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add scripts for installing deps, building caffe and running benchmark
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‎models/intel_optimized_models/alexnet/bdw/train_val_dummydata.prototxt‎

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name: "AlexNet"
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layer {
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name: "data"
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type: "DummyData"
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top: "data"
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top: "label"
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include {
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phase: TRAIN
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}
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dummy_data_param {
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data_filler {
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type: "constant"
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value: 0.01
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}
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shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
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shape: { dim: 256 dim: 1 dim: 1 dim: 1 }
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}
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}
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layer {
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name: "data"
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type: "DummyData"
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top: "data"
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top: "label"
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include {
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phase: TEST
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}
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dummy_data_param {
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data_filler {
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type: "constant"
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value: 0.01
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}
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shape: { dim: 256 dim: 3 dim: 224 dim: 224 }
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shape: { dim: 256 dim: 1 dim: 1 dim: 1 }
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}
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "norm1"
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type: "LRN"
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bottom: "conv1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "pool1"
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type: "Pooling"
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bottom: "norm1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "pool1"
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top: "conv2"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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bottom: "conv2"
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top: "conv2"
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}
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layer {
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name: "norm2"
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type: "LRN"
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bottom: "conv2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "pool2"
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type: "Pooling"
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bottom: "norm2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "pool2"
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top: "conv3"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu3"
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type: "ReLU"
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bottom: "conv3"
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top: "conv3"
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "conv3"
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top: "conv4"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu4"
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type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "conv4"
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top: "conv5"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu5"
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type: "ReLU"
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bottom: "conv5"
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top: "conv5"
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}
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layer {
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name: "pool5"
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type: "Pooling"
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bottom: "conv5"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "fc6"
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type: "InnerProduct"
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bottom: "pool5"
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top: "fc6"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 4096
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weight_filler {
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type: "gaussian"
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std: 0.005
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu6"
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type: "ReLU"
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bottom: "fc6"
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top: "fc6"
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}
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layer {
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name: "drop6"
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type: "Dropout"
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc7"
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type: "InnerProduct"
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bottom: "fc6"
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top: "fc7"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 4096
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weight_filler {
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type: "gaussian"
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std: 0.005
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu7"
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type: "ReLU"
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bottom: "fc7"
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top: "fc7"
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}
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layer {
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name: "drop7"
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type: "Dropout"
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc8"
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type: "InnerProduct"
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bottom: "fc7"
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top: "fc8"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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inner_product_param {
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num_output: 1000
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "loss"
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type: "SoftmaxWithLoss"
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bottom: "fc8"
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bottom: "label"
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top: "loss"
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}
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layer {
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name: "loss3/top-1"
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type: "Accuracy"
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bottom: "fc8"
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bottom: "label"
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top: "loss3/top-1"
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include {
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phase: TEST
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}
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}
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layer {
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name: "loss3/top-5"
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type: "Accuracy"
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bottom: "fc8"
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bottom: "label"
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top: "loss3/top-5"
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include {
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phase: TEST
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}
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accuracy_param {
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top_k: 5
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}
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}

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