# theanets.layers.recurrent.RNN¶

class theanets.layers.recurrent.RNN(h_0=None, **kwargs)[source]

Standard recurrent network layer.

Notes

There are many different styles of recurrent network layers, but the one implemented here is known as an Elman layer or an SRN (Simple Recurrent Network) – the output from the layer at the previous time step is incorporated into the input of the layer at the current time step.

$h_t = \sigma(x_t W_{xh} + h_{t-1} W_{hh} + b)$

Here, $$\sigma(\cdot)$$ is the activation function of the layer, and the subscript represents the time step of the data being processed. The state of the hidden layer at time $$t$$ depends on the input at time $$t$$ and the state of the hidden layer at time $$t-1$$.

Parameters

• b — bias
• xh — matrix connecting inputs to hiddens
• hh — matrix connecting hiddens to hiddens

Outputs

• out — the post-activation state of the layer
• pre — the pre-activation state of the layer
__init__(h_0=None, **kwargs)

x.__init__(…) initializes x; see help(type(x)) for signature

Methods

 __init__([h_0]) 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 fully-scoped 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.
setup()[source]

Set up the parameters and initial values for 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(). output : Theano expression The output for this layer is the same as the input. updates : list An empty updates list.