Question

我在Caffe做回归。数据集是400 x 125x128大小的RGB图像，标签包含范围（-1,1）中的浮点数。我应用于数据集的唯一转换是标准化（将RGB中的每个像素值除以255）。但这种损失似乎并没有收敛。

可能的原因是什么？有人可以建议我吗？

这是我的培训日志：

Training..
Using solver: solver_hdf5.prototxt
I0929 21:50:21.657784 13779 caffe.cpp:112] Use CPU.
I0929 21:50:21.658033 13779 caffe.cpp:174] Starting Optimization
I0929 21:50:21.658107 13779 solver.cpp:34] Initializing solver from parameters: 
test_iter: 100
test_interval: 500
base_lr: 0.0001
display: 25
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "lenet_hdf5"
solver_mode: CPU
net: "train_test_hdf5.prototxt"
I0929 21:50:21.658143 13779 solver.cpp:75] Creating training net from net file: train_test_hdf5.prototxt
I0929 21:50:21.658567 13779 net.cpp:334] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0929 21:50:21.658709 13779 net.cpp:46] Initializing net from parameters: 
name: "MSE regression"
state {
  phase: TRAIN
}
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  hdf5_data_param {
    source: "train_hdf5file.txt"
    batch_size: 64
    shuffle: true
  }
}
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"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "dropout1"
  type: "Dropout"
  bottom: "pool1"
  top: "pool1"
  dropout_param {
    dropout_ratio: 0.1
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "pool1"
  top: "fc1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "dropout2"
  type: "Dropout"
  bottom: "fc1"
  top: "fc1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "fc2"
  bottom: "label"
  top: "loss"
}
I0929 21:50:21.658833 13779 layer_factory.hpp:74] Creating layer data
I0929 21:50:21.658859 13779 net.cpp:96] Creating Layer data
I0929 21:50:21.658871 13779 net.cpp:415] data -> data
I0929 21:50:21.658902 13779 net.cpp:415] data -> label
I0929 21:50:21.658926 13779 net.cpp:160] Setting up data
I0929 21:50:21.658936 13779 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: train_hdf5file.txt
I0929 21:50:21.659220 13779 hdf5_data_layer.cpp:94] Number of HDF5 files: 1
I0929 21:50:21.920578 13779 net.cpp:167] Top shape: 64 3 128 128 (3145728)
I0929 21:50:21.920656 13779 net.cpp:167] Top shape: 64 1 (64)
I0929 21:50:21.920686 13779 layer_factory.hpp:74] Creating layer conv1
I0929 21:50:21.920740 13779 net.cpp:96] Creating Layer conv1
I0929 21:50:21.920774 13779 net.cpp:459] conv1 <- data
I0929 21:50:21.920825 13779 net.cpp:415] conv1 -> conv1
I0929 21:50:21.920877 13779 net.cpp:160] Setting up conv1
I0929 21:50:21.921985 13779 net.cpp:167] Top shape: 64 20 124 124 (19681280)
I0929 21:50:21.922050 13779 layer_factory.hpp:74] Creating layer relu1
I0929 21:50:21.922085 13779 net.cpp:96] Creating Layer relu1
I0929 21:50:21.922108 13779 net.cpp:459] relu1 <- conv1
I0929 21:50:21.922137 13779 net.cpp:404] relu1 -> conv1 (in-place)
I0929 21:50:21.922185 13779 net.cpp:160] Setting up relu1
I0929 21:50:21.922227 13779 net.cpp:167] Top shape: 64 20 124 124 (19681280)
I0929 21:50:21.922250 13779 layer_factory.hpp:74] Creating layer pool1
I0929 21:50:21.922277 13779 net.cpp:96] Creating Layer pool1
I0929 21:50:21.922298 13779 net.cpp:459] pool1 <- conv1
I0929 21:50:21.922323 13779 net.cpp:415] pool1 -> pool1
I0929 21:50:21.922418 13779 net.cpp:160] Setting up pool1
I0929 21:50:21.922472 13779 net.cpp:167] Top shape: 64 20 62 62 (4920320)
I0929 21:50:21.922495 13779 layer_factory.hpp:74] Creating layer dropout1
I0929 21:50:21.922534 13779 net.cpp:96] Creating Layer dropout1
I0929 21:50:21.922555 13779 net.cpp:459] dropout1 <- pool1
I0929 21:50:21.922582 13779 net.cpp:404] dropout1 -> pool1 (in-place)
I0929 21:50:21.922613 13779 net.cpp:160] Setting up dropout1
I0929 21:50:21.922652 13779 net.cpp:167] Top shape: 64 20 62 62 (4920320)
I0929 21:50:21.922672 13779 layer_factory.hpp:74] Creating layer fc1
I0929 21:50:21.922709 13779 net.cpp:96] Creating Layer fc1
I0929 21:50:21.922729 13779 net.cpp:459] fc1 <- pool1
I0929 21:50:21.922757 13779 net.cpp:415] fc1 -> fc1
I0929 21:50:21.922801 13779 net.cpp:160] Setting up fc1
I0929 21:50:22.301134 13779 net.cpp:167] Top shape: 64 500 (32000)
I0929 21:50:22.301193 13779 layer_factory.hpp:74] Creating layer dropout2
I0929 21:50:22.301210 13779 net.cpp:96] Creating Layer dropout2
I0929 21:50:22.301218 13779 net.cpp:459] dropout2 <- fc1
I0929 21:50:22.301232 13779 net.cpp:404] dropout2 -> fc1 (in-place)
I0929 21:50:22.301244 13779 net.cpp:160] Setting up dropout2
I0929 21:50:22.301254 13779 net.cpp:167] Top shape: 64 500 (32000)
I0929 21:50:22.301259 13779 layer_factory.hpp:74] Creating layer fc2
I0929 21:50:22.301270 13779 net.cpp:96] Creating Layer fc2
I0929 21:50:22.301275 13779 net.cpp:459] fc2 <- fc1
I0929 21:50:22.301285 13779 net.cpp:415] fc2 -> fc2
I0929 21:50:22.301295 13779 net.cpp:160] Setting up fc2
I0929 21:50:22.301317 13779 net.cpp:167] Top shape: 64 1 (64)
I0929 21:50:22.301328 13779 layer_factory.hpp:74] Creating layer loss
I0929 21:50:22.301338 13779 net.cpp:96] Creating Layer loss
I0929 21:50:22.301343 13779 net.cpp:459] loss <- fc2
I0929 21:50:22.301350 13779 net.cpp:459] loss <- label
I0929 21:50:22.301360 13779 net.cpp:415] loss -> loss
I0929 21:50:22.301374 13779 net.cpp:160] Setting up loss
I0929 21:50:22.301385 13779 net.cpp:167] Top shape: (1)
I0929 21:50:22.301391 13779 net.cpp:169]     with loss weight 1
I0929 21:50:22.301419 13779 net.cpp:239] loss needs backward computation.
I0929 21:50:22.301425 13779 net.cpp:239] fc2 needs backward computation.
I0929 21:50:22.301430 13779 net.cpp:239] dropout2 needs backward computation.
I0929 21:50:22.301436 13779 net.cpp:239] fc1 needs backward computation.
I0929 21:50:22.301441 13779 net.cpp:239] dropout1 needs backward computation.
I0929 21:50:22.301446 13779 net.cpp:239] pool1 needs backward computation.
I0929 21:50:22.301452 13779 net.cpp:239] relu1 needs backward computation.
I0929 21:50:22.301457 13779 net.cpp:239] conv1 needs backward computation.
I0929 21:50:22.301463 13779 net.cpp:241] data does not need backward computation.
I0929 21:50:22.301468 13779 net.cpp:282] This network produces output loss
I0929 21:50:22.301482 13779 net.cpp:531] Collecting Learning Rate and Weight Decay.
I0929 21:50:22.301491 13779 net.cpp:294] Network initialization done.
I0929 21:50:22.301496 13779 net.cpp:295] Memory required for data: 209652228
I0929 21:50:22.301908 13779 solver.cpp:159] Creating test net (#0) specified by net file: train_test_hdf5.prototxt
I0929 21:50:22.301935 13779 net.cpp:334] The NetState phase (1) differed from the phase (0) specified by a rule in layer data
I0929 21:50:22.302028 13779 net.cpp:46] Initializing net from parameters: 
name: "MSE regression"
state {
  phase: TEST
}
layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  hdf5_data_param {
    source: "test_hdf5file.txt"
    batch_size: 30
  }
}
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"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "dropout1"
  type: "Dropout"
  bottom: "pool1"
  top: "pool1"
  dropout_param {
    dropout_ratio: 0.1
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "pool1"
  top: "fc1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "dropout2"
  type: "Dropout"
  bottom: "fc1"
  top: "fc1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "fc2"
  bottom: "label"
  top: "loss"
}
I0929 21:50:22.302146 13779 layer_factory.hpp:74] Creating layer data
I0929 21:50:22.302158 13779 net.cpp:96] Creating Layer data
I0929 21:50:22.302165 13779 net.cpp:415] data -> data
I0929 21:50:22.302176 13779 net.cpp:415] data -> label
I0929 21:50:22.302186 13779 net.cpp:160] Setting up data
I0929 21:50:22.302191 13779 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: test_hdf5file.txt
I0929 21:50:22.302305 13779 hdf5_data_layer.cpp:94] Number of HDF5 files: 1
I0929 21:50:22.434798 13779 net.cpp:167] Top shape: 30 3 128 128 (1474560)
I0929 21:50:22.434849 13779 net.cpp:167] Top shape: 30 1 (30)
I0929 21:50:22.434864 13779 layer_factory.hpp:74] Creating layer conv1
I0929 21:50:22.434895 13779 net.cpp:96] Creating Layer conv1
I0929 21:50:22.434914 13779 net.cpp:459] conv1 <- data
I0929 21:50:22.434944 13779 net.cpp:415] conv1 -> conv1
I0929 21:50:22.434996 13779 net.cpp:160] Setting up conv1
I0929 21:50:22.435084 13779 net.cpp:167] Top shape: 30 20 124 124 (9225600)
I0929 21:50:22.435119 13779 layer_factory.hpp:74] Creating layer relu1
I0929 21:50:22.435205 13779 net.cpp:96] Creating Layer relu1
I0929 21:50:22.435237 13779 net.cpp:459] relu1 <- conv1
I0929 21:50:22.435292 13779 net.cpp:404] relu1 -> conv1 (in-place)
I0929 21:50:22.435328 13779 net.cpp:160] Setting up relu1
I0929 21:50:22.435371 13779 net.cpp:167] Top shape: 30 20 124 124 (9225600)
I0929 21:50:22.435400 13779 layer_factory.hpp:74] Creating layer pool1
I0929 21:50:22.435443 13779 net.cpp:96] Creating Layer pool1
I0929 21:50:22.435470 13779 net.cpp:459] pool1 <- conv1
I0929 21:50:22.435511 13779 net.cpp:415] pool1 -> pool1
I0929 21:50:22.435550 13779 net.cpp:160] Setting up pool1
I0929 21:50:22.435597 13779 net.cpp:167] Top shape: 30 20 62 62 (2306400)
I0929 21:50:22.435626 13779 layer_factory.hpp:74] Creating layer dropout1
I0929 21:50:22.435669 13779 net.cpp:96] Creating Layer dropout1
I0929 21:50:22.435698 13779 net.cpp:459] dropout1 <- pool1
I0929 21:50:22.435739 13779 net.cpp:404] dropout1 -> pool1 (in-place)
I0929 21:50:22.435780 13779 net.cpp:160] Setting up dropout1
I0929 21:50:22.435823 13779 net.cpp:167] Top shape: 30 20 62 62 (2306400)
I0929 21:50:22.435853 13779 layer_factory.hpp:74] Creating layer fc1
I0929 21:50:22.435899 13779 net.cpp:96] Creating Layer fc1
I0929 21:50:22.435926 13779 net.cpp:459] fc1 <- pool1
I0929 21:50:22.435971 13779 net.cpp:415] fc1 -> fc1
I0929 21:50:22.436018 13779 net.cpp:160] Setting up fc1
I0929 21:50:22.816076 13779 net.cpp:167] Top shape: 30 500 (15000)
I0929 21:50:22.816138 13779 layer_factory.hpp:74] Creating layer dropout2
I0929 21:50:22.816154 13779 net.cpp:96] Creating Layer dropout2
I0929 21:50:22.816160 13779 net.cpp:459] dropout2 <- fc1
I0929 21:50:22.816170 13779 net.cpp:404] dropout2 -> fc1 (in-place)
I0929 21:50:22.816182 13779 net.cpp:160] Setting up dropout2
I0929 21:50:22.816192 13779 net.cpp:167] Top shape: 30 500 (15000)
I0929 21:50:22.816197 13779 layer_factory.hpp:74] Creating layer fc2
I0929 21:50:22.816208 13779 net.cpp:96] Creating Layer fc2
I0929 21:50:22.816249 13779 net.cpp:459] fc2 <- fc1
I0929 21:50:22.816262 13779 net.cpp:415] fc2 -> fc2
I0929 21:50:22.816277 13779 net.cpp:160] Setting up fc2
I0929 21:50:22.816301 13779 net.cpp:167] Top shape: 30 1 (30)
I0929 21:50:22.816316 13779 layer_factory.hpp:74] Creating layer loss
I0929 21:50:22.816329 13779 net.cpp:96] Creating Layer loss
I0929 21:50:22.816337 13779 net.cpp:459] loss <- fc2
I0929 21:50:22.816347 13779 net.cpp:459] loss <- label
I0929 21:50:22.816359 13779 net.cpp:415] loss -> loss
I0929 21:50:22.816370 13779 net.cpp:160] Setting up loss
I0929 21:50:22.816381 13779 net.cpp:167] Top shape: (1)
I0929 21:50:22.816388 13779 net.cpp:169]     with loss weight 1
I0929 21:50:22.816407 13779 net.cpp:239] loss needs backward computation.
I0929 21:50:22.816416 13779 net.cpp:239] fc2 needs backward computation.
I0929 21:50:22.816426 13779 net.cpp:239] dropout2 needs backward computation.
I0929 21:50:22.816433 13779 net.cpp:239] fc1 needs backward computation.
I0929 21:50:22.816442 13779 net.cpp:239] dropout1 needs backward computation.
I0929 21:50:22.816452 13779 net.cpp:239] pool1 needs backward computation.
I0929 21:50:22.816460 13779 net.cpp:239] relu1 needs backward computation.
I0929 21:50:22.816468 13779 net.cpp:239] conv1 needs backward computation.
I0929 21:50:22.816478 13779 net.cpp:241] data does not need backward computation.
I0929 21:50:22.816486 13779 net.cpp:282] This network produces output loss
I0929 21:50:22.816500 13779 net.cpp:531] Collecting Learning Rate and Weight Decay.
I0929 21:50:22.816510 13779 net.cpp:294] Network initialization done.
I0929 21:50:22.816517 13779 net.cpp:295] Memory required for data: 98274484
I0929 21:50:22.816565 13779 solver.cpp:47] Solver scaffolding done.
I0929 21:50:22.816587 13779 solver.cpp:363] Solving MSE regression
I0929 21:50:22.816596 13779 solver.cpp:364] Learning Rate Policy: inv
I0929 21:50:22.870337 13779 solver.cpp:424] Iteration 0, Testing net (#0)

更新（这是在@lejlot＆＃39的回复之后）

更改我的数据后训练图像：

Answer 1

似乎在学习，损失惨淡。但是，您的数据显然存在问题。在学习之前（迭代0），你已经有0.0006的损失。对于随机模型，这是非常小的损失。因此，您的数据看起来很奇怪。看看你的依赖值，它们是否真的很好地分布在-1和1之间？或者它是否有99％的“0”和其他几个值？方法本身没有任何问题，您只需要对数据进行更多分析。确保它实际上跨越[-1,1]间隔。一旦你修复它，就会有更多的小东西可以玩 - 但这是目前最大的问题 - 你用随机模型来解决小错误，因此问题是数据，而不是算法/方法/参数。为了让事情变得更快，您还可以将学习率从目前使用的0.0001提高，但如前所述 - 首先修复数据。

损失没有收敛的咖啡回归

1 个答案: