| name: "conv" |
| train_steps: 10000 |
| test_steps:100 |
| test_freq:500 |
| disp_freq:50 |
| train_one_batch { |
| alg: kBP |
| } |
| updater { |
| momentum:0.9 |
| weight_decay:0.0005 |
| type: kSGD |
| learning_rate { |
| type : kInverse |
| base_lr:0.01 |
| inverse_conf { |
| gamma:0.0001 |
| pow:0.75 |
| } |
| } |
| } |
| neuralnet { |
| layer { |
| name: "data" |
| type: kRecordInput |
| store_conf { |
| backend: "kvfile" |
| path: "examples/mnist/train_data.bin" |
| batchsize: 64 |
| std_value: 255 |
| random_skip: 5000 |
| shape: 1 |
| shape: 28 |
| shape: 28 |
| } |
| include: kTrain |
| } |
| |
| layer { |
| name: "data" |
| type: kRecordInput |
| store_conf { |
| backend: "kvfile" |
| path: "examples/mnist/test_data.bin" |
| std_value: 255 |
| batchsize: 100 |
| shape: 1 |
| shape: 28 |
| shape: 28 |
| } |
| include: kTest |
| } |
| |
| layer { |
| name: "conv1" |
| type: kCConvolution |
| srclayers: "data" |
| convolution_conf { |
| num_filters: 20 |
| kernel: 5 |
| stride: 1 |
| } |
| param{ |
| name: "w1" |
| init { |
| type : kUniformSqrtFanIn |
| } |
| } |
| param{ |
| name: "b1" |
| init { |
| type : kConstant |
| value:0 |
| } |
| lr_scale:2.0 |
| } |
| } |
| layer { |
| name: "pool1" |
| type: kCPooling |
| srclayers: "conv1" |
| pooling_conf { |
| pool: MAX |
| kernel: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "conv2" |
| type: kCConvolution |
| srclayers: "pool1" |
| convolution_conf { |
| num_filters: 50 |
| kernel: 5 |
| stride: 1 |
| } |
| param{ |
| name: "w2" |
| init { |
| type :kUniformSqrtFanIn |
| } |
| } |
| param{ |
| name: "b2" |
| init { |
| type : kConstant |
| value:0 |
| } |
| lr_scale:2.0 |
| } |
| } |
| layer { |
| name: "pool2" |
| type: kCPooling |
| srclayers: "conv2" |
| pooling_conf { |
| pool: MAX |
| kernel: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "ip1" |
| type: kInnerProduct |
| srclayers:"pool2" |
| innerproduct_conf { |
| num_output: 500 |
| } |
| param{ |
| name: "w3" |
| init { |
| type :kUniformSqrtFanIn |
| } |
| } |
| param{ |
| name: "b3" |
| init { |
| type : kConstant |
| value:0 |
| } |
| lr_scale:2.0 |
| } |
| } |
| |
| layer { |
| name: "relu1" |
| type: kReLU |
| srclayers:"ip1" |
| } |
| |
| layer { |
| name: "ip2" |
| type: kInnerProduct |
| srclayers:"relu1" |
| innerproduct_conf { |
| num_output: 10 |
| } |
| param { |
| name: "w4" |
| init { |
| type :kUniformSqrtFanIn |
| } |
| } |
| param { |
| name: "b4" |
| init { |
| type : kConstant |
| value:0 |
| } |
| lr_scale:2 |
| } |
| } |
| layer{ |
| name: "loss" |
| type: kSoftmaxLoss |
| softmaxloss_conf{ |
| topk:1 |
| } |
| srclayers:"ip2" |
| srclayers:"data" |
| } |
| } |
| cluster { |
| nworker_groups: 1 |
| nserver_groups: 1 |
| workspace: "examples/mnist" |
| } |