funktional

A minimalistic toolkit for functionally composable neural network layers with Theano.

Installation

If you'll be making changes to the code it's best to install in the development mode, so all the changes are used by the Python interpreter. From the directory of the repo run the following command:

> python setup.py develop --user

Rationale

Conceptually, neural network layers are functions on tensors. In funktional, layers are implemented as callable Python objects which means that they can be easily mixed and matched with regular Python functions operating on Theano tensors. Function-like layers are also easy to compose into more complex networks using function application and function composition idioms familiar from general purpose programming.

For example, suppose we need to implement a recurrent encoder-decoder network. As the encoder we use a Gated Recurrent Unit (GRU) layer which takes one argument (the input sequence) and returns a sequence of hidden states. The decoder is another GRU which takes two arguments (an initial state, and a sequence of outputs), and returns a sequence of hidden states. From these hidden states we then predict the next output element:

                y1  y2  y3  y.
                ^   ^   ^   ^
                |   |   |   |            
h0->h1->h2->h3->g1->g2->g3->g4
    ^   ^   ^   ^   ^   ^   ^
    |   |   |   |   |   |   |
    x1  x2  x.  y0  y1  y2  y3

With funktional, you would create the layers and then compose them using function application like so:

def last(x):
    """Returns the last time step of all sequences in x."""
    return x.dimshuffle((1,0,2))[-1]

class EncoderDecoder(Layer):
    def __init__(self, size_in, size, size_out):
        self.Encode = GRUH0(size_in=size_in, size=size)
        self.Decode = GRU(size_in=size_out, size=size)
        self.Output = Dense(size_in=size, size=size_out)
        self.params = self.Encode.params + self.Decode.params + self.Output.params

    def __call__(self, inp, out_prev):
        return self.Output(self.Decode(last(self.Encode(inp)), out_prev))

Note that in the definition of __call__ we specify the network connectivity using function application syntax, and mix Layer objects together with regular Python functions such as last. The EncoderDecoder we defined can in turn be composed with other layers or functions:

Encdec = EncoderDecoder(size_in, size, size_out)
output = softmax3d(Encdec(input, output_prev))

See layer.py for more examples of layer compositions.

See reimaginet for examples of models defined using funktional.

gchrupala/funktional

funktional

Installation

Rationale