{ "info": { "author": "Santiago Castro", "author_email": "santiago@xmartlabs.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "benderthon\n==========\n\nSet of utilities to work easier with\n`Bender `__.\n\nCurrently there's support for TensorFlow and Caffe, but we are working\non more stuff!\n\nWorks on Python 2.7.+ and 3.+, with Tensorflow 1.2+.\n\nTo install:\n\n.. code:: bash\n\n pip install benderthon\n\nTensorFlow is required too. The simplest way to install it is:\n\n.. code:: bash\n\n pip install tensorflow\n\nThere are other ways, see `Installing\nTensorflow `__. Benderthon does not\ninstall it by default to let the usage of a custom installation.\n\ntf-freeze\n---------\n\nUtility to convert **TensorFlow** checkpoints into minimal frozen\n**graphs**.\n\nUsage\n~~~~~\n\nTo take the checkpoint in ``checkpoint_path.ckpt``, whose output is\nyielded by the node named ``Tanh``, and save it to\n``graph_with_weights.pb``:\n\n.. code:: bash\n\n benderthon tf-freeze checkpoint_path.ckpt graph_with_weights.pb Tanh\n\nSample\n~~~~~~\n\nThe file ``sample.py`` contains a network example for MNIST dataset with\n2 convolutional layers and 2 dens layers. If you run it, it will\ngenerate checkpoints files with prefix ``checkpoints/mnist.ckpt``:\n\n.. code:: bash\n\n ./sample.py\n\nThen you can get a minimal protobuf version with the weights frozen:\n\n.. code:: bash\n\n benderthon tf-freeze checkpoints/mnist.ckpt output/mnist.pb Prediction\n\nThe generated file occupies **half** the original checkpoints (26MB to\n13MB).\n\nYou can also get only the graph, which occupies just **13kB**:\n\n.. code:: bash\n\n benderthon tf-freeze --no-weights checkpoints/mnist.ckpt output/mnist_only_graph.pb Prediction\n\nTo save the weights in a separate path for later processing:\n\n.. code:: bash\n\n benderthon tf-freeze --only-weights checkpoints/mnist.ckpt weights/ Prediction\n\ncaffe-freeze\n------------\n\nThis module cannot be accessed from the command line utility, it should\nbe used from Python code, importing ``benderthon.caffe_freeze``.\n\nYou need ``caffeflow`` package installed first:\n\n.. code:: bash\n\n pip install -e git://github.com/xmartlabs/caffeflow.git@4618f89#egg=caffeflow\n\nDevelopment\n-----------\n\nThis utility is under development and the API **is not stable**. So, do\nnot heavily rely on it.\n\nTo install locally you should do ``./setup.py install``, but first have\n`pandoc `__ and\n`pypandoc `__ installed.\n\nLicense\n-------\n\n::\n\n Copyright 2017 Xmartlabs SRL.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/xmartlabs/benderthon", "keywords": "Bender,machine 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