theanets.layers.recurrent.MRNN¶
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class
theanets.layers.recurrent.
MRNN
(factors=None, **kwargs)¶ Define a recurrent network layer using multiplicative dynamics.
The formulation of MRNN implemented here uses a factored dynamics matrix as described in Sutskever, Martens & Hinton, ICML 2011, “Generating text with recurrent neural networks.” This paper is available online at http://www.icml-2011.org/papers/524_icmlpaper.pdf.
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__init__
(factors=None, **kwargs)¶
Methods
__init__
([factors])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. 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_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.
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to_spec
()¶ Create a specification dictionary for this layer.
Returns: spec : dict
A dictionary specifying the configuration of this layer.
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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 : dict of theano expressions
A map from string output names to Theano expressions for the outputs from this layer. This layer type generates a “factors” output that gives the activation of the hidden weight factors given the input data (but not incorporating influence from the hidden states), a “pre” output that gives the unit activity before applying the layer’s activation function, and an “out” output that gives the post-activation output.
updates : list of update pairs
A sequence of updates to apply inside a theano function.
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