Comments (2)
update myself
- I already know how to prepare my data
according caffe/examples/siamese/convert_mnist_siamese_data.cpp
...
caffe::Datum datum;
datum.set_channels(2); // one channel for each image in the pair
datum.set_height(rows);
datum.set_width(cols);
....
modify the cpp
could make two channel dataset for caffe
for now I use the hinge loss layer instead to the hinge loss function proposed from paper
i tried to make the prototxt
....
layer {
name: "caffe_InnerProduct_9"
type: "InnerProduct"
bottom: "caffe_Flatten_8"
top: "caffe_InnerProduct_9"
inner_product_param {
num_output: 2
axis: -1
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "caffe_InnerProduct_9"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "HingeLoss"
bottom: "caffe_InnerProduct_9"
bottom: "label"
top: "loss"
hinge_loss_param {
norm: L2
}
}
but seems weird? the accuracy only 0.76XXXX
I tried the siamese for the same dataset, i could get accuracy 0.94.
who did i do wrong?
(i made my dataset for two classed, label:0 and label:1)
I still confused....
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@shawnlee103 I've never used caffe for training, can't help here.
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