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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: "prob"
+ type: "softmax_loss"
+ }
+ bottom: "ip2"
+ bottom: "label"
+}