{
    "info": {
        "author": "nake nat",
        "author_email": "nakanat.stock@gmail.com",
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        "description": "[![Build Status](https://travis-ci.org/neka-nat/tensorboard-chainer.svg?branch=master)](https://travis-ci.org/neka-nat/tensorboard-chainer)\n[![codecov](https://codecov.io/gh/neka-nat/tensorboard-chainer/branch/master/graph/badge.svg)](https://codecov.io/gh/neka-nat/tensorboard-chainer)\n\n[![Code Climate](https://codeclimate.com/github/neka-nat/tensorboard-chainer/badges/gpa.svg)](https://codeclimate.com/github/neka-nat/tensorboard-chainer)\n\n[![PyPI version](https://badge.fury.io/py/tensorboard-chainer.svg)](https://badge.fury.io/py/tensorboard-chainer)\n\n# tensorboard-chainer\n\nWrite tensorboard events with simple command.\nincluding scalar, image, histogram, audio, text, graph and embedding.\n\nThis is based on [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch).\n\n## Usage\n\nInstall tensorflow.\n\n```\npip install tensorflow\n```\n\nExecute demo.py and tensorboard.\nAccess \"localhost:6006\" in your browser.\n\n```\ncd examples\npython demo.py\ntensorboard --logdir runs\n```\n\n## Scalar example\n\n![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/scalar.png)\n\n## Histogram example\n\n![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/histogram.png)\n\n## Graph example\n\n![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/graph.gif)\n\n## Name scope\n\nLike tensorflow, nodes in the graph can be grouped together in the namespace to make it easy to see.\n\n```python\nimport chainer\nimport chainer.functions as F\nimport chainer.links as L\nfrom tb_chainer import name_scope, within_name_scope\n\nclass MLP(chainer.Chain):\n    def __init__(self, n_units, n_out):\n        super(MLP, self).__init__()\n        with self.init_scope():\n            self.l1 = L.Linear(None, n_units)  # n_in -> n_units\n            self.l2 = L.Linear(None, n_units)  # n_units -> n_units\n            self.l3 = L.Linear(None, n_out)  # n_units -> n_out\n\n    @within_name_scope('MLP')\n    def __call__(self, x):\n        with name_scope('linear1', self.l1.params()):\n            h1 = F.relu(self.l1(x))\n        with name_scope('linear2', self.l2.params()):\n            h2 = F.relu(self.l2(h1))\n        with name_scope('linear3', self.l3.params()):\n            o = self.l3(h2)\n        return o\n```\n\nHow to save the logs using this model is shown below.\n`add_all_variable_images` is the function that saves the Variable's data in the model that matches the pattern as an images.\n`add_all_parameter_histograms` is the function that save histograms of the Parameter's data in the model that match the pattern.\n\n```python\nfrom datetime import datetime\nfrom tb_chainer import SummaryWriter\n\nmodel = L.Classifier(MLP(1000, 10))\n\nres = model(chainer.Variable(np.random.rand(1, 784).astype(np.float32)),\n            chainer.Variable(np.random.rand(1).astype(np.int32)))\n\nwriter = SummaryWriter('runs/'+datetime.now().strftime('%B%d  %H:%M:%S'))\nwriter.add_graph([res])\nwriter.add_all_variable_images([res], pattern='.*MLP.*')\nwriter.add_all_parameter_histograms([res], pattern='.*MLP.*')\n\nwriter.close()\n```\n\n## Reference\n\n* [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch)\n* [tensorboard_logger](https://github.com/TeamHG-Memex/tensorboard_logger)\n* [tfchain](https://github.com/mitmul/tfchain)",
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