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)

Methods

__init__(**kwargs)
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.