TFComponents is a component structure for building complex structures in tensorflow. It currently is designed to work with tflearn.
The list of predefined components are as follow:
- Identity()
- Sequence(listOfComponentBlocks)
- Parallel(listOfComponentBlocks)
- Chain(size, componentBlock)
- Fractal(size, componentBlock)
- Residual(componentBlock)
These components also have several keyword arguments. They can take the property name for the name of the enclosing scope. It can also use a globalDroppath boolean, localDroppath boolean, and localDroppathProb tensor float between 0 and 1. The booleans can either be a python boolean or a potentially changing 0D boolean tensor.
In addition, the tflearn layers Conv2d and ShallowResidualBlock have been converted to component form (they no longer possess the number of blocks property)
All components possess a getitem method, like this:
outputTensor = RandomComponent(...some arguments...)[inputTensor]These components can be combined by passing some component definitions into others.
# Basic residual net
net = Chain(10, Residual(ShallowResidualBlock(16)))[net]
globalDroppath = tf.less(tf.random_uniform([]), 0.5)
localDroppath = tf.logical_not(globalDroppath)
localDroppathProb = 0.85 #keeps this fraction
#Fractalnet with droppath
net = Fractal(2, FractalBlock(filters), globalDroppath=globalDroppath, localDroppath=localDroppath, localDroppathProb=localDroppathProb)[net]
#Residual net with droppath
net = Chain(10, Residual(ShallowResidualBlock(16)), globalDroppath=globalDroppath, localDroppath=localDroppath, localDroppathProb=localDroppathProb)[net]To create a custom component, extend the TFComponent class. It must have an init method that sets self.opts to a dictionary (with options, ideally). The init method can also take ordered arguments which can just be saved directly to the object.
It should also define a method get(self, incoming, opts, inherit). Incoming is an incoming tensor, opts is the self.opts dictionary converted to a