name: "VGG_ILSVRC_19_layer"layer {name: "data"type: "ImageData"top: "data"top: "label"include {phase: TRAIN}image_data_param {batch_size: 12source: "../../fine_tuning_data/HAT_fineTuning_data/train_data_fineTuning.txt"root_folder: "../../fine_tuning_data/HAT_fineTuning_data/train_data/"}}layer {name: "data"type: "ImageData"top: "data"top: "label"include {phase: TEST}transform_param {mirror: false}image_data_param {batch_size: 10source: "../../fine_tuning_data/HAT_fineTuning_data/test_data_fineTuning.txt"root_folder: "../../fine_tuning_data/HAT_fineTuning_data/test_data/"}}layer {bottom:"data" top:"conv1_1" name:"conv1_1" type:"Convolution" convolution_param {num_output:64 pad:1kernel_size:3 }}layer {bottom:"conv1_1" top:"conv1_1" name:"relu1_1" type:"ReLU" }layer {bottom:"conv1_1" top:"conv1_2" name:"conv1_2" type:"Convolution" convolution_param {num_output:64 pad:1kernel_size:3}}layer {bottom:"conv1_2" top:"conv1_2" name:"relu1_2" type:"ReLU" }layer {bottom:"conv1_2" top:"pool1" name:"pool1" type:"Pooling" pooling_param {pool:MAX kernel_size:2stride:2 }}layer {bottom:"pool1" top:"conv2_1" name:"conv2_1" type:"Convolution" convolution_param {num_output:128pad:1kernel_size:3}}layer {bottom:"conv2_1" top:"conv2_1" name:"relu2_1" type:"ReLU" }layer {bottom:"conv2_1" top:"conv2_2" name:"conv2_2" type:"Convolution" convolution_param {num_output:128 pad:1kernel_size:3}}layer {bottom:"conv2_2" top:"conv2_2" name:"relu2_2" type:"ReLU" }layer {bottom:"conv2_2" top:"pool2" name:"pool2" type:"Pooling" pooling_param {pool:MAXkernel_size:2 stride:2 }}layer {bottom:"pool2" top:"conv3_1" name: "conv3_1"type:"Convolution" convolution_param {num_output:256 pad:1kernel_size:3}}layer {bottom:"conv3_1" top:"conv3_1" name:"relu3_1" type:"ReLU" }layer {bottom:"conv3_1" top:"conv3_2" name:"conv3_2" type:"Convolution" convolution_param {num_output:256pad:1kernel_size:3}}layer {bottom:"conv3_2" top:"conv3_2" name:"relu3_2" type:"ReLU" }layer {bottom:"conv3_2" top:"conv3_3" name:"conv3_3" type:"Convolution" convolution_param {num_output:256 pad:1 kernel_size:3}}layer {bottom:"conv3_3" top:"conv3_3"name:"relu3_3" type:"ReLU" }layer {bottom:"conv3_3" top:"conv3_4" name:"conv3_4" type:"Convolution" convolution_param {num_output:256pad:1kernel_size:3}}layer {bottom:"conv3_4" top:"conv3_4" name:"relu3_4" type:"ReLU" }layer {bottom:"conv3_4" top:"pool3" name:"pool3" type:"Pooling" pooling_param {pool:MAX kernel_size: 2stride: 2}}layer {bottom:"pool3" top:"conv4_1" name:"conv4_1" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv4_1" top:"conv4_1" name:"relu4_1" type:"ReLU" }layer {bottom:"conv4_1" top:"conv4_2" name:"conv4_2" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv4_2" top:"conv4_2" name:"relu4_2" type:"ReLU" }layer {bottom:"conv4_2" top:"conv4_3" name:"conv4_3" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv4_3" top:"conv4_3" name:"relu4_3" type:"ReLU" }layer {bottom:"conv4_3" top:"conv4_4" name:"conv4_4" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv4_4" top:"conv4_4" name:"relu4_4" type:"ReLU" }layer {bottom:"conv4_4" top:"pool4" name:"pool4" type:"Pooling" pooling_param {pool:MAXkernel_size: 2stride: 2}}layer {bottom:"pool4" top:"conv5_1" name:"conv5_1" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv5_1" top:"conv5_1" name:"relu5_1" type:"ReLU" }layer {bottom:"conv5_1" top:"conv5_2" name:"conv5_2" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv5_2" top:"conv5_2" name:"relu5_2" type:"ReLU" }layer {bottom:"conv5_2" top:"conv5_3" name:"conv5_3" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv5_3" top:"conv5_3" name:"relu5_3" type:"ReLU" }layer {bottom:"conv5_3" top:"conv5_4" name:"conv5_4" type:"Convolution" convolution_param {num_output: 512pad: 1kernel_size: 3}}layer {bottom:"conv5_4" top:"conv5_4" name:"relu5_4" type:"ReLU" }layer {bottom:"conv5_4" top:"pool5" name:"pool5" type:"Pooling" pooling_param {pool:MAX kernel_size: 2stride: 2}}layer {bottom:"pool5" top:"fc6_" name:"fc6_" type:"InnerProduct" inner_product_param {num_output: 4096}}layer {bottom:"fc6_" top:"fc6_" name:"relu6" type:"ReLU" }layer {bottom:"fc6_" top:"fc6_" name:"drop6" type:"Dropout" dropout_param {dropout_ratio: 0.5}}layer {bottom:"fc6_" top:"fc7" name:"fc7" type:"InnerProduct" inner_product_param {num_output: 4096}}layer {bottom:"fc7" top:"fc7" name:"relu7" type:"ReLU" }layer {bottom:"fc7" top:"fc7" name:"drop7" type:"Dropout" dropout_param {dropout_ratio: 0.5}}layer {bottom:"fc7" top:"fc8_" name:"fc8_" type:"InnerProduct" inner_product_param {num_output: 43}}layer {name: "sigmoid"type: "Sigmoid"bottom: "fc8_"top: "fc8_"}layer {name: "accuracy"type: "Accuracy"bottom: "fc8_"bottom: "label"top: "accuracy"include {phase: TEST}}layer {name: "loss"type: "EuclideanLoss"bottom: "fc8_"bottom: "label"top: "loss"}

name: "VGG_ILSVRC_16_layer"layers {name: "data"type: IMAGE_DATAtop: "data"top: "label"include {phase: TRAIN}image_data_param {batch_size: 80source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Labeled_Train_0.5_.txt"root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/train_image_sun_256_256/"new_height: 224new_width: 224}}layers {name: "data"type: IMAGE_DATAtop: "data"top: "label"include {phase: TEST}transform_param {mirror: false}image_data_param {batch_size: 10source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Test_0.5_.txt"root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/test_image_sun_227_227/"new_height:224new_width:224}}layers {  bottom: "data"  top: "conv1_1"  name: "conv1_1"  type: CONVOLUTION  convolution_param {    num_output: 64    pad: 1    kernel_size: 3  }}layers {  bottom: "conv1_1"  top: "conv1_1"  name: "relu1_1"  type: RELU}layers {  bottom: "conv1_1"  top: "conv1_2"  name: "conv1_2"  type: CONVOLUTION  convolution_param {    num_output: 64    pad: 1    kernel_size: 3  }}layers {  bottom: "conv1_2"  top: "conv1_2"  name: "relu1_2"  type: RELU}layers {  bottom: "conv1_2"  top: "pool1"  name: "pool1"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool1"  top: "conv2_1"  name: "conv2_1"  type: CONVOLUTION  convolution_param {    num_output: 128    pad: 1    kernel_size: 3  }}layers {  bottom: "conv2_1"  top: "conv2_1"  name: "relu2_1"  type: RELU}layers {  bottom: "conv2_1"  top: "conv2_2"  name: "conv2_2"  type: CONVOLUTION  convolution_param {    num_output: 128    pad: 1    kernel_size: 3  }}layers {  bottom: "conv2_2"  top: "conv2_2"  name: "relu2_2"  type: RELU}layers {  bottom: "conv2_2"  top: "pool2"  name: "pool2"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool2"  top: "conv3_1"  name: "conv3_1"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_1"  top: "conv3_1"  name: "relu3_1"  type: RELU}layers {  bottom: "conv3_1"  top: "conv3_2"  name: "conv3_2"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_2"  top: "conv3_2"  name: "relu3_2"  type: RELU}layers {  bottom: "conv3_2"  top: "conv3_3"  name: "conv3_3"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_3"  top: "conv3_3"  name: "relu3_3"  type: RELU}layers {  bottom: "conv3_3"  top: "pool3"  name: "pool3"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool3"  top: "conv4_1"  name: "conv4_1"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv4_1"  top: "conv4_1"  name: "relu4_1"  type: RELU}layers {  bottom: "conv4_1"  top: "conv4_2"  name: "conv4_2"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv4_2"  top: "conv4_2"  name: "relu4_2"  type: RELU}layers {  bottom: "conv4_2"  top: "conv4_3"  name: "conv4_3"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv4_3"  top: "conv4_3"  name: "relu4_3"  type: RELU}layers {  bottom: "conv4_3"  top: "pool4"  name: "pool4"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool4"  top: "conv5_1"  name: "conv5_1"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv5_1"  top: "conv5_1"  name: "relu5_1"  type: RELU}layers {  bottom: "conv5_1"  top: "conv5_2"  name: "conv5_2"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv5_2"  top: "conv5_2"  name: "relu5_2"  type: RELU}layers {  bottom: "conv5_2"  top: "conv5_3"  name: "conv5_3"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv5_3"  top: "conv5_3"  name: "relu5_3"  type: RELU}layers {  bottom: "conv5_3"  top: "pool5"  name: "pool5"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool5"  top: "fc6"  name: "fc6"  type: INNER_PRODUCT  inner_product_param {    num_output: 4096  }}layers {  bottom: "fc6"  top: "fc6"  name: "relu6"  type: RELU}layers {  bottom: "fc6"  top: "fc6"  name: "drop6"  type: DROPOUT  dropout_param {    dropout_ratio: 0.5  }}layers {  bottom: "fc6"  top: "fc7"  name: "fc7"  type: INNER_PRODUCT  inner_product_param {    num_output: 4096  }}layers {  bottom: "fc7"  top: "fc7"  name: "relu7"  type: RELU}layers {  bottom: "fc7"  top: "fc7"  name: "drop7"  type: DROPOUT  dropout_param {    dropout_ratio: 0.5  }}layers {  bottom: "fc7"  top: "fc8_"  name: "fc8_"  type: INNER_PRODUCT  inner_product_param {    num_output: 88  }}layers {  name: "accuracy"  type: ACCURACY  bottom: "fc8_"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}layers{  name: "loss"  type: SOFTMAX_LOSS  bottom: "fc8_"  bottom: "label"  top: "loss"}