theanets.layers.recurrent.GRU¶

class
theanets.layers.recurrent.
GRU
(h_0=None, **kwargs)[source]¶ Gated Recurrent Unit layer.
Notes
The Gated Recurrent Unit lies somewhere between the
LSTM
and theRRNN
in complexity. Like theRRNN
, its hidden state is updated at each time step to be a linear interpolation between the previous hidden state, \(h_{t1}\), and the “target” hidden state, \(h_t\). The interpolation is modulated by an “update gate” that serves the same purpose as the rate gates in theRRNN
. Like theLSTM
, the target hidden state can also be reset using a dedicated gate. All gates in this layer are activated based on the current input as well as the previous hidden state.The update equations in this layer are largely those given by [Chu14], page 4, except for the addition of a hidden bias term. They are:
\[\begin{split}\begin{eqnarray} r_t &=& \sigma(x_t W_{xr} + h_{t1} W_{hr} + b_r) \\ z_t &=& \sigma(x_t W_{xz} + h_{t1} W_{hz} + b_z) \\ \hat{h}_t &=& g\left(x_t W_{xh} + (r_t \odot h_{t1}) W_{hh} + b_h\right) \\ h_t &=& (1  z_t) \odot h_{t1} + z_t \odot \hat{h}_t. \end{eqnarray}\end{split}\]Here, \(g(\cdot)\) is the activation function for the layer, and \(\sigma(\cdot)\) is the logistic sigmoid, which ensures that the two gates in the layer are limited to the open interval (0, 1). The symbol \(\odot\) indicates elementwise multiplication.
Parameters
hh
— matrix connecting hiddens to hiddenshr
— matrix connecting hiddens to reset gateshz
— matrix connecting hiddens to rate gatesw
— matrix connecting inputs to [hidden, reset, rate] unitsb
— vector of bias values for [hidden, reset, rate] units
Outputs
out
— the postactivation state of the layerpre
— the preactivation state of the layerhid
— the preratemixing hidden staterate
— the rate values
References
[Chu14] (1, 2) J. Chung, C. Gulcehre, K. H. Cho, & Y. Bengio (2014), “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling” http://arxiv.org/abs/1412.3555v1 
__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 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. 
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.