transpose
paddle.fluid.layers.
transpose
(x, perm, name=None)[源代码]
该OP根据perm对输入的多维Tensor进行数据重排。返回多维Tensor的第i维对应输入Tensor的perm[i]维。
- 参数:
- x (Variable) - 输入:x:[N_1, N_2, …, N_k, D]多维Tensor,可选的数据类型为float16, float32, float64, int32, int64。
- perm (list) - perm长度必须和X的维度相同,并依照perm中数据进行重排。
- name (str) - 该层名称(可选)。
返回: 多维Tensor
返回类型:Variable
示例:
- x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]]
- [[13 14 15 16] [17 18 19 20] [21 22 23 24]]]
- shape(x) = [2,3,4]
- # 例0
- perm0 = [1,0,2]
- y_perm0 = [[[ 1 2 3 4] [13 14 15 16]]
- [[ 5 6 7 8] [17 18 19 20]]
- [[ 9 10 11 12] [21 22 23 24]]]
- shape(y_perm0) = [3,2,4]
- # 例1
- perm1 = [2,1,0]
- y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]]
- [[ 2 14] [ 6 18] [10 22]]
- [[ 3 15] [ 7 19] [11 23]]
- [[ 4 16] [ 8 20] [12 24]]]
- shape(y_perm1) = [4,3,2]
代码示例:
- # 请使用 append_batch_size=False 来避免
- # 在数据张量中添加多余的batch大小维度
- import paddle.fluid as fluid
- x = fluid.layers.data(name='x', shape=[2, 3, 4],
- dtype='float32', append_batch_size=False)
- x_transposed = fluid.layers.transpose(x, perm=[1, 0, 2])
- print(x_transposed.shape)
- #(3L, 2L, 4L)