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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