# theanets.layers.recurrent.Bidirectional¶

class theanets.layers.recurrent.Bidirectional(worker='rnn', **kwargs)[source]

A bidirectional recurrent layer runs worker models forward and backward.

Parameters: worker : str, optional This string specifies the type of worker layer to use for the forward and backward processing. This parameter defaults to ‘rnn’ (i.e., vanilla recurrent network layer), but can be given as any string that specifies a recurrent layer type.

Notes

The size of this layer is split in half, with each half allocated to a “worker” layer that processes data in one direction in time. The outputs of the forward and backward passes are concatenated into the overall output for the layer.

For an example specification of a bidirectional recurrent network, see [Gra13b].

References

 [Gra13b] (1, 2) A. Graves, N. Jaitly, & A. Mohamed. (2013) “Hybrid Speech Recognition with Deep Bidirectional LSTM.” http://www.cs.toronto.edu/~graves/asru_2013.pdf
Attributes: worker : str The form of the underlying worker networks. forward : theanets.layers.base.Layer The layer that processes input data forwards in time. backward : theanets.layers.base.Layer The layer that processes input data backwards in time.
__init__(worker='rnn', **kwargs)[source]

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

Methods

 __init__([worker]) 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(*args, **kwargs) 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
bind(*args, **kwargs)[source]

Bind this layer into a computation graph.

This method is a wrapper for performing common initialization tasks. It calls resolve(), setup(), and log().

Parameters: graph : Network A computation network in which this layer is to be bound. reset : bool, optional If True (the default), reset the resolved layers for this layer. initialize : bool, optional If True (the default), initialize the parameters for this layer by calling setup(). theanets.util.ConfigurationError : If an input cannot be resolved.
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(). output : Theano expression The output for this layer is the same as the input. updates : list An empty updates list.