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 > > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
