Caffe cifar10_quick_train_test
has the output of the pooling layer
OH=(H-FH)/S+1
(32-3)/2 + 1
29/2 + 1
The calculations are as follows:
I don't think the number will be divisible, but what kind of process is being done?
https://github.com/BVLC/caffe/blob/master/examples/cifar10/cifar10_quick_train_test.prototxt
layer{
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param{
lr_mult: 1
}
param{
lr_mult:2
}
convolution_param{
num_output —32
pad:2
kernel_size —5
stride:1
weight_filler{
type: "gaussian"
std —0.0001
}
bias_filler{
type: "constant"
}
}
}
layer{
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param{
pool —MAX
kernel_size:3
stride —2
}
}
If not, the response depends on the deep learning framework (software) and some output errors or round to the nearest integer.
Looking around here, caffe allows you to change the operating mode with parameters.
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