I'm trying to learn caffe by making an xor example.
I'm following this link from the caffe website, but they are doing a CNN.
I'm trying to follow along the tutorial and I am stuck when it comes to compiling the model.
I made a prototxt file describes the model architecture, I am trying to make a two layered xor network. My code is below:
name: "xor_test"
layer {
name: "data"
type: "Data"
transform_param {
scale: 1
}
data_param {
source: "0 0 0
1 0 1
0 1 1
1 1 0"
backend: LMDB
batch_size: 1
}
top: "data"
top: "data"
}
layer {
name: "ip1"
type: "InnerProduct"
param { lr_mult: 1 }
param { lr_mult: 2 }
inner_product_param {
num_output: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
bottom: "data"
top: "ip1"
}
layer {
name: "tanh1"
type: "Tanh"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
param { lr_mult: 1 }
param { lr_mult: 2 }
inner_product_param {
num_output: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
bottom: "ip1"
top: "ip2"
}
layer {
name: "tanh2"
type: "Tanh"
bottom: "ip2"
top: "ip2"
}
I don't know if this model is correct, I can't find other examples to reference.
After this, the tutorial says to create a solver prototxt file which referenced the previously created file.
net: "test.prototxt"
test_iter: 2
test_interval: 5
base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005
lr_policy: "inv"
gamma: 0.0001
power: 0.75
display: 5
# The maximum number of iterations
max_iter: 10
# solver mode: CPU or GPU
solver_mode: CPU
I'm not sure how to train or test the model since my inputs are not images.
