Keras:平均pooling1d层和全球平均pooling1d层之间的差异

谈到角膜的平均聚集层,我有点困惑。 The documentation 声明如下:

AveragePooling1d:时间数据的平均池。

争论

pool_size: Integer, size of the average pooling windows.
strides: Integer, or None. Factor by which to downscale. E.g. 2 will halve the input. If None, it will default to pool_size.
padding: One of "valid" or "same" (case-insensitive).
data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.

通道最后对应于具有形状的输入(批处理、步骤, 特征)而通道_首先对应于具有形状的输入 (批次、特征、步骤)。

输入形状

If data_format='channels_last': 3D tensor with shape: (batch_size, steps, features)
If data_format='channels_first': 3D tensor with shape: (batch_size, features, steps)

输出形状

If data_format='channels_last': 3D tensor with shape: (batch_size, downsampled_steps, features)
If data_format='channels_first': 3D tensor with shape: (batch_size, features, downsampled_steps)

和

globalaveragepooling1d:时间数据的全局平均池操作。

争论

data_format: A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs.

通道最后对应于具有形状的输入(批处理、步骤, 特征)而通道_首先对应于具有形状的输入 (批次、特征、步骤)。

输入形状

if data_format='channels_last':带形状的三维张量:(批量大小、步骤、功能)
if data_format='channels_first':带形状的三维张量:(批量尺寸、特征、步骤)

输出形状

带形状的二维张量:(批量大小,特征)

我(认为我)确实得到了平均池的概念,但我不太明白为什么GlobalAveragePooling1d层只是简单地删除了steps参数。非常感谢你的回答。