theanets.layers.recurrent.MRNN¶

class
theanets.layers.recurrent.
MRNN
(factors=None, **kwargs)[source]¶ A recurrent network layer with multiplicative dynamics.
Notes
The formulation of MRNN implemented here uses a factored dynamics matrix. To understand the motivation for a factored dynamics, imagine for a moment a vanilla recurrent layer with one binary input, whose hidden dynamics depend on the input, so that \(W_{hh}^0\) is used if the input is 0, and \(W_{hh}^1\) is used if the input is 1:
\[h_t = \sigma(h_{t1} W_{hh}^{x_t} + x_t W_{xh} + b)\]This generalizes to the idea that there might be an entire collection of \(W_{hh}^i\) matrices that govern the hidden dynamics of the network, one for each \(0 \le i < N\). But in the general case, it would be prohibitively expensive to store this weight tensor; in addition, there are probably many shared hidden dynamics that one might want to learn across all of these runtime “modes.”
The MRNN solves this problem by factoring the weight tensor idea into two 2–dimensional arrays. The hidden state is mapped to and from “factor space” by \(W_{hf}\) and \(W_{fh}\), respectively, and the latent factors are modulated by the input using \(W_{xf}\).
The overall hidden activation for the MRNN model, then, looks like:
\[h_t = \sigma((x_t W_{xf} \odot h_{t1} W_{hf}) W_{fh} + x_t W_{xh} + b)\]where \(odot\) represents the elementwise product of two vectors.
Parameters
b
— vector of bias values for each hidden unitxf
— matrix connecting inputs to factorsxh
— matrix connecting inputs to hiddenshf
— matrix connecting hiddens to factorsfh
— matrix connecting factors to hiddens
Outputs
out
— the postactivation state of the layerpre
— the preactivation state of the layerfactors
— the activations of the latent factors
References
[Sut11] I. Sutskever, J. Martens, & G. E. Hinton. (ICML 2011) “Generating text with recurrent neural networks.” http://www.icml2011.org/papers/524_icmlpaper.pdf 
__init__
(factors=None, **kwargs)[source]¶ x.__init__(…) initializes x; see help(type(x)) for signature
Methods
__init__
([factors])x.__init__(…) initializes x; see help(type(x)) for signature add_bias
(name, size[, mean, std])Helper method to create a new bias vector. add_weights
(name, nin, nout[, mean, std, …])Helper method to create a new weight matrix. bind
(graph[, reset, initialize])Bind this layer into a computation graph. connect
(inputs)Create Theano variables representing the outputs of this layer. find
(key)Get a shared variable for a parameter by name. full_name
(name)Return a fullyscoped name for the given layer output. log
()Log some information about this layer. log_params
()Log information about this layer’s parameters. resolve_inputs
(layers)Resolve the names of inputs for this layer into shape tuples. resolve_outputs
()Resolve the names of outputs for this layer into shape tuples. setup
()Set up the parameters and initial values for this layer. to_spec
()Create a specification dictionary for this layer. transform
(inputs)Transform the inputs for this layer into an output for the layer. Attributes
input_name
Name of layer input (for layers with one input). input_shape
Shape of layer input (for layers with one input). input_size
Size of layer input (for layers with one input). output_name
Full name of the default output for this layer. output_shape
Shape of default output from this layer. output_size
Number of “neurons” in this layer’s default output. params
A list of all parameters in this layer. 
to_spec
()[source]¶ Create a specification dictionary for this layer.
Returns:  spec : dict
A dictionary specifying the configuration of this layer.

transform
(inputs)[source]¶ Transform the inputs for this layer into an output for the layer.
Parameters:  inputs : dict of Theano expressions
Symbolic inputs to this layer, given as a dictionary mapping string names to Theano expressions. See
Layer.connect()
.
Returns:  output : Theano expression
The output for this layer is the same as the input.
 updates : list
An empty updates list.