theanets.layers.recurrent.LRRNN

class theanets.layers.recurrent.LRRNN(**kwargs)

A learned-rate RNN defines per-hidden-unit accumulation rates.

In a normal RNN, a hidden unit is updated completely at each time step, \(h_t = f(x_t, h_{t-1})\). With an explicit update rate, the state of a hidden unit is computed as a mixture of the new and old values, h_t = alpha_t h_{t-1} + (1 - alpha_t) f(x_t, h_{t-1}).

Rates might be defined in a number of ways, spanning a continuum between vanilla RNNs (i.e., all rate parameters are fixed at 1), fixed but non-uniform rates for each hidden unit [Ben12], parametric rates that are dependent only on the input (i.e., the ARRNN), all the way to parametric rates that are computed as a function of the inputs and the hidden state at each time step (i.e., something more like the gated recurrent unit).

This class represents rates as a single learnable vector of parameters. This representation uses the fewest number of parameters for learnable rates, but the simplicity of the model comes at the cost of effectively fixing the rate for each unit as a constant value across time.

References

[Ben12]

(1, 2) Y. Bengio, N. Boulanger-Lewandowski, R. Pascanu. (2012) “Advances in Optimizing Recurrent Networks.” http://arxiv.org/abs/1212.0901

__init__(**kwargs)

Methods

__init__(**kwargs)
add_weights(name, nin, nout[, mean, std, ...])	Helper method to create a new weight matrix.
initial_state(name, batch_size)	Return an array of suitable for representing initial state.
setup()	Set up the parameters and initial values for this layer.
transform(inputs)	Transform the inputs for this layer into an output for the layer.

Attributes

input_size	For networks with one input, get the input size.
num_params	Total number of learnable parameters in this layer.
params	A list of all parameters in this layer.

setup()

Set up the parameters and initial values for this layer.

transform(inputs)

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 base.Layer.connect().

Returns:

outputs : theano expression

A map from string output names to Theano expressions for the outputs from this layer. This layer type generates a “pre” output that gives the unit activity before applying the layer’s activation function, a “hid” output that gives the rate-independent, post-activation hidden state, a “rate” output that gives the rate value for each hidden unit, and an “out” output that gives the hidden output.

updates : list of update pairs

A sequence of updates to apply inside a theano function.