A high level tensorflow library for building deep learning models

deep-learning, tensorflow
pip install tensorgraph==5.0.1


Build Status

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.


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+https://github.com/hycis/TensorGraph.git@master

or simply clone and add to PYTHONPATH.

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 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.

from tensorgraph.layers import DenseBlock, BaseModel, Flatten, Linear, Softmax
import tensorgraph as tg

class ModelA(BaseModel):
    def __init__(self):
        layers = []
        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

class ModelB(BaseModel):
    def __int__(self):
        layers = []
        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 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.

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 and VGG19 code - Very Deep Convolutional Networks for Large-Scale Image Recognition
  2. DenseNet code - Densely Connected Convolutional Networks
  3. ResNet code - Deep Residual Learning for Image Recognition
  4. Unet code - U-Net: Convolutional Networks for Biomedical Image Segmentation

TensorGraph on Multiple GPUS

To use tensorgraph on multiple gpus, you can easily integrate it with horovod.

import horovod.tensorflow as hvd
from tensorflow.python.framework import ops
import tensorflow as tf

# 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(),
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:

    # 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

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 of building a tensor graph.

Graph Example

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 whereby the lower hierachical softmax layer can be conditioned on all the upper hierachical softmax layers.

## 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.

## 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)