Thank you Fred! I read that it was written for Python 2.7. According to you
is so complex to convert it to Python 3.4? I followed your advices and the
errors are these ones:
$ sh generate_toy_data.sh
ciao
generating toy dataset ...
make_hkl.py:72: VisibleDeprecationWarning: using a non-integer number
instead of an integer will result in an error in the future
hkl.dump(img_batch[:, :, :, :half_size],
make_hkl.py:76: VisibleDeprecationWarning: using a non-integer number
instead of an integer will result in an error in the future
hkl.dump(img_batch[:, :, :, half_size:],
Traceback (most recent call last):
File "make_train_val_txt.py", line 26, in <module>
synsets = scipy.io.loadmat(meta_clsloc_mat)['synsets'][0]
File
"C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio.py",
line 136, in loadmat
matfile_dict = MR.get_variables(variable_names)
File
"C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio5.py",
line 272, in get_variables
hdr, next_position = self.read_var_header()
File
"C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio5.py",
line 226, in read_var_header
mdtype, byte_count = self._matrix_reader.read_full_tag()
File "scipy\io\matlab\mio5_utils.pyx", line 546, in
scipy.io.matlab.mio5_utils.VarReader5.read_full_tag
(scipy\io\matlab\mio5_utils.c:5330)
File "scipy\io\matlab\mio5_utils.pyx", line 554, in
scipy.io.matlab.mio5_utils.VarReader5.cread_full_tag
(scipy\io\matlab\mio5_utils.c:5400)
File "scipy\io\matlab\streams.pyx", line 164, in
scipy.io.matlab.streams.ZlibInputStream.read_into
(scipy\io\matlab\streams.c:3052)
File "scipy\io\matlab\streams.pyx", line 151, in
scipy.io.matlab.streams.ZlibInputStream._fill_buffer
(scipy\io\matlab\streams.c:2913)
zlib.error: Error -3 while decompressing data: invalid distance too far back
Il giorno martedì 14 marzo 2017 13:42:11 UTC+1, nouiz ha scritto:
>
> The only one error you wrote about is for to different o Python version.
> Not hdf5. Use Python 2.7 or do the fix Jesse wrote.
>
> Fred
>
> Le mar. 14 mars 2017 06:27, Goffredo Giordano <[email protected]
> <javascript:>> a écrit :
>
>> Thank you Salah! Your comment was useful, however I think that more
>> important are the issues from scipy.io.matlab. I have installed h5py 2.6.0
>> and I think that hdf5 library is still working. But I'm not so sure and
>> probably these problems are related to the hdf5 library, or matlab file
>> meta_clsloc.mat. What's your idea about?
>>
>>
>> Il giorno lunedì 13 marzo 2017 20:44:27 UTC+1, Salah Rifai ha scritto:
>>
>>> It's weird since all the numerator and denominator are both ints. Try
>>> explicitly cast it to int:
>>>
>>> for ind in range(int(labels.size / batch_size)):
>>>
>>> Best,
>>>
>> On Mon, Mar 13, 2017 at 2:51 PM, Goffredo Giordano <[email protected]
>>> > wrote:
>>>
>> Thank you, but it doesn't modify nothing. The errors are the same.
>>>>
>>>>
>>>>
>>>>
>>>> Il giorno lunedì 13 marzo 2017 17:12:06 UTC+1, Jesse Livezey ha scritto:
>>>>>
>>>>> You can probably modify line 27 in make_labels.py to be
>>>>> for ind in range(labels.size // batch_size):
>>>>>
>>>>> This code was probably written with python 2 where division worked
>>>>> differently.
>>>>>
>>>>> On Monday, March 13, 2017 at 8:45:39 AM UTC-7, Goffredo Giordano wrote:
>>>>>>
>>>>>> Hi,
>>>>>> I'm a new user and I'm trying to study the ample world of machine
>>>>>> learning. I would like to run the theano_alexnet training from
>>>>>> https://github.com/uoguelph-mlrg/theano_alexnet.
>>>>>> My computer is a Windows 10 native-machine 64 bit Intel core i7. I
>>>>>> use WinPython-64bit-3.4.4.4QT5 from WinPython 3.4.4.3, Visual Studio
>>>>>> 2015
>>>>>> Community Edition Update 3, CUDA 8.0.44 (64-bit), cuDNN v5.1 (August 10,
>>>>>> 2016) for CUDA 8.0, Git source control based on MinGW compiler and
>>>>>> OpenBLAS
>>>>>> 0.2.14.
>>>>>> As fundamental python libraries Theano is 0.9.0beta1 version, Scipy
>>>>>> is 0.19.0, Keras 1.2.2, Lasagne 0.2.dev1, Numpy 1.11.1, hickle 2.0.4,
>>>>>> h5py
>>>>>> 2.6.0, pycuda, pylearn2, zeromq.
>>>>>> I have downloaded the training images, the validation images and I
>>>>>> have unzipped the development kit from Imagenet dataset. I have
>>>>>> configured
>>>>>> the paths.yaml with my folders but I do not know where I could find the
>>>>>> val.txt and train.txt files. I used the meta_clsloc.mat file and
>>>>>> ILSVRC2012_validation_ground_truth.txt file from the development kit
>>>>>> from
>>>>>> Imagenet dataset. With the Git bash control i try to run the
>>>>>> generate_toy_data.sh and I can find the train_labels.npy,
>>>>>> val_labels.npy,
>>>>>> img_mean.npy, shuffled_train_filenames.npy with the validation alex net
>>>>>> *.hkl files, but nothing in the training folder. Probably I forgot some
>>>>>> important features, so I would apologize previously. Thank you so much!
>>>>>>
>>>>>>
>>>>>> Goffredo_Giordano@Goffredo MINGW64
>>>>>> /c/deep_learning/alexnet/preprocessing
>>>>>> $ sh generate_toy_data.sh
>>>>>> ciao
>>>>>> generating toy dataset ...
>>>>>> make_hkl.py:72:
>>>>>> VisibleDeprecationWarning: using a non-integer number instead of an
>>>>>> integer
>>>>>> will result in an error in the future
>>>>>> hkl.dump(img_batch[:, :, :, :half_size],
>>>>>> make_hkl.py:76: VisibleDeprecationWarning: using a non-integer number
>>>>>> instead of an integer will result in an error in the future
>>>>>> hkl.dump(img_batch[:, :, :, half_size:],
>>>>>> Traceback (most recent call last):
>>>>>> File "make_train_val_txt.py", line 26, in <module>
>>>>>> synsets = scipy.io.loadmat(meta_clsloc_mat)['synsets'][0]
>>>>>> File
>>>>>> "C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio.py",
>>>>>>
>>>>>> line 136, in loadmat
>>>>>> matfile_dict = MR.get_variables(variable_names)
>>>>>> File
>>>>>> "C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio5.py",
>>>>>>
>>>>>> line 272, in get_variables
>>>>>> hdr, next_position = self.read_var_header()
>>>>>> File
>>>>>> "C:\deep_learning\WinPython-64bit-3.4.4.4Qt5\python-3.4.4.amd64\lib\site-packages\scipy\io\matlab\mio5.py",
>>>>>>
>>>>>> line 226, in read_var_header
>>>>>> mdtype, byte_count = self._matrix_reader.read_full_tag()
>>>>>> File "scipy\io\matlab\mio5_utils.pyx", line 546, in
>>>>>> scipy.io.matlab.mio5_utils.VarReader5.read_full_tag
>>>>>> (scipy\io\matlab\mio5_utils.c:5330)
>>>>>> File "scipy\io\matlab\mio5_utils.pyx", line 554, in
>>>>>> scipy.io.matlab.mio5_utils.VarReader5.cread_full_tag
>>>>>> (scipy\io\matlab\mio5_utils.c:5400)
>>>>>> File "scipy\io\matlab\streams.pyx", line 164, in
>>>>>> scipy.io.matlab.streams.ZlibInputStream.read_into
>>>>>> (scipy\io\matlab\streams.c:3052)
>>>>>> File "scipy\io\matlab\streams.pyx", line 151, in
>>>>>> scipy.io.matlab.streams.ZlibInputStream._fill_buffer
>>>>>> (scipy\io\matlab\streams.c:2913)
>>>>>> zlib.error: Error -3 while decompressing data: invalid distance too
>>>>>> far back
>>>>>> make_labels.py:17: VisibleDeprecationWarning: using a non-integer
>>>>>> number instead of an integer will result in an error in the future
>>>>>> labels = labels[:labels.size / orig_batch_size * orig_batch_size]
>>>>>> make_labels.py:23: VisibleDeprecationWarning: using a non-integer
>>>>>> number instead of an integer will result in an error in the future
>>>>>> labels_0 = labels.reshape((-1, batch_size))[::num_div].reshape(-1)
>>>>>> make_labels.py:24: VisibleDeprecationWarning: using a non-integer
>>>>>> number instead of an integer will result in an error in the future
>>>>>> labels_1 = labels.reshape((-1, batch_size))[1::num_div].reshape(-1)
>>>>>> Traceback (most recent call last):
>>>>>> File "make_labels.py", line 125, in <module>
>>>>>> div_labels(train_label_name, orig_batch_size, num_div)
>>>>>> File "make_labels.py", line 27, in div_labels
>>>>>> for ind in range(labels.size / batch_size):
>>>>>> TypeError: 'float' object cannot be interpreted as an integer
>>>>>>
>>>>>> --
>>>>
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