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.


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


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.