Installing Keras with TensorFlow backend The first part of this blog post provides a short discussion of Keras backends and why we should (or should not) care which one we are using. From there I provide detailed instructions that you can use to install Keras with a TensorFlow backend for machine learning on your own system.
Okay, I was able to reproduce this in virtualenv by installing protobuf2.6.1 The short answer is that we depend on protobuf 3.0.0, and having the protobuf pip library installed seems to interfere with ours. Two solutions:.
pip install protobuf=3.0.0a1 or higher, then pip install tensorflow. pip uninstall protobuf first, and then install tensorflow again - it should bring in the dependency I was able to do both in virtualenv and they worked - let me know if either suffices for you. We might add protobuf = 3 to our whl dependencies if so. Found the same issue on my end. Pip install Collecting tensorflow0.5.0 from Using cached Collecting six=1.10.0 (from tensorflow0.5.0) Using cached six-1.10.0-py2.py3-none-any.whl Collecting numpy=1.9.2 (from tensorflow0.5.0) Using cached numpy-1.10.1-cp27-none-macosx106intel.macosx109intel.macosx109x8664.macosx1010intel.macosx1010x8664.whl Installing collected packages: six, numpy, tensorflow Found existing installation: six 1.4.1 DEPRECATION: Uninstalling a distutils installed project (six) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project. I have the same problem, but my protobuf is apparently up-to-date?
Here is the output to pip show numpy protobuf Name: numpy Version: 1.10.1 Location: /mnt/4tbinternal/python/tensorflow/lib/python2.7/site-packages Requires: Name: protobuf Version: 3.0.0a3 Location: /mnt/4tbinternal/python/tensorflow/lib/python2.7/site-packages Requires: setuptools Here is the output to uname -a. My machine runs Ubuntu 14.04 LTS. I have a similar issue on my mac running the eval lua command (th eval.lua -model. /modelid1-5541.t7 -imagefolder. /img -numimages 1) on a small folder of ten images.
I got the same syntax keyword error with both 0.5 and 0.6 versions of tensorflow. I'm using a virtualenv setup. Oddly, I got the error within ipython sessions, but didn't get the error when running python. My solution was to remove ipython from the system install and install it in the virtualenv environment, instead. Hope this helps folks sole their install issues.
Relevant IPython warning that pointed me to this solution: $ ipython WARNING: Attempting to work in a virtualenv. If you encounter problems, please install IPython inside the virtualenv. I got the same syntax error on the gpu 0.5 version installed using python 2. I'm using a virtualenv setup. The other i am not understanding is that when i wrote the 'pip show numpy protobuf ' command the result was as follows Name: numpy Version: 1.8.2 Location: /usr/lib/python2.7/dist-packages Requires: Name: protobuf Version: 2.5.0 Location: /usr/lib/python2.7/dist-packages Requires: where as while importing the google.protobuf and printing the google.protobuf version the result was as follows (tensorflow)root@dmlserver:/home/rzibello/Documents# python Python 2.7.6 (default, Jun 22 2015, 17:58:13) GCC 4.8.2 on linux2 Type 'help', 'copyright', 'credits' or 'license' for more information.
Import google.protobuf print google.protobuf. Version 3.0.0-alpha-1 The OS i am using Linux Ubuntu 14.04. Can sombody please let me know their difference and what i could do get rid off the syntax error as well?. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated).
The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5. Enable the use of libxsmm for matrix multiplications.
Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications). Fixed libxsmmconfigarguments in libxsmm.BUILD. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold).
The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator. For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow.
The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext. Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize). Simply renamed API items in order to follow changes in LIBXSMM 1.7. This is incomplete as more changes/adjustments are needed. Account for removed non-check API. Include libxsmmmalloc.h now that libxsmmtfallocator is used. Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer.
Renamed LIBXSMMDNNCONVFORMAT. to LIBXSMMDNNTENSORFORMAT.
Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer. Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND. to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure).
Updated to new libxsmmdnnlink. functions. Updated to use new libxsmmdnnbind. functions. Fixed calling libxsmmdnntransposefilter.
Updates in preparation of LIBXSMM 1.7. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5.
Enable the use of libxsmm for matrix multiplications. Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications).
Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator. For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow. The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext.
Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize). Simply renamed API items in order to follow changes in LIBXSMM 1.7.
This is incomplete as more changes/adjustments are needed. Account for removed non-check API. Include libxsmmmalloc.h now that libxsmmtfallocator is used. Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer. Renamed LIBXSMMDNNCONVFORMAT. to LIBXSMMDNNTENSORFORMAT. Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer.
Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND. to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure).
Updated to new libxsmmdnnlink. functions. Updated to use new libxsmmdnnbind.
functions. Fixed calling libxsmmdnntransposefilter.
Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5.
Enable the use of libxsmm for matrix multiplications. Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications).
Fixed libxsmmconfigarguments in libxsmm.BUILD. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold.
Note, explicitly JIT'ting code is/was never subject to a threshold. Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator.
For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow.
The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext. Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize). Simply renamed API items in order to follow changes in LIBXSMM 1.7.
This is incomplete as more changes/adjustments are needed. Account for removed non-check API.
Include libxsmmmalloc.h now that libxsmmtfallocator is used. Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer. Renamed LIBXSMMDNNCONVFORMAT. to LIBXSMMDNNTENSORFORMAT. Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer.
Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND.
to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure). Updated to new libxsmmdnnlink.
functions. Updated to use new libxsmmdnnbind. functions. Fixed calling libxsmmdnntransposefilter. Updates in preparation of LIBXSMM 1.7. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch.
Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5.
Enable the use of libxsmm for matrix multiplications. Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications). Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator. For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow. The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext.
Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize). Simply renamed API items in order to follow changes in LIBXSMM 1.7. This is incomplete as more changes/adjustments are needed. Account for removed non-check API. Include libxsmmmalloc.h now that libxsmmtfallocator is used. Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer. Renamed LIBXSMMDNNCONVFORMAT.
to LIBXSMMDNNTENSORFORMAT. Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer. Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND. to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure).
Updated to new libxsmmdnnlink. functions. Updated to use new libxsmmdnnbind. functions. Fixed calling libxsmmdnntransposefilter. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch.
Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5.
Enable the use of libxsmm for matrix multiplications. Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications). Fixed libxsmmconfigarguments in libxsmm.BUILD. Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated).
The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold. Note, explicitly JIT'ting code is/was never subject to a threshold.
Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator. For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow. The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext. Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize).
Simply renamed API items in order to follow changes in LIBXSMM 1.7. This is incomplete as more changes/adjustments are needed. Account for removed non-check API.
Include libxsmmmalloc.h now that libxsmmtfallocator is used. Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer. Renamed LIBXSMMDNNCONVFORMAT. to LIBXSMMDNNTENSORFORMAT.
Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer. Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND. to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure).
Updated to new libxsmmdnnlink. functions. Updated to use new libxsmmdnnbind. functions. Fixed calling libxsmmdnntransposefilter. Updates in preparation of LIBXSMM 1.7.
Fixed libxsmmconfigarguments: Fixed the incorrect value supposed to trigger auto-prefetch. Fixed the 0-threshold, which is now accounted for in LIBXSMM (by just populating the default threshold). The problem arised from the assumption 'threshold: fallback to BLAS if n.m.k above this', which is wrong (the threshold populates an upper bound until which JIT code is generated). The previous configuration perhaps caused all sorts of issues due to other values derived from the 0-threshold.
Note, explicitly JIT'ting code is/was never subject to a threshold. Upgraded to libxsmm 1.6.5. Enable the use of libxsmm for matrix multiplications. Enable the use of libxsmm to speedup 1x1 convolutions (which are computed using matrix multiplications). Make use of TensorFlow's allocation infrastructure even when using LIBXSMM allocation functions. In particular, the (cached) libxsmmspmdminit now relies on TF's cpuallocator.
For C code, one can use a libxsmmscopedallocator in order to (temporarily) setup a different allocation mechanism. For instance, using libxsmmtfallocator changes LIBXSMM's scratch allocator to rely on TensorFlow. The libxsmmtfallocator provides two kinds of c'tors: (1) the no-argument variant adopts TF's cpuallocator, whereas the one-argument form (2) adopts the allocator from the given OpKernelContext.
Changing the allocator in LIBXSMM with pending buffers (from different allocators) is valid, and all other services in LIBXSMM's 'malloc domain' work regardless of the allocation mechanism (e.g., libxsmmmallocsize). Simply renamed API items in order to follow changes in LIBXSMM 1.7. This is incomplete as more changes/adjustments are needed. Account for removed non-check API. Include libxsmmmalloc.h now that libxsmmtfallocator is used.
Renamed libxsmmdnncreateconvhandle to libxsmmdnncreateconvlayer. Renamed LIBXSMMDNNCONVFORMAT.
to LIBXSMMDNNTENSORFORMAT. Renamed libxsmmdnndestroyconvhandle to libxsmmdnndestroyconvlayer. Include missing header file (libxsmmmalloc.h). Renamed LIBXSMMDNNCONVKIND. to LIBXSMMDNNCOMPUTEKIND. Account for the fact that datatypein/out is now only datatype (libxsmmdnnconvdesc structure). Updated to new libxsmmdnnlink.
functions. Updated to use new libxsmmdnnbind. functions.
Fixed calling libxsmmdnntransposefilter.
Phil Goetz April 10, 2017, C: Windows system32pip3 install –upgrade tensorflow C: Windows system32echo off Please use: python -m pip3 to run this feature or: python PYTHONINSTALLDIR Scripts pip3-script.py C: Windows system32python -m pip3 install –upgrade tensorflow E: bin dev lang ActivePython 3.6 python.exe: No module named pip3 C: Windows system32python -m pip install –upgrade tensorflow Collecting tensorflow Could not find a version that satisfies the requirement tensorflow (from versions: ) Suggestions on how to use it with ActivePython? Jake April 10, 2017, Hey Phil, Which version of Python are you using? You are going to need the latest 64-bit Python 3.5.x or higher for in order to get TensorFlow. So just give that a look, if not you can try to from the official website. I did, and yes they do have the latest version of Python.
Although I’m quiet unfamiliar with how ActivePython works, it should work for you if you have the latest version. You can also try to enter this in and see if it does the trick python3 -m pip install -upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0-py3-none-any.whl. Ty May 11, 2017, Hi, I’m new to Python, so apologies for my immediate ignorance, what is the command to install Tensor flow to utilize my SSE instructions? Based on my errors it looks like I need to include the following into my build: SSE, SSE2, SSE3, SSE4.1, SSE4.2, AVX.
I’ve read about using some Bazel build or creating a wheel (not too familiar what that is yet, but I’m assuming it’s a custom build). Do I need to add this Bazel thing first before doing the standard Tensorflow install? Or can I do this all in one go with one command line? Mequanent May 15, 2018, Both pip3 install –upgrade tensorflow and pip3 install tensorflow didn’t work for me as follows. What shall I do? The cmd was run as admininstrator. Vandana July 26, 2018, When I try to install tensorflow I m getting the following error.
Can you suggest a way to resolve this issue and install tensor flow? C: Windows System32python -m pip3 install –upgrade tensorflow C: Users SHM-WS-02 AppData Local Programs Python Python36 python.exe: No module named pip3 C: Windows System32python -m pip install –upgrade tensorflow Collecting tensorflow Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by ‘ReadTimeoutError(“HTTPSConnectionPool(host=’pypi.or g’, port=443): Read timed out.
(read timeout=15)”,)’: /simple/tensorflow/ Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by ‘ReadTimeoutError(“HTTPSConnectionPool(host=’pypi.or g’, port=443): Read timed out. (read timeout=15)”,)’: /simple/tensorflow/ Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by ‘ReadTimeoutError(“HTTPSConnectionPool(host=’pypi.or g’, port=443): Read timed out. (read timeout=15)”,)’: /simple/tensorflow/ Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by ‘ReadTimeoutError(“HTTPSConnectionPool(host=’pypi.or g’, port=443): Read timed out.
(read timeout=15)”,)’: /simple/tensorflow/ Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by ‘ReadTimeoutError(“HTTPSConnectionPool(host=’pypi.or g’, port=443): Read timed out. (read timeout=15)”,)’: /simple/tensorflow/ Could not find a version that satisfies the requirement tensorflow (from versi ons: ) No matching distribution found for tensorflow. Naven Suresh August 21, 2018, Hi I have installed python in windows and while I am installing tensorflow the following error is displayed, Is there any way to resolve this issue?