Given an Activation's name returns appropriate Activation layer.

Given a number of rows and columns returns a Dense layer.

Without the weights and biases of dense/4 this Dense layer will be have no neurons. Upon Layer.init_layer/2 the Dense layer will be initialized with random neurons; Neurons with random weights and biases.

Given a number of rows, columns, some weights, and some biases returns a built Dense layer.

Given a frequency (between 0.0 and 1.0) returns a LayerConfig for a Dropout. The Dropout layer randomly, at a given frequency, returns 0.0 for an input regardless of that input's value.

Given an initialized Learner learner and some data returns a prediction.

The learner should be initialized with Learner.init_learner before being used with the predict/2 function.

Also, it's a good idea to train the learner (using train/3 or train/4) before using it to make predicitons. Chances are slim that an untrained Learner is capable of making accurate predictions.

Given a list of layers returns a LayerConfig for a Sequence.

Trains an Annex.Learner given learner, data, labels, and options.

The learner should be initialized Learner.init_learner/2 before being trained.

Returns the trained learner along with some measure of loss or performance.