PaddlePaddle
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paddle.fluid / layers / sequence_conv
sequence_conv¶
-
paddle.fluid.layers.
sequence_conv
( input, num_filters, filter_size=3, filter_stride=1, padding=True, padding_start=None, bias_attr=None, param_attr=None, act=None, name=None ) [源代码] ¶
注意:该OP的输入只能是LoDTensor,如果您需要处理的输入是Tensor类型,请使用conv2d函数(fluid.layers. conv2d )。
该OP在给定的卷积参数下(如卷积核数目、卷积核大小等),对输入的变长序列(sequence)LoDTensor进行卷积操作。默认情况下,该OP会自适应地在每个输入序列的两端等长地填充全0数据,以确保卷积后的序列输出长度和输入长度一致。支持通过配置 padding_start
参数来指定序列填充的行为。
提示: 参数 padding
为无用参数,将在未来的版本中被移除。
这里详细介绍数据填充操作的细节:
对于一个min-batch为2的变长序列输入,分别包含3个、1个时间步(time_step),
假设输入input是一个[4, N]的float类型LoDTensor,为了方便,这里假设N = 2
input.data = [[1, 1],
[2, 2],
[3, 3],
[4, 4]]
input.lod = [[0, 3, 4]]
即输入input总共有4个词,每个词被表示为一个2维向量。
Case1:
若 padding_start = -1,filter_size = 3,
则两端填充数据的长度分别为:
up_pad_len = max(0, -padding_start) = 1
down_pad_len = max(0, filter_size + padding_start - 1) = 1
则以此填充后的输入数据为:
data_aftet_padding = [[0, 0, 1, 1, 2, 2],
[1, 1, 2, 2, 3, 3],
[2, 2, 3, 3, 0, 0],
[0, 0, 4, 4, 0, 0]]
它将和卷积核矩阵相乘得到最终的输出,假设num_filters = 3:
output.data = [[ 0.3234, -0.2334, 0.7433],
[ 0.5646, 0.9464, -0.1223],
[-0.1343, 0.5653, 0.4555],
[ 0.9954, -0.1234, -0.1234]]
output.shape = [4, 3] # 3 = num_filters
output.lod = [[0, 3, 4]] # 保持不变
- 参数:
-
input (Variable) - 维度为 \((M, K)\) 的二维LoDTensor,仅支持lod_level为1。其中M是mini-batch的总时间步数,K是输入的
hidden_size
特征维度。数据类型为float32或float64。num_filters (int) - 滤波器的数量。
filter_size (int) - 滤波器的高度(H);不支持指定滤波器宽度(W),宽度固定取值为输入的
hidden_size
。默认值为3。filter_stride (int) - 滤波器每次移动的步长。目前只支持取值为1,默认为1。
padding (bool) - 此参数不起任何作用,将在未来的版本中被移除。 无论
padding
取值为False或者True,默认地,该函数会自适应地在每个输入序列的两端等长地填充全0数据,以确保卷积后的输出序列长度和输入长度一致。默认填充是考虑到输入的序列长度可能会小于卷积核大小,这会导致无正确计算卷积输出。填充为0的数据在训练过程中不会被更新。默认为True。padding_start (int) - 表示对输入序列填充时的起始位置,可以为负值。负值表示在每个序列的首端填充
|padding_start|
个时间步(time_step)的全0数据;正值表示对每个序列跳过前padding_start
个时间步的数据。同时在末端填充 \(filter\_size + padding\_start - 1\) 个时间步的全0数据,以保证卷积输出序列长度和输入长度一致。如果padding_start
为None,则在每个序列的两端填充 \(\frac{filter\_size}{2}\) 个时间步的全0数据;如果padding_start
设置为0,则只在序列的末端填充 \(filter\_size - 1\) 个时间步的全0数据。默认为None。bias_attr (ParamAttr) - 指定偏置参数属性的对象。默认值为None,表示使用默认的偏置参数属性。具体用法请参见 ParamAttr 。
param_attr (ParamAttr) - 指定权重参数属性的对象。默认值为None,表示使用默认的权重参数属性。具体用法请参见 ParamAttr 。
act (str) – 应用于输出上的激活函数,如tanh、softmax、sigmoid,relu等,支持列表请参考 激活函数 ,默认值为None。
name (str,可选) – 具体用法请参见 Name ,一般无需设置,默认值为None。
返回:和输入序列等长的LoDTensor,数据类型和输入一致,为float32或float64。
返回类型:Variable
代码示例
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[10,10], append_batch_size=False, dtype='float32')
x_conved = fluid.layers.sequence_conv(x,2)
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