Metadata-Version: 2.1
Name: tensorboard-chainer
Version: 0.4.0
Summary: Log TensorBoard events with chainer
Home-page: https://github.com/neka-nat/tensorboard-chainer
Author: nake nat
Author-email: nakanat.stock@gmail.com
License: MIT license
Description: [![Build Status](https://travis-ci.org/neka-nat/tensorboard-chainer.svg?branch=master)](https://travis-ci.org/neka-nat/tensorboard-chainer)
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        # tensorboard-chainer
        
        Write tensorboard events with simple command.
        including scalar, image, histogram, audio, text, graph and embedding.
        
        This is based on [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch).
        
        ## Usage
        
        Install tensorflow.
        
        ```
        pip install tensorflow
        ```
        
        Execute demo.py and tensorboard.
        Access "localhost:6006" in your browser.
        
        ```
        cd examples
        python demo.py
        tensorboard --logdir runs
        ```
        
        ## Scalar example
        
        ![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/scalar.png)
        
        ## Histogram example
        
        ![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/histogram.png)
        
        ## Graph example
        
        ![graph](https://raw.githubusercontent.com/neka-nat/tensorboard-chainer/master/screenshots/graph.gif)
        
        ## Name scope
        
        Like tensorflow, nodes in the graph can be grouped together in the namespace to make it easy to see.
        
        ```python
        import chainer
        import chainer.functions as F
        import chainer.links as L
        from tb_chainer import name_scope, within_name_scope
        
        class MLP(chainer.Chain):
            def __init__(self, n_units, n_out):
                super(MLP, self).__init__()
                with self.init_scope():
                    self.l1 = L.Linear(None, n_units)  # n_in -> n_units
                    self.l2 = L.Linear(None, n_units)  # n_units -> n_units
                    self.l3 = L.Linear(None, n_out)  # n_units -> n_out
        
            @within_name_scope('MLP')
            def __call__(self, x):
                with name_scope('linear1', self.l1.params()):
                    h1 = F.relu(self.l1(x))
                with name_scope('linear2', self.l2.params()):
                    h2 = F.relu(self.l2(h1))
                with name_scope('linear3', self.l3.params()):
                    o = self.l3(h2)
                return o
        ```
        
        How to save the logs using this model is shown below.
        `add_all_variable_images` is the function that saves the Variable's data in the model that matches the pattern as an images.
        `add_all_parameter_histograms` is the function that save histograms of the Parameter's data in the model that match the pattern.
        
        ```python
        from datetime import datetime
        from tb_chainer import SummaryWriter
        
        model = L.Classifier(MLP(1000, 10))
        
        res = model(chainer.Variable(np.random.rand(1, 784).astype(np.float32)),
                    chainer.Variable(np.random.rand(1).astype(np.int32)))
        
        writer = SummaryWriter('runs/'+datetime.now().strftime('%B%d  %H:%M:%S'))
        writer.add_graph([res])
        writer.add_all_variable_images([res], pattern='.*MLP.*')
        writer.add_all_parameter_histograms([res], pattern='.*MLP.*')
        
        writer.close()
        ```
        
        ## Reference
        
        * [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch)
        * [tensorboard_logger](https://github.com/TeamHG-Memex/tensorboard_logger)
        * [tfchain](https://github.com/mitmul/tfchain)
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