theanets.layers.feedforward.Classifier

class theanets.layers.feedforward.Classifier(**kwargs)

A classifier layer performs a softmax over a linear input transform.

Classifier layers are typically the “output” layer of a classifier network.

This layer type really only wraps the output activation of a standard Feedforward layer.

Notes

The classifier layer is just a vanilla Feedforward layer that uses a 'softmax' output activation.

__init__(**kwargs)

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

Methods

__init__(**kwargs)	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 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.