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!
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.
Related
I have a caffe "Check failed" error as:
...
I0415 15:35:30.497133 39410 net.cpp:129] Top shape: 1 4096 (4096)
I0415 15:35:30.497135 39410 net.cpp:137] Memory required for data: 2898304
I0415 15:35:30.497138 39410 layer_factory.hpp:77] Creating layer conv1
I0415 15:35:30.497155 39410 net.cpp:84] Creating Layer conv1
I0415 15:35:30.497169 39410 net.cpp:406] conv1 <- ReLU0
I0415 15:35:30.497174 39410 net.cpp:380] conv1 -> conv1
F0415 15:35:30.497185 39410 base_conv_layer.cpp:35] Check failed: num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_ kernel_size must be specified once, or once per spatial dimension (kernel_size specified 2 times; 0 spatial dims).
and here is a little part of the proto.txt file:
...
layer {
name: "loss0"
type: "EuclideanLoss"
bottom: "ampl0"
bottom: "label_b4_noise"
top: "loss0"
}
layer {
name: "ReLU0"
type: "ReLU"
bottom: "ampl0"
top: "ReLU0"
relu_param {
negative_slope: 0
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "ReLU0"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 16
bias_term: false
pad: 0
pad: 0
kernel_size: 1
kernel_size: 5
group: 1
stride: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
axis: 1
}
}
...
could you please tell me why "Check failed"?
what "kernel_size specified 2 times; 0 spatial dims" means?
what num_spatial_axes_ kernel_size is here?
sorry if my question is trivial.
Look at the input for your layer ("ReLU0"):
I0415 15:35:30.497133 39410 net.cpp:129] Top shape: 1 4096 (4096)
Its dimensions are 1x4096 that is, it has 1 batch with 4096 channels with no width and no height (that is, width and height are singleton dimensions that are usually ignored).
Now you want to apply "conv1" a 1x5 kernel. How do you want to apply a convolution on a blob that has no spatial dimensions?!
This is the error you got from caffe: you specified 2 spatial dimensions for conv kernel (you specified kernel_size twice) but your input blob has no spatial dimensions at all, that is its num_spatial_axes_==0.
I've trained a net and trying to apply it, but recieve following error:
Cannot copy param 0 weigths from layer 'ip1'; shape mismatch. Source
param shape is 384 72576 (27869184); target param shape is 384
224(86016). To learn this layer's parameters from scratch rather than
copying from a saved net, rename the layer.
Net config for this layer looks like this:
layer {
type: "Concat"
bottom: "conv5f"
bottom: "conv5_pf"
top: "feat"
name: "concat1"
}
layer {
name: "ip1"
type: "InnerProduct"
bottom: "feat"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 384
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
It is a joint place for Siamese network.
When I start training I recieve following output:
setting up concat
Top shape: 256 72576
...
Setting up ip1
Top shape: 256 384
Applying net:
setting up concat
Top shape: 256 224
...
Setting up ip1
Top shape: 256 384
I've used batch size of 256 while training, if it matters.
What is wrong here? I just can't see. I've copypasted my net from train.prototxt file to apply_net.prototxt, that's it
The mismatch is due to different blob sizes of the layers. You need to compute the output dimensions of each layer of your network. Use the formula given here.
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 in hdf5 and run Caffe using:
load data.mat
hdf5write('my_data.h5', '/new_train_x', single( reshape(new_train_x,[200, 6000, 1, 1]) ) );
hdf5write('my_data.h5', '/label_train', single( reshape(label_train,[200, 1, 1, 1]) ), 'WriteMode', 'append' );
And my layer.prototxt (just data layer) is:
layer {
type: "HDF5Data"
name: "data"
top: "new_train_x" # note: same name as in HDF5
top: "label_train" #
hdf5_data_param {
source: "/path/to/list/file.txt"
batch_size: 20
}
include { phase: TRAIN }
}
but, i have an error:
( Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000))
I1222 17:02:48.915861 3941 layer_factory.hpp:76] Creating layer data
I1222 17:02:48.915871 3941 net.cpp:110] Creating Layer data
I1222 17:02:48.915877 3941 net.cpp:433] data -> new_train_x
I1222 17:02:48.915890 3941 net.cpp:433] data -> label_train
I1222 17:02:48.915900 3941 hdf5_data_layer.cpp:81] Loading list of HDF5 filenames from: file.txt
I1222 17:02:48.915923 3941 hdf5_data_layer.cpp:95] Number of HDF5 files: 1
F1222 17:02:48.993865 3941 hdf5_data_layer.cpp:55] Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000)
*** Check failure stack trace: ***
# 0x7fd2e6608ddd google::LogMessage::Fail()
# 0x7fd2e660ac90 google::LogMessage::SendToLog()
# 0x7fd2e66089a2 google::LogMessage::Flush()
# 0x7fd2e660b6ae google::LogMessageFatal::~LogMessageFatal()
# 0x7fd2e69f9eda caffe::HDF5DataLayer<>::LoadHDF5FileData()
# 0x7fd2e69f901f caffe::HDF5DataLayer<>::LayerSetUp()
# 0x7fd2e6a48030 caffe::Net<>::Init()
# 0x7fd2e6a49278 caffe::Net<>::Net()
# 0x7fd2e6a9157a caffe::Solver<>::InitTrainNet()
# 0x7fd2e6a928b1 caffe::Solver<>::Init()
# 0x7fd2e6a92c19 caffe::Solver<>::Solver()
# 0x41222d caffe::GetSolver<>()
# 0x408ed9 train()
# 0x406741 main
# 0x7fd2e533ca40 (unknown)
# 0x406f69 _start
Aborted (core dumped)
Many thanks!!!! Any advice would be appreciated!
The problem
It seems like there is indeed a conflict with the order of elements in arrays: matlab arranges the elements from the first dimension to the last (like fortran), while caffe and hdf5 stores the arrays from last dimension to first:
Suppose we have X of shape nxcxhxw then the "second element of X" is X[2,1,1,1] in matlab but X[0,0,0,1] in C (1-based vs 0-based indexing doesn't make life easier at all).
Therefore, when you save an array of size=[200, 6000, 1, 1] in Matlab, what hdf5 and caffe are actually seeing is as array of shape=[6000,200].
Using the h5ls command line tool can help you spot the problem.
In matlab you saved
>> hdf5write('my_data.h5', '/new_train_x',
single( reshape(new_train_x,[200, 6000, 1, 1]) );
>> hdf5write('my_data.h5', '/label_train',
single( reshape(label_train,[200, 1, 1, 1]) ),
'WriteMode', 'append' );
Now you can inspect the resulting my_data.h5 using h5ls (in Linux terminal):
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200}
new_train_x Dataset {6000, 200}
As you can see, the arrays are written "backwards".
Solution
Taking this conflict into account when exporting data from Matlab, you should permute:
load data.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' );
Inspect the resulting my_data.h5 using h5ls now results with:
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200, 1, 1, 1}
new_train_x Dataset {200, 6000, 1, 1}
Which is what you expected in the first place.
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 in hdf5 and run Caffe using:
load data.mat
hdf5write('my_data.h5', '/new_train_x', single( reshape(new_train_x,[200, 6000, 1, 1]) ) );
hdf5write('my_data.h5', '/label_train', single( reshape(label_train,[200, 1, 1, 1]) ), 'WriteMode', 'append' );
And my layer.prototxt (just data layer) is:
layer {
type: "HDF5Data"
name: "data"
top: "new_train_x" # note: same name as in HDF5
top: "label_train" #
hdf5_data_param {
source: "/path/to/list/file.txt"
batch_size: 20
}
include { phase: TRAIN }
}
but, i have an error:
( Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000))
I1222 17:02:48.915861 3941 layer_factory.hpp:76] Creating layer data
I1222 17:02:48.915871 3941 net.cpp:110] Creating Layer data
I1222 17:02:48.915877 3941 net.cpp:433] data -> new_train_x
I1222 17:02:48.915890 3941 net.cpp:433] data -> label_train
I1222 17:02:48.915900 3941 hdf5_data_layer.cpp:81] Loading list of HDF5 filenames from: file.txt
I1222 17:02:48.915923 3941 hdf5_data_layer.cpp:95] Number of HDF5 files: 1
F1222 17:02:48.993865 3941 hdf5_data_layer.cpp:55] Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000)
*** Check failure stack trace: ***
# 0x7fd2e6608ddd google::LogMessage::Fail()
# 0x7fd2e660ac90 google::LogMessage::SendToLog()
# 0x7fd2e66089a2 google::LogMessage::Flush()
# 0x7fd2e660b6ae google::LogMessageFatal::~LogMessageFatal()
# 0x7fd2e69f9eda caffe::HDF5DataLayer<>::LoadHDF5FileData()
# 0x7fd2e69f901f caffe::HDF5DataLayer<>::LayerSetUp()
# 0x7fd2e6a48030 caffe::Net<>::Init()
# 0x7fd2e6a49278 caffe::Net<>::Net()
# 0x7fd2e6a9157a caffe::Solver<>::InitTrainNet()
# 0x7fd2e6a928b1 caffe::Solver<>::Init()
# 0x7fd2e6a92c19 caffe::Solver<>::Solver()
# 0x41222d caffe::GetSolver<>()
# 0x408ed9 train()
# 0x406741 main
# 0x7fd2e533ca40 (unknown)
# 0x406f69 _start
Aborted (core dumped)
Many thanks!!!! Any advice would be appreciated!
The problem
It seems like there is indeed a conflict with the order of elements in arrays: matlab arranges the elements from the first dimension to the last (like fortran), while caffe and hdf5 stores the arrays from last dimension to first:
Suppose we have X of shape nxcxhxw then the "second element of X" is X[2,1,1,1] in matlab but X[0,0,0,1] in C (1-based vs 0-based indexing doesn't make life easier at all).
Therefore, when you save an array of size=[200, 6000, 1, 1] in Matlab, what hdf5 and caffe are actually seeing is as array of shape=[6000,200].
Using the h5ls command line tool can help you spot the problem.
In matlab you saved
>> hdf5write('my_data.h5', '/new_train_x',
single( reshape(new_train_x,[200, 6000, 1, 1]) );
>> hdf5write('my_data.h5', '/label_train',
single( reshape(label_train,[200, 1, 1, 1]) ),
'WriteMode', 'append' );
Now you can inspect the resulting my_data.h5 using h5ls (in Linux terminal):
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200}
new_train_x Dataset {6000, 200}
As you can see, the arrays are written "backwards".
Solution
Taking this conflict into account when exporting data from Matlab, you should permute:
load data.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' );
Inspect the resulting my_data.h5 using h5ls now results with:
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200, 1, 1, 1}
new_train_x Dataset {200, 6000, 1, 1}
Which is what you expected in the first place.
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 in hdf5 and run Caffe using:
load data.mat
hdf5write('my_data.h5', '/new_train_x', single( reshape(new_train_x,[200, 6000, 1, 1]) ) );
hdf5write('my_data.h5', '/label_train', single( reshape(label_train,[200, 1, 1, 1]) ), 'WriteMode', 'append' );
And my layer.prototxt (just data layer) is:
layer {
type: "HDF5Data"
name: "data"
top: "new_train_x" # note: same name as in HDF5
top: "label_train" #
hdf5_data_param {
source: "/path/to/list/file.txt"
batch_size: 20
}
include { phase: TRAIN }
}
but, i have an error:
( Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000))
I1222 17:02:48.915861 3941 layer_factory.hpp:76] Creating layer data
I1222 17:02:48.915871 3941 net.cpp:110] Creating Layer data
I1222 17:02:48.915877 3941 net.cpp:433] data -> new_train_x
I1222 17:02:48.915890 3941 net.cpp:433] data -> label_train
I1222 17:02:48.915900 3941 hdf5_data_layer.cpp:81] Loading list of HDF5 filenames from: file.txt
I1222 17:02:48.915923 3941 hdf5_data_layer.cpp:95] Number of HDF5 files: 1
F1222 17:02:48.993865 3941 hdf5_data_layer.cpp:55] Check failed: hdf_blobs_[i]->shape(0) == num (200 vs. 6000)
*** Check failure stack trace: ***
# 0x7fd2e6608ddd google::LogMessage::Fail()
# 0x7fd2e660ac90 google::LogMessage::SendToLog()
# 0x7fd2e66089a2 google::LogMessage::Flush()
# 0x7fd2e660b6ae google::LogMessageFatal::~LogMessageFatal()
# 0x7fd2e69f9eda caffe::HDF5DataLayer<>::LoadHDF5FileData()
# 0x7fd2e69f901f caffe::HDF5DataLayer<>::LayerSetUp()
# 0x7fd2e6a48030 caffe::Net<>::Init()
# 0x7fd2e6a49278 caffe::Net<>::Net()
# 0x7fd2e6a9157a caffe::Solver<>::InitTrainNet()
# 0x7fd2e6a928b1 caffe::Solver<>::Init()
# 0x7fd2e6a92c19 caffe::Solver<>::Solver()
# 0x41222d caffe::GetSolver<>()
# 0x408ed9 train()
# 0x406741 main
# 0x7fd2e533ca40 (unknown)
# 0x406f69 _start
Aborted (core dumped)
Many thanks!!!! Any advice would be appreciated!
The problem
It seems like there is indeed a conflict with the order of elements in arrays: matlab arranges the elements from the first dimension to the last (like fortran), while caffe and hdf5 stores the arrays from last dimension to first:
Suppose we have X of shape nxcxhxw then the "second element of X" is X[2,1,1,1] in matlab but X[0,0,0,1] in C (1-based vs 0-based indexing doesn't make life easier at all).
Therefore, when you save an array of size=[200, 6000, 1, 1] in Matlab, what hdf5 and caffe are actually seeing is as array of shape=[6000,200].
Using the h5ls command line tool can help you spot the problem.
In matlab you saved
>> hdf5write('my_data.h5', '/new_train_x',
single( reshape(new_train_x,[200, 6000, 1, 1]) );
>> hdf5write('my_data.h5', '/label_train',
single( reshape(label_train,[200, 1, 1, 1]) ),
'WriteMode', 'append' );
Now you can inspect the resulting my_data.h5 using h5ls (in Linux terminal):
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200}
new_train_x Dataset {6000, 200}
As you can see, the arrays are written "backwards".
Solution
Taking this conflict into account when exporting data from Matlab, you should permute:
load data.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' );
Inspect the resulting my_data.h5 using h5ls now results with:
user#host:~/$ h5ls ./my_data.h5
label_train Dataset {200, 1, 1, 1}
new_train_x Dataset {200, 6000, 1, 1}
Which is what you expected in the first place.