Activations of freezed layers are different between before/after training, why?
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ntinoson el 29 de Jun. de 2018
I follow the example "transfer-learning-using-googlenet" where, the last 3 layers ('loss3-classifier','prob','output') are replaced with 3 new ones. Then I 'freeze' the first 141 layers (that is up to and including 'pool5-drop_7x7_s1'):
layers(1:141) = freezeWeights(layers(1:141));
lgraph = createLgraphUsingConnections(layers,connections);
Then I follow fine-tuning.
Since 'pool5-7x7_s1' is BEFORE 'pool5-drop_7x7_s1', I would expect that the following two vectors were the same:
b_orig= activations(net_orig, I, 'pool5-7x7_s1');
b_tune= activations(net_tune, I, 'pool5-7x7_s1');
but they aren't!... Any idea why?
p.s. I also tried the activation of several other layers BEFORE 'pool5-drop_7x7_s1', and I got different vectors.... 'I' is an image, 'net_orig=googlenet;', and 'net_tune' is the resulting net after tuning.