theanets.layers.base.Product

class theanets.layers.base.Product(size, inputs, name=None, activation='relu', **kwargs)

Multiply several inputs together elementwise.

Notes

This layer performs an elementwise multiplication of multiple inputs; all inputs must be the same shape.

Outputs

• out — elementwise product of its inputs

__init__(size, inputs, name=None, activation='relu', **kwargs)

Methods

__init__(size, inputs[, name, activation])
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.
connect(inputs)	Create Theano variables representing the outputs of this layer.
find(key)	Get a shared variable for a parameter by name.
log()	Log some information about this layer.
output_name([name])	Return a fully-scoped name for the given layer output.
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.

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 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 “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

An empty sequence of updates.