net 网络模型：
name: "LeNet"
layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {

lr_mult: 1

}
param {

lr_mult: 2

}
convolution_param {

num_output: 20
kernel_size: 5
stride: 1
weight_filler {
  type: "xavier"
}
bias_filler {
  type: "constant"
}

}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {

pool: MAX
kernel_size: 2
stride: 2

}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {

lr_mult: 1

}
param {

lr_mult: 2

}
convolution_param {

num_output: 50
kernel_size: 5
stride: 1
weight_filler {
  type: "xavier"
}
bias_filler {
  type: "constant"
}

}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {

pool: MAX
kernel_size: 2
stride: 2

}
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {

lr_mult: 1

}
param {

lr_mult: 2

}
inner_product_param {

num_output: 500
weight_filler {
  type: "xavier"
}
bias_filler {
  type: "constant"
}

}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {

lr_mult: 1

}
param {

lr_mult: 2

}
inner_product_param {

num_output: 10
weight_filler {
  type: "xavier"
}
bias_filler {
  type: "constant"
}

}
}
layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

solver:

The train/test net protocol buffer definition

net: "examples/mnist/lenet_train_test.prototxt"

test_iter specifies how many forward passes the test should carry out.

In the case of MNIST, we have test batch size 100 and 100 test iterations,

covering the full 10,000 testing images.

test_iter: 100

Carry out testing every 500 training iterations.

test_interval: 500

The base learning rate, momentum and the weight decay of the network.

base_lr: 0.01
momentum: 0.9
weight_decay: 0.0005

The learning rate policy

lr_policy: "inv"
gamma: 0.0001
power: 0.75

Display every 100 iterations

display: 100

The maximum number of iterations

max_iter: 10000

snapshot intermediate results

snapshot: 5000
snapshot_prefix: "examples/mnist/lenet"

solver mode: CPU or GPU

solver_mode: CPU