TensorGraphX 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 TensorGraphX 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.
TensorGraphX 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 tensorgraphx simply do via pip
sudo pip install tensorgraphxor for bleeding edge version do
sudo pip install --upgrade git+https://github.com/hycis/TensorGraphX.git@masteror simply clone and add to PYTHONPATH.
git clone https://github.com/hycis/TensorGraphX.git
export PYTHONPATH=/path/to/TensorGraphX:$PYTHONPATHin order for the install to persist via export PYTHONPATH. Add PYTHONPATH=/path/to/TensorGraphX:$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 TensorGraphX 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 TensorGraphX 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 tensorgraphx.layers import DenseBlock, BaseModel, Flatten, Linear, Softmax
import tensorgraphx as tg
class ModelA(BaseModel):
@BaseModel.init_name_scope
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
class ModelB(BaseModel):
@BaseModel.init_name_scope
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 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 TensorGraphX
- VGG16 code and VGG19 code - Very Deep Convolutional Networks for Large-Scale Image Recognition
- DenseNet code - Densely Connected Convolutional Networks
- ResNet code - Deep Residual Learning for Image Recognition
- Unet code - U-Net: Convolutional Networks for Biomedical Image Segmentation
To use tensorgraphx 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
hvd.init()
# tensorgraphx 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 tensorgraphx on horovod
In TensorGraphX, we defined three types of nodes
- StartNode : for inputs to the graph
- HiddenNode : for putting sequential layers inside
- 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.
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],
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.
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)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)Below is an example on transfer learning with bi-modality inputs and merge at the middle layer with shared representation, in fact, TensorGraphX 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)