sequence_first_step¶

api_attr

declarative programming (static graph)

paddle.fluid.layers.sequence_first_step(input)[source]

This operator only supports LoDTensor as input. Given the input LoDTensor, it will select first time-step feature of each sequence as output.

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

 output is a LoDTensor:
     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 LoDTensor 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 LoDTensor:
     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 (Variable) – LoDTensor with lod_level no more than 2. The data type should be float32.

Returns

LoDTensor consist of the sequence’s first step vector. The data type is float32.

Return type

Variable

Examples

import paddle.fluid as fluid
x = fluid.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)