Merge pull request !7723 from lihongkang/v2_mastertags/v1.1.0
| @@ -334,7 +334,7 @@ class AvgPool1d(_PoolNd): | |||||
| Tensor of shape :math:`(N, C_{out}, L_{out})`. | Tensor of shape :math:`(N, C_{out}, L_{out})`. | ||||
| Examples: | Examples: | ||||
| >>> pool = nn.AvgPool1d(kernel_size=6, strides=1) | |||||
| >>> pool = nn.AvgPool1d(kernel_size=6, stride=1) | |||||
| >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32) | >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32) | ||||
| >>> output = pool(x) | >>> output = pool(x) | ||||
| >>> output.shape | >>> output.shape | ||||
| @@ -1376,8 +1376,9 @@ class ArgMinWithValue(PrimitiveWithInfer): | |||||
| - output_x (Tensor) - The minimum value of input tensor, with the same shape as index. | - output_x (Tensor) - The minimum value of input tensor, with the same shape as index. | ||||
| Examples: | Examples: | ||||
| >>> input_x = Tensor(np.random.rand(5)) | |||||
| >>> input_x = Tensor(np.random.rand(5), mindspore.float32) | |||||
| >>> index, output = P.ArgMinWithValue()(input_x) | >>> index, output = P.ArgMinWithValue()(input_x) | ||||
| 0 0.0496291 | |||||
| """ | """ | ||||
| @prim_attr_register | @prim_attr_register | ||||
| @@ -5740,9 +5740,13 @@ class LRN(PrimitiveWithInfer): | |||||
| Tensor, with the same shape and data type as the input tensor. | Tensor, with the same shape and data type as the input tensor. | ||||
| Examples: | Examples: | ||||
| >>> x = Tensor(np.random.rand(1, 10, 4, 4)), mindspore.float32) | |||||
| >>> x = Tensor(np.random.rand(1, 2, 2, 2), mindspore.float32) | |||||
| >>> lrn = P.LRN() | >>> lrn = P.LRN() | ||||
| >>> lrn(x) | >>> lrn(x) | ||||
| [[[[0.18990143 0.59475636] | |||||
| [0.6291904 0.1371534 ]] | |||||
| [[0.6258911 0.4964315 ] | |||||
| [0.3141494 0.43636137]]]] | |||||
| """ | """ | ||||
| @prim_attr_register | @prim_attr_register | ||||
| def __init__(self, depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region="ACROSS_CHANNELS"): | def __init__(self, depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region="ACROSS_CHANNELS"): | ||||