Keras：AveragePooling1D 层和 GlobalAveragePooling1D 层的区别

Question

I'm a bit confused when it comes to the average pooling layers of Keras. The documentation states the following:

AveragePooling1D: Average pooling for temporal data.

Arguments

pool_size: Integer, size of the average pooling windows. strides: Integer, or None. Factor by which to downscale. Eg 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.

channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).

Input shape

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)

Output shape

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)

and

GlobalAveragePooling1D: Global average pooling operation for temporal data.

Arguments

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

channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).

Input shape

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)

Output shape

2D tensor with shape: (batch_size, features)

I (think that I) do get the concept of average pooling but I don't really understand why the GlobalAveragePooling1D layer simply drops the steps parameter. Thank you very much for your answers.

Answer 1

GlobalAveragePooling1D is same as AveragePooling1D with pool_size=steps . So, for each feature dimension, it takes average among all time steps. The output thus have shape (batch_size, 1, features) (if data_format='channels_last' ). They just flatten the second (or third if data_format='channels_first' ) dimension, that is how you get output shape equal to (batch_size, features) .