Caffe BP回帰モデルを使用すると予測される結果の大部分が0になるのはなぜですか？

Question

入力データをhdf5形式に変換しました。フォローとして

name: "RegressionNet" 
layer { 
    name: "framert" 
    type: "HDF5Data" 
    top: "data" 
    top: "label" 
    include { 
     phase: TRAIN 
    } 
    hdf5_data_param { 
     source: "train_data_list.txt" 
     batch_size: 100 
    } 
} 
layer { 
    name: "framert" 
    type: "HDF5Data" 
    top: "data" 
    top: "label" 
    include { 
     phase: TEST 
    } 
    hdf5_data_param { 
     source: "test_data_list.txt" 
     batch_size: 100 
    } 
} 
layer { 
    name: "inner1" 
    type: "InnerProduct" 
    bottom: "data" 
    top: "inner1" 
    param { 
     lr_mult: 1 
     decay_mult: 1.5 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 500 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "inner2" 
    type: "InnerProduct" 
    bottom: "inner1" 
    top: "inner2" 
    param { 
     lr_mult: 1 
     decay_mult: 1.0 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 400 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "inner3" 
    type: "InnerProduct" 
    bottom: "inner2" 
    top: "inner3" 
    param { 
     lr_mult: 1 
     decay_mult: 1.0 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 300 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "inner4" 
    type: "InnerProduct" 
    bottom: "inner3" 
    top: "inner4" 
    param { 
     lr_mult: 1 
     decay_mult: 1.0 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 200 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "inner5" 
    type: "InnerProduct" 
    bottom: "inner4" 
    top: "inner5" 
    param { 
     lr_mult: 1 
     decay_mult: 1.0 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 100 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "inner6" 
    type: "InnerProduct" 
    bottom: "inner5" 
    top: "inner6" 
    param { 
     lr_mult: 1 
     decay_mult: 1.0 
    } 
    param { 
     lr_mult: 2 
     decay_mult: 0 
    } 
    inner_product_param { 
     num_output: 1 
     weight_filler { 
      type: "xavier" 
     } 
     bias_filler { 
      type: "constant" 
      value: 0.1 
     } 
    } 
} 
layer { 
    name: "relu1" 
    type: "ReLU" 
    bottom: "inner6" 
    top: "inner6" 
    relu_param { 
     engine: CAFFE 
    } 
} 
layer { 
    name: "accuracy" 
    type: "Accuracy" 
    bottom: "inner6" 
    bottom: "label" 
    top: "accuracy" 
    include { 
     phase: TEST 
    } 
} 
layer { 
    name: "loss" 
    type: "EuclideanLoss" 
    bottom: "inner6" 
    bottom: "label" 
    top: "loss" 
} 

私のソルバー：

net: "net_csv_hdf5.prototxt" 

test_iter: 100 

test_interval: 100 

base_lr: 0.001 

momentum: 0.9 

weight_decay: 0.0005 

lr_policy: "inv" 

gamma: 0.0001 

power: 0.75 

display: 50 

max_iter: 5000 

snapshot: 2500 

snapshot_prefix: "/examples" 

solver_mode: CPU 

次のように part of the input data like this

私のネット構造： そして、それぞれの入力データは309暗くなるの形状やラベル だけで以下のように入力されたデータを持っています

トレーニングフェーズが完了したら、テストデータを使用して予測結果を予測します（あまりにも多くのゼロがあります）。

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0.000122316 
0.00318826 
0.00606083 
0.0092759 
0.0124592 
0.015264 
0.0181027 
0.021088 
0.0237832 
0.027108 
0.0306765 
0.0345342 
0.0379068 
0.0409781 
0.044281 
0.0478444 
0.0509017 

Answer 1

あなたがこの」

layer { 
    name: "Sigmoid1" 
    type: "Sigmoid" 
    bottom: "inner1" 
    top: "Sigmoid1" 
}