theanets.layers.convolution.Conv1

class theanets.layers.convolution.Conv1(filter_size, stride=1, border_mode='valid', **kwargs)

1-dimensional convolutions run over one data axis.

One-dimensional convolution layers can only be included in theanets models that use recurrent inputs and outputs, i.e., theanets.recurrent.Autoencoder, theanets.recurrent.Predictor, theanets.recurrent.Classifier, or theanets.recurrent.Regressor. The convolution will always be applied over the “time” dimension (axis 1).

Parameters:

filter_size : int

Length of the convolution filters for this layer.

stride : int, optional

Apply convolutions with this stride; i.e., skip this many samples between convolutions. Defaults to 1, i.e., no skipping.

border_mode : str, optional

Compute convolutions with this border mode. Defaults to ‘valid’.

__init__(filter_size, stride=1, border_mode='valid', **kwargs)

Methods

__init__(filter_size[, stride, border_mode])
add_bias(name, size[, mean, std])	Helper method to create a new bias vector.
add_conv_weights(name[, mean, std, sparsity])	Add a convolutional weight array to this layer’s parameters.
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.

setup()

Set up the parameters and initial values for 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

A sequence of updates to apply inside a Theano function.