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|
name: "LeNet"
layers {
layer {
name: "mnist"
type: "data"
source: "../data/mnist-train-leveldb"
batchsize: 64
scale: 0.00390625
}
top: "data"
top: "label"
}
layers {
layer {
name: "conv1"
type: "conv"
num_output: 20
kernelsize: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
blobs_lr: 1.
blobs_lr: 2.
}
bottom: "data"
top: "conv1"
}
layers {
layer {
name: "pool1"
type: "pool"
kernelsize: 2
stride: 2
pool: MAX
}
bottom: "conv1"
top: "pool1"
}
layers {
layer {
name: "conv2"
type: "conv"
num_output: 50
kernelsize: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
blobs_lr: 1.
blobs_lr: 2.
}
bottom: "pool1"
top: "conv2"
}
layers {
layer {
name: "pool2"
type: "pool"
kernelsize: 2
stride: 2
pool: MAX
}
bottom: "conv2"
top: "pool2"
}
layers {
layer {
name: "ip1"
type: "innerproduct"
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
blobs_lr: 1.
blobs_lr: 2.
}
bottom: "pool2"
top: "ip1"
}
layers {
layer {
name: "relu1"
type: "relu"
}
bottom: "ip1"
top: "ip1"
}
layers {
layer {
name: "ip2"
type: "innerproduct"
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
blobs_lr: 1.
blobs_lr: 2.
}
bottom: "ip1"
top: "ip2"
}
layers {
layer {
name: "loss"
type: "softmax_loss"
}
bottom: "ip2"
bottom: "label"
}
|
