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.

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’.

Notes

One-dimensional convolution layers are typically used 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 be applied over the “time” dimension (axis 1).

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

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

Methods

__init__(filter_size[, stride, border_mode])	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_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.
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 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	A list of all parameters in this layer.

resolve_outputs()

Resolve the names of outputs for this layer into shape tuples.

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:

output : Theano expression

The output for this layer is the same as the input.

updates : list

An empty updates list.