Metadata-Version: 1.1
Name: tensorgraph
Version: 4.4.11
Summary: A high level tensorflow library for building deep learning models
Home-page: https://github.com/hycis/TensorGraph
Author: Wu Zhen Zhou
Author-email: hyciswu@gmail.com
License: Apache 2.0, see LICENCE
Download-URL: https://github.com/hycis/TensorGraph/tarball/4.4.11
Description-Content-Type: UNKNOWN
Description: [![Build Status](https://travis-ci.org/hycis/TensorGraph.svg?branch=master)](https://travis-ci.org/hycis/TensorGraph)
        
        # TensorGraph - Simplicity is Beauty
        TensorGraph is a simple, lean, and clean framework on TensorFlow for building any imaginable models.
        
        As deep learning becomes more and more common and the architectures becoming more
        and more complicated, it seems that we need some easy to use framework to quickly
        build these models and that's what TensorGraph is designed for. It's a very simple
        framework that adds a very thin layer above tensorflow. It is for more advanced
        users who want to have more control and flexibility over his model building and
        who wants efficiency at the same time.
        
        -----
        ### Target Audience
        TensorGraph is targeted more at intermediate to advance users who feel keras or
        other packages is having too much restrictions and too much black box on model
        building, and someone who don't want to rewrite the standard layers in tensorflow
        constantly. Also for enterprise users who want to share deep learning models
        easily between teams.
        
        -----
        ### Install
        
        First you need to install [tensorflow](https://www.tensorflow.org/versions/r0.9/get_started/os_setup.html)
        
        To install tensorgraph simply do via pip
        ```bash
        sudo pip install tensorgraph
        ```
        or for bleeding edge version do
        ```bash
        sudo pip install --upgrade git+https://github.com/hycis/TensorGraph.git@master
        ```
        or simply clone and add to `PYTHONPATH`.
        ```bash
        git clone https://github.com/hycis/TensorGraph.git
        export PYTHONPATH=/path/to/TensorGraph:$PYTHONPATH
        ```
        in order for the install to persist via export `PYTHONPATH`. Add `PYTHONPATH=/path/to/TensorGraph:$PYTHONPATH` to your `.bashrc` for linux or
        `.bash_profile` for mac. While this method works, you will have to ensure that
        all the dependencies in [setup.py](setup.py) are installed.
        
        -----
        ### Everything in TensorGraph is about Layers
        Everything in TensorGraph is about layers. A model such as VGG or Resnet can be a layer. An identity block from Resnet or a dense block from Densenet can be a layer as well. Building models in TensorGraph is same as building a toy with lego. For example you can create a new model (layer) by subclass the `BaseModel` layer and use `DenseBlock` layer inside your `ModelA` layer.
        
        ```python
        from tensorgraph.layers import DenseBlock, BaseModel, Flatten, Linear, Softmax
        import tensorgraph as tg
        
        class ModelA(BaseModel):
            def __init__(self):
                layers = []
                layers.append(DenseBlock())
                layers.append(Flatten())
                layers.append(Linear())
                layers.append(Softmax())
                self.startnode = tg.StartNode(input_vars=[None])
                hn = tg.HiddenNode(prev=[self.startnode], layers=layers)
                self.endnode = tg.EndNode(prev=[hn])
        ```
        
        if someone wants to use your `ModelA` in his `ModelB`, he can easily do this
        ```python
        class ModelB(BaseModel):
            def __int__(self):
                layers = []
                layers.append(ModelA())
                layers.append(Linear())
                layers.append(Softmax())
                self.startnode = tg.StartNode(input_vars=[None])
                hn = tg.HiddenNode(prev=[self.startnode], layers=layers)
                self.endnode = tg.EndNode(prev=[hn])
        ```
        
        creating a layer only created all the `Variables`. To connect the `Variables` into a graph, you can do a `_train_fprop(X)` or `_test_fprop(X)` to create the tensorflow graph. By abstracting `Variable` creation away from linking the `Variable` nodes into graph prevent the problem of certain tensorflow layers that always reinitialise its weights when it's called, example the [`tf.nn.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization) layer. Also having a separate channel for training and testing is to cater to layers with different training and testing behaviours such as batchnorm and dropout.
        
        ```python
        modelb = ModelB()
        X_ph = tf.placeholder()
        y_train = modelb._train_fprop(X_ph)
        y_test = modelb._test_fprop(X_ph)
        ```
        
        checkout some well known models in TensorGraph
        1. [VGG16 code](tensorgraph/layers/backbones.py#L37) and [VGG19 code](tensorgraph/layers/backbones.py#L125) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
        2. [DenseNet code](tensorgraph/layers/backbones.py#L477) - [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
        3. [ResNet code](tensorgraph/layers/backbones.py#L225) - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
        4. [Unet code](tensorgraph/layers/backbones.py#L531) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
        
        -----
        ### TensorGraph on Multiple GPUS
        To use tensorgraph on multiple gpus, you can easily integrate it with [horovod](https://github.com/uber/horovod).
        
        ```python
        import horovod.tensorflow as hvd
        from tensorflow.python.framework import ops
        import tensorflow as tf
        hvd.init()
        
        # tensorgraph model derived previously
        modelb = ModelB()
        X_ph = tf.placeholder()
        y_ph = tf.placeholder()
        y_train = modelb._train_fprop(X_ph)
        y_test = modelb._test_fprop(X_ph)
        
        train_cost = mse(y_train, y_ph)
        test_cost = mse(y_test, y_ph)
        
        opt = tf.train.RMSPropOptimizer(0.001)
        opt = hvd.DistributedOptimizer(opt)
        
        # required for BatchNormalization layer
        update_ops = ops.get_collection(ops.GraphKeys.UPDATE_OPS)
        with ops.control_dependencies(update_ops):
            train_op = opt.minimize(train_cost)
        
        init_op = tf.group(tf.global_variables_initializer(),
                           tf.local_variables_initializer())
        bcast = hvd.broadcast_global_variables(0)
        
        # Pin GPU to be used to process local rank (one GPU per process)
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.visible_device_list = str(hvd.local_rank())
        
        with tf.Session(graph=graph, config=config) as sess:
            sess.run(init_op)
            bcast.run()
        
            # training model
            for epoch in range(100):
                for X,y in train_data:
                    _, loss_train = sess.run([train_op, train_cost], feed_dict={X_ph:X, y_ph:y})
        ```
        
        for a full example on [tensorgraph on horovod](./examples/multi_gpus_horovod.py)
        
        -----
        ### How TensorGraph Works?
        In TensorGraph, we defined three types of nodes
        
        1. StartNode : for inputs to the graph
        2. HiddenNode : for putting sequential layers inside
        3. EndNode : for getting outputs from the model
        
        We put all the sequential layers into a `HiddenNode`, and connect the hidden nodes
        together to build the architecture that you want. The graph always
        starts with `StartNode` and ends with `EndNode`. The `StartNode` is where you place
        your starting point, it can be a `placeholder`, a symbolic output from another graph,
        or data output from `tfrecords`. `EndNode` is where you want to get an output from
        the graph, where the output can be used to calculate loss or simply just a peek at the
        outputs at that particular layer. Below shows an
        [example](examples/example.py) of building a tensor graph.
        
        -----
        ### Graph Example
        
        <img src="draw/graph.png" height="250">
        
        First define the `StartNode` for putting the input placeholder
        ```python
        y1_dim = 50
        y2_dim = 100
        batchsize = 32
        learning_rate = 0.01
        
        y1 = tf.placeholder('float32', [None, y1_dim])
        y2 = tf.placeholder('float32', [None, y2_dim])
        s1 = StartNode(input_vars=[y1])
        s2 = StartNode(input_vars=[y2])
        ```
        Then define the `HiddenNode` for putting the sequential layers in each `HiddenNode`
        ```python
        h1 = HiddenNode(prev=[s1, s2],
                        input_merge_mode=Concat(),
                        layers=[Linear(y1_dim+y2_dim, y2_dim), RELU()])
        h2 = HiddenNode(prev=[s2],
                        layers=[Linear(y2_dim, y2_dim), RELU()])
        h3 = HiddenNode(prev=[h1, h2],
                        input_merge_mode=Sum(),
                        layers=[Linear(y2_dim, y1_dim), RELU()])
        ```
        Then define the `EndNode`. `EndNode` is used to back-trace the graph to connect
        the nodes together.
        ```python
        e1 = EndNode(prev=[h3])
        e2 = EndNode(prev=[h2])
        ```
        Finally build the graph by putting `StartNodes` and `EndNodes` into `Graph`
        ```python
        graph = Graph(start=[s1, s2], end=[e1, e2])
        ```
        Run train forward propagation to get symbolic output from train mode. The number
        of outputs from `graph.train_fprop` is the same as the number of `EndNodes` put
        into `Graph`
        ```python
        o1, o2 = graph.train_fprop()
        ```
        Finally build an optimizer to optimize the objective function
        ```python
        o1_mse = tf.reduce_mean((y1 - o1)**2)
        o2_mse = tf.reduce_mean((y2 - o2)**2)
        mse = o1_mse + o2_mse
        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(mse)
        ```
        -----
        ### Hierachical Softmax Example
        Below is another example for building a more powerful [hierachical softmax](examples/hierachical_softmax.py)
        whereby the lower hierachical softmax layer can be conditioned on all the upper
        hierachical softmax layers.
        
        <img src="draw/hsoftmax.png" height="250">
        
        ```python
        ## params
        x_dim = 50
        component_dim = 100
        batchsize = 32
        learning_rate = 0.01
        
        
        x_ph = tf.placeholder('float32', [None, x_dim])
        # the three hierachical level
        y1_ph = tf.placeholder('float32', [None, component_dim])
        y2_ph = tf.placeholder('float32', [None, component_dim])
        y3_ph = tf.placeholder('float32', [None, component_dim])
        
        # define the graph model structure
        start = StartNode(input_vars=[x_ph])
        
        h1 = HiddenNode(prev=[start], layers=[Linear(x_dim, component_dim), Softmax()])
        h2 = HiddenNode(prev=[h1], layers=[Linear(component_dim, component_dim), Softmax()])
        h3 = HiddenNode(prev=[h2], layers=[Linear(component_dim, component_dim), Softmax()])
        
        
        e1 = EndNode(prev=[h1], input_merge_mode=Sum())
        e2 = EndNode(prev=[h1, h2], input_merge_mode=Sum())
        e3 = EndNode(prev=[h1, h2, h3], input_merge_mode=Sum())
        
        graph = Graph(start=[start], end=[e1, e2, e3])
        
        o1, o2, o3 = graph.train_fprop()
        
        o1_mse = tf.reduce_mean((y1_ph - o1)**2)
        o2_mse = tf.reduce_mean((y2_ph - o2)**2)
        o3_mse = tf.reduce_mean((y3_ph - o3)**2)
        mse = o1_mse + o2_mse + o3_mse
        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(mse)
        ```
        
        -----
        ### Transfer Learning Example
        Below is an example on transfer learning with bi-modality inputs and merge at
        the middle layer with shared representation, in fact, TensorGraph can be used
        to build any number of modalities for transfer learning.
        
        <img src="draw/transferlearn.png" height="250">
        
        ```python
        ## params
        x1_dim = 50
        x2_dim = 100
        shared_dim = 200
        y_dim = 100
        batchsize = 32
        learning_rate = 0.01
        
        
        x1_ph = tf.placeholder('float32', [None, x1_dim])
        x2_ph = tf.placeholder('float32', [None, x2_dim])
        y_ph = tf.placeholder('float32', [None, y_dim])
        
        # define the graph model structure
        s1 = StartNode(input_vars=[x1_ph])
        s2 = StartNode(input_vars=[x2_ph])
        
        h1 = HiddenNode(prev=[s1], layers=[Linear(x1_dim, shared_dim), RELU()])
        h2 = HiddenNode(prev=[s2], layers=[Linear(x2_dim, shared_dim), RELU()])
        h3 = HiddenNode(prev=[h1,h2], input_merge_mode=Sum(),
                        layers=[Linear(shared_dim, y_dim), Softmax()])
        
        e1 = EndNode(prev=[h3])
        
        graph = Graph(start=[s1, s2], end=[e1])
        o1, = graph.train_fprop()
        
        mse = tf.reduce_mean((y_ph - o1)**2)
        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(mse)
        ```
        
Platform: UNKNOWN
