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tensorgraph 4.2.1

A high level tensorflow library for building deep learning models

# 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 what TensorGraph is designed for. It's a very simple
and easy to use framework and allows you to build any imaginable models.

### Target Audience
TensorGraph is targeted more at intermediate to advance users who feel keras or
other packages is having too much restrictions on model building, and someone who
don't want to rewrite the standard layers in tensorflow constantly, and someone
who want to use some higher level layers to build models quickly without the
restrictions of most other deep learning package.

### Install

First you need to install [tensorflow](

To install tensorgraph simply do via pip
sudo pip install tensorgraph
or for bleeding edge version do
sudo pip install --upgrade git+
or simply clone and add to `PYTHONPATH`.
git clone
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 []( 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 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/ of building a tensor graph.

### Graph Example

<img src="draw/graph.png" height="250">

First define the `StartNode` for putting the input placeholder
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`
h1 = HiddenNode(prev=[s1, s2],
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],
layers=[Linear(y2_dim, y1_dim), RELU()])
Then define the `EndNode`. `EndNode` is used to back-trace the graph to connect
the nodes together.
e1 = EndNode(prev=[h3])
e2 = EndNode(prev=[h2])
Finally build the graph by putting `StartNodes` and `EndNodes` into `Graph`
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`
o1, o2 = graph.train_fprop()
Finally build an optimizer to optimize the objective function
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/
whereby the lower hierachical softmax layer can be conditioned on all the upper
hierachical softmax layers.

<img src="draw/hsoftmax.png" height="250">

## 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">

## 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)
File Type Py Version Uploaded on Size
tensorgraph-4.2.1.tar.gz (md5) Source 2018-02-14 52KB