Ошибка проверки: как использовать слой данных hdf5 в глубоком слое?

У меня есть данные поезда и метки как data.mat. (У меня есть 200 обучающих данных с 6000 функциями и метками (-1, +1), которые сохранены в data.mat).

Я пытаюсь преобразовать свои данные (обучить и протестировать) в hdf5 и запустить Caffe, используя:

load input.mat
hdf5write('my_data.h5', '/new_train_x', single( permute(reshape(new_train_x,[200, 6000, 1, 1]),[4:-1:1] ) ));
hdf5write('my_data.h5', '/label_train', single( permute(reshape(label_train,[200, 1, 1, 1]), [4:-1:1] ) ) , 'WriteMode', 'append' );
hdf5write('my_data_test.h5', '/test_x', single( permute(reshape(test_x,[77, 6000, 1, 1]),[4:-1:1] ) ));
hdf5write('my_data_test.h5', '/label_test', single( permute(reshape(label_test,[77, 1, 1, 1]), [4:-1:1] ) ) , 'WriteMode', 'append' );

(См. эту тему относительно преобразования файлов mat в hdf5 в Matlab).

My train_val.prototxt is:

  layer {
  type: "HDF5Data"
  name: "data"
  top: "new_train_x"     # note: same name as in HDF5
  top: "label_train"     # 
  hdf5_data_param {
    source: "file.txt"
    batch_size: 20
  }
  include { phase: TRAIN }
}
layer {
  type: "HDF5Data"
  name: "data"
  top: "test_x"     # note: same name as in HDF5
  top: "label_test"     # 
  hdf5_data_param {
    source: "file_test.txt"
    batch_size: 20
  }
  include { phase:TEST }
}

layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "new_train_x"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 30
    weight_filler {
      type: "gaussian" # initialize the filters from a Gaussian
      std: 0.01 
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "tanh1"
  type: "TanH"
  bottom: "ip1"
  top: "tanh1"
}

layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "tanh1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "gaussian" # initialize the filters from a Gaussian
      std: 0.01 
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "TanH"
  bottom: "ip2"
  bottom: "label_train"
  top: "loss"
}

Но у меня есть проблема. Кажется, он не может прочитать мои входные данные.

I1227 10:27:21.880826  7186 layer_factory.hpp:76] Creating layer data
I1227 10:27:21.880851  7186 net.cpp:110] Creating Layer data
I1227 10:27:21.880866  7186 net.cpp:433] data -> new_train_x
I1227 10:27:21.880893  7186 net.cpp:433] data -> label_train
I1227 10:27:21.880915  7186 hdf5_data_layer.cpp:81] Loading list of HDF5 filenames from: file.txt
I1227 10:27:21.880965  7186 hdf5_data_layer.cpp:95] Number of HDF5 files: 1
I1227 10:27:21.962596  7186 net.cpp:155] Setting up data
I1227 10:27:21.962702  7186 net.cpp:163] Top shape: 20 6000 1 1 (120000)
I1227 10:27:21.962738  7186 net.cpp:163] Top shape: 20 1 1 1 (20)
I1227 10:27:21.962772  7186 layer_factory.hpp:76] Creating layer ip1
I1227 10:27:21.962838  7186 net.cpp:110] Creating Layer ip1
I1227 10:27:21.962873  7186 net.cpp:477] ip1 <- new_train_x
I1227 10:27:21.962918  7186 net.cpp:433] ip1 -> ip1
I1227 10:27:21.979375  7186 net.cpp:155] Setting up ip1
I1227 10:27:21.979434  7186 net.cpp:163] Top shape: 20 30 (600)
I1227 10:27:21.979478  7186 layer_factory.hpp:76] Creating layer tanh1
I1227 10:27:21.979529  7186 net.cpp:110] Creating Layer tanh1
I1227 10:27:21.979557  7186 net.cpp:477] tanh1 <- ip1
I1227 10:27:21.979583  7186 net.cpp:433] tanh1 -> tanh1
I1227 10:27:21.979620  7186 net.cpp:155] Setting up tanh1
I1227 10:27:21.979650  7186 net.cpp:163] Top shape: 20 30 (600)
I1227 10:27:21.979670  7186 layer_factory.hpp:76] Creating layer ip2
I1227 10:27:21.979696  7186 net.cpp:110] Creating Layer ip2
I1227 10:27:21.979720  7186 net.cpp:477] ip2 <- tanh1
I1227 10:27:21.979746  7186 net.cpp:433] ip2 -> ip2
I1227 10:27:21.979796  7186 net.cpp:155] Setting up ip2
I1227 10:27:21.979825  7186 net.cpp:163] Top shape: 20 1 (20)
I1227 10:27:21.979854  7186 layer_factory.hpp:76] Creating layer loss
I1227 10:27:21.979881  7186 net.cpp:110] Creating Layer loss
I1227 10:27:21.979909  7186 net.cpp:477] loss <- ip2
I1227 10:27:21.979931  7186 net.cpp:477] loss <- label_train
I1227 10:27:21.979962  7186 net.cpp:433] loss -> loss
F1227 10:27:21.980006  7186 layer.hpp:374] Check failed: ExactNumBottomBlobs() == bottom.size() (1 vs. 2) TanH Layer takes 1 bottom blob(s) as input.
*** Check failure stack trace: ***
    @     0x7f44cbc68ea4  (unknown)
    @     0x7f44cbc68deb  (unknown)
    @     0x7f44cbc687bf  (unknown)
    @     0x7f44cbc6ba35  (unknown)
    @     0x7f44cbfd0ba8  caffe::Layer<>::CheckBlobCounts()
    @     0x7f44cbfed9da  caffe::Net<>::Init()
    @     0x7f44cbfef108  caffe::Net<>::Net()
    @     0x7f44cc03f71a  caffe::Solver<>::InitTrainNet()
    @     0x7f44cc040a51  caffe::Solver<>::Init()
    @     0x7f44cc040db9  caffe::Solver<>::Solver()
    @           0x41222d  caffe::GetSolver<>()
    @           0x408ed9  train()
    @           0x406741  main
    @     0x7f44ca997a40  (unknown)
    @           0x406f69  _start
    @              (nil)  (unknown)
Aborted (core dumped)

Теперь, если я изменю слой потерь следующим образом:

layer {
  name: "loss"
  type: "TanH"
  bottom: "ip2"
  top: "loss"
}

У меня есть проблема:

F1227 10:53:17.884419  9102 insert_splits.cpp:35] Unknown bottom blob 'new_train_x' (layer 'ip1', bottom index 0)
*** Check failure stack trace: ***
    @     0x7f502ab5dea4  (unknown)
    @     0x7f502ab5ddeb  (unknown)
    @     0x7f502ab5d7bf  (unknown)
    @     0x7f502ab60a35  (unknown)
    @     0x7f502af1d75b  caffe::InsertSplits()
    @     0x7f502aee19e9  caffe::Net<>::Init()
    @     0x7f502aee4108  caffe::Net<>::Net()
    @     0x7f502af35172  caffe::Solver<>::InitTestNets()
    @     0x7f502af35abd  caffe::Solver<>::Init()
    @     0x7f502af35db9  caffe::Solver<>::Solver()
    @           0x41222d  caffe::GetSolver<>()
    @           0x408ed9  train()
    @           0x406741  main
    @     0x7f502988ca40  (unknown)
    @           0x406f69  _start
    @              (nil)  (unknown)
Aborted (core dumped)

Большое спасибо!!!! Будем признательны за любые советы!