sequence_pool¶

paddle.static.nn. sequence_pool ( input, pool_type, is_test=False, pad_value=0.0 ) [source]

Note

Only receives Tensor as input. If your input is Tensor, please use pool2d Op.(static.nn.** avg_pool2d or max_pool2d ).

This operator only supports Tensor as input. It will apply specified pooling operation on the input Tensor. It pools features of all time-steps of each sequence at the last lod_level using pool_type mentioned in the parameters, such as sum, average, sqrt, etc.

It supports six pool_type:

• average: \(Out[i] = \\frac{\sum_i X_i}{N}\)

• sum: \(Out[i] = \sum_jX_{ij}\)

• sqrt: \(Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}\)

• max: \(Out[i] = max(X_i)\)

• last: \(Out[i] = X_{N_i}\)

• first: \(Out[i]\) = X_0

where \(N_i\) is the length of i-th input sequence.

Case 1:
input is a 1-level Tensor and pad_value = 0.0:
    input.lod = [[0, 2, 5, 7, 7]]
    input.data = [[1.], [3.], [2.], [4.], [6.], [5.], [1.]]
    input.shape = [7, 1]

output is Tensor:
    out.shape = [4, 1]
    with condition out.shape[0] == len(x.lod[-1]) == 4

for different pool_type:
    average: out.data = [[2.], [4.], [3.], [0.0]], where 2.=(1. + 3.)/2, 4.=(2. + 4. + 6.)/3, 3.=(5. + 1.)/2
    sum    : out.data = [[4.], [12.], [6.], [0.0]], where 4.=1. + 3., 12.=2. + 4. + 6., 6.=5. + 1.
    sqrt   : out.data = [[2.82], [6.93], [4.24], [0.0]], where 2.82=(1. + 3.)/sqrt(2), 6.93=(2. + 4. + 6.)/sqrt(3), 4.24=(5. + 1.)/sqrt(2)
    max    : out.data = [[3.], [6.], [5.], [0.0]], where 3.=max(1., 3.), 6.=max(2., 4., 6.), 5.=max(5., 1.)
    last   : out.data = [[3.], [6.], [1.], [0.0]], where 3.=last(1., 3.), 6.=last(2., 4., 6.), 1.=last(5., 1.)
    first  : out.data = [[1.], [2.], [5.], [0.0]], where 1.=first(1., 3.), 2.=first(2., 4., 6.), 5.=first(5., 1.)

    and all above [0.0] at last of out.data is padding data.

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]

If pool_typ = sum, it will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is Tensor:
    out.shape= [5, 1]
    out.lod = [[0, 2, 2, 5]]
    where out.shape[0] == len(x.lod[-1]) == 5
    sum: out.data = [[1.], [5.], [4.], [0.0], [12.]]
    where 1.=1., 5.=3. + 2., 4.=4., 0.0=pad_value, 12.=6. + 5. + 1.

Parameters

• input (variable) – Tensor with lod_level no more than 2. The data type should be float32 or float64.

• pool_type (str) – The pooling type that supports average, sum, sqrt, max, last or first.

• is_test (bool) – Only works when pool_type is max. If set False, a temporary Tensor maxIndex is created to record the index information corresponding to the maximum value, which is used for backward gradient calculation in the training phase. Default: False.

• pad_value (float) – Used to pad the pooling result for empty input sequence. Default: 0.0

Returns

Tensor after pooling with data type float32 or float64.

Return type

Tensor

Examples

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

>>> x = paddle.static.data(name='x', shape=[None, 10], dtype='float32', lod_level=1)
>>> avg_x = paddle.static.nn.sequence_pool(input=x, pool_type='average')
>>> sum_x = paddle.static.nn.sequence_pool(input=x, pool_type='sum')
>>> sqrt_x = paddle.static.nn.sequence_pool(input=x, pool_type='sqrt')
>>> max_x = paddle.static.nn.sequence_pool(input=x, pool_type='max')
>>> last_x = paddle.static.nn.sequence_pool(input=x, pool_type='last')
>>> first_x = paddle.static.nn.sequence_pool(input=x, pool_type='first')