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Layer output shape is 1 x 128 x 19 x 19 if i understand correctly. Looking at the layer's weights' shapes in net->layers () [1]->blobs (): layer 1: type Convolution 'conv1_7x7_128' blob 0: 128 13 7 7 blob 1: 128. Looks like blob 0 has all the weigths: one 7x7 matrix per plane (13) per filter (128). Doing convolutions with blob 0 on 1 x 13 x 19 ...
In caffe, the convolution layer takes one bottom blob, and convolves it with learned filters (which are initialized using the weight type - "Xavier", "MSRA" etc.). However, my question is whether …
This machine's caffe directory is / home / jourluohua / src / caffe, so the compile command is:
I use caffe to train my CNN but loss become nan after a few thousand iterations. I dump the weights before that iterations and I found that some weights in the inner product …
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You will get NaN on the first test phase before training as there are no learned batch norm parameters yet (mean,std etc.) and so caffe will use the std of the test image …
We will use a batch size of 64, and scale the incoming pixels so that they are in the range [0,1). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the …
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However, any layer able to backpropagate may be given a non-zero loss_weight, allowing one to, for example, regularize the activations produced by some intermediate layer(s) of the network …
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