Yes that can be done. Its explained in the paper ‘Striving for simplicity: The all convolutional net’ https://arxiv.org/pdf/1412.6806.pdf. Quote from the paper: ‘We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks’
Td;lr GlobalMaxPooling1D for temporal data takes the max vector over the steps dimension. So a tensor with shape [10, 4, 10] becomes a tensor with shape [10, 10] after global pooling. MaxPooling1D takes the max over the steps too but constrained to a pool_size for each stride. So a [10, 4, 10] tensor with pooling_size=2 … Read more
You could use scikit-image block_reduce: import numpy as np import skimage.measure a = np.array([ [ 20, 200, -5, 23], [ -13, 134, 119, 100], [ 120, 32, 49, 25], [-120, 12, 9, 23] ]) skimage.measure.block_reduce(a, (2,2), np.max) Gives: array([[200, 119], [120, 49]])