Metadata-Version: 1.1
Name: tensorgraph
Version: 2.5.1
Summary: A tensorflow library for building all kinds of 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/2.5.1
Description: # TensorGraph
        TensorGraph is a framework for building any imaginable models based on TensorFlow.
        
        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 why TensorGraph is born. It's a very simple and easy
        to use framework, but it allows you to build all kinds of imaginable models.
        
        -----
        ### 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.
        
        -----
        ### 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 all kinds of architectures without worrying about how to design
        the forward propagation. We let the graph engine sort out all the forward propagations
        for you, so that you just focus on designing the architecture. The graph always
        starts with `StartNode` and ends with `EndNode`. 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=[start2],
                        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
