asnumpy(), out_expect2)
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