sequence_last_step¶

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

Only supports Tensor as input. Given the input Tensor, it will select last 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 = [[3.], [6.], [1.]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(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.], [2.], [4.], [0.0], [1.]]
    where 1.=last(1.), 2.=last(3., 2.), 4.=last(4.), 0.0 = pad_value, 1=last(6., 5., 1.)

Parameters

input (Tensor) – Tensor with lod_level no more than 2. The data type should be float32.

Returns

Tensor consist of the sequence’s last step vector. The data type is float32.

Return type

Tensor

Examples

>>> import paddle
>>> paddle.enable_static()

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