# sequence_pool¶

Note

This operator only supports LoDTensor as input. It will apply specified pooling operation on the input LoDTensor. 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 LoDTensor 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 LoDTensor:
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 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]

If pool_typ = sum, it will apply pooling on last lod_level [0, 1, 3, 4, 4, 7]. pad_value = 0.0
output is LoDTensor:
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) – LoDTensor 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 Tenosr 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

LoDTensor after pooling with data type float32 or float64.

Return type

Variable

Examples

import paddle