In the original paper , global pooling is supposed to get rid of fully connected layer. I don’t want to use fully connected layer . Link : https://arxiv.org/pdf/1312.4400.pdf 159 page 4 section 3.2  paragraph 3
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In this paper, we propose another strategy called global average pooling to replace the traditional
fully connected layers in CNN. The idea is to generate one feature map for each corresponding
category of the classification task in the last mlpconv layer. Instead of adding fully connected layers
on top of the feature maps, we take the average of each feature map, and the resulting vector is fed
directly into the softmax layer.  One advantage of global average pooling over the fully connected
layers is that it is more native to the convolution structure by enforcing correspondences between
feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence
maps.  Another advantage is that there is no parameter to optimize in the global average pooling
thus overfitting is avoided at this layer.  Futhermore, global average pooling sums out the spatial
information, thus it is more robust to spatial translations of the input.
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