Check fail: how to use hdf5 data layer in deep layer?

问题

I have the train and label data as data.mat. (I have 200 training data with 6000 features and labels are (-1, +1) that have saved in data.mat).

I am trying to convert my data (train and test) in hdf5 and run Caffe using:

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' );

(See this thread regarding converting mat-files to hdf5 in 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"
}

But I have a problem. It seems, it cannot read my input data.

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)

Now, if i change loss layer like this:

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

I have this problem:

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)

Many thanks!!!! Any advice would be appreciated!

回答1:

Your data layer is only defined for phase: TRAIN I believe the error occurs when caffe attempts to construct the test-time net (i.e., the phase: TEST net).
You should have an additional layer with test data:

layer {
  type: "HDF5Data"
  name: "data"
  top: "new_train_x"     # note: same name as in HDF5
  top: "label_train"     # 
  hdf5_data_param {
    source: "test_file.txt"
    batch_size: 20
  }
  include { phase: TEST } # do not forget TEST phase
}

BTW, if you do not want to test your net during training, you can switch this option off. See this thread for more information.

Update:
Forgive me for being blunt, but you are making quite a mess.

"TanH" is not a loss layer - it's a neuron/activation layer. It serves as a non-linarity applied to a linear layer (conv/inner-product). As such, it accepts a single input (bottom blob) and outputs a single blob (top).
A loss layer computes a scalar loss value and usually requires two inputs: prediction and ground truth to compare to.
You did change your net and added a "HDF5Data" layer for the TEST phase as well, but this layer outputs a top: "test_x", no layer in your net expects a bottom: "test_x" you only have layers expecting "new_train_x"... same goes for "label_text".

I suggest you re-write your hdf5 files with more generic names (e.g., x and label) for both train and test. Just use different file names to distinguish between them. This way your net works with "x" and "label" in both phases and only loads the appropriate dataset according to phase.

来源：https://stackoverflow.com/questions/34454278/check-fail-how-to-use-hdf5-data-layer-in-deep-layer

标签

neural-network

deep-learning

hdf5

caffe