sequence_first_step

paddle.static.nn. sequence_first_step ( input ) [source]

Warning

API “paddle.static.nn.sequence_lod.sequence_first_step” is deprecated since 3.0.0, and will be removed in future versions. Reason: This API will be deprecated in the future, because it’s just for old statics mode.

Only supports Tensor as input. Given the input Tensor, it will select first time-step feature of each sequence as output.

           Case 1:
 input is 1-level Tensor:
     input.lod = [[0, 2, 5, 7]]
     input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
     input.shape = [7, 1]

 output is a Tensor:
     out.shape = [3, 1]
     out.shape[0] == len(x.lod[-1]) == 3
     out.data = [[1.], [2.], [5.]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)

 Case 2:
 input is a 2-level Tensor containing 3 sequences with length info [2, 0, 3],
 where 0 means empty sequence.
 The first sequence contains 2 subsequence with length info [1, 2];
 The last sequence contains 3 subsequence with length info [1, 0, 3].
     input.lod = [[0, 2, 2, 5], [0, 1, 3, 4, 4, 7]]
     input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
     input.shape = [7, 1]

 It will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
 output is a Tensor:
     out.shape= [5, 1]
     out.lod = [[0, 2, 2, 5]]
     out.shape[0] == len(x.lod[-1]) == 5
     out.data = [[1.], [3.], [4.], [0.0], [6.]]
     where 1.=first(1.), 3.=first(3., 2.), 4.=first(4.), 0.0 = pad_value, 6.=first(6., 5., 1.)

          

Parameters: input (Tensor) – Tensor with lod_level no more than 2. The data type should be float32 or float64.
Returns: Tensor consist of the sequence’s first step vector. The data type is float32 or float64.
Return type: Tensor

Examples

           >>> 
>>> # set env var before import paddle to disable pir mode, following example code use os module.
>>> import os
>>> os.environ['FLAGS_enable_pir_api'] = '0'
>>> import paddle
>>> paddle.enable_static()

>>> x = paddle.static.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
>>> x_first_step = paddle.static.nn.sequence_first_step(input=x)

          