Annex v0.2.1 Annex View Source
Annex is a library for composing and running deep artificial
Link to this section Summary
Functions
Given an Activation's name returns appropriate Activation
layer.
Given a number of rows
and columns
returns a Dense layer.
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.
Given a list of layers
returns a LayerConfig
for a Sequence
.
Trains an Annex.Learner
given learner
, data
, labels
, and options
.
Link to this section Functions
activation(name)
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activation(Annex.Layer.Activation.func_name()) ::
Annex.LayerConfig.t(Annex.Layer.Activation)
activation(Annex.Layer.Activation.func_name()) :: Annex.LayerConfig.t(Annex.Layer.Activation)
Given an Activation's name returns appropriate Activation
layer.
dense(rows, columns)
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dense(pos_integer(), pos_integer()) :: Annex.LayerConfig.t(Annex.Layer.Dense)
dense(pos_integer(), pos_integer()) :: Annex.LayerConfig.t(Annex.Layer.Dense)
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.
dense(rows, columns, weights, biases)
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dense(pos_integer(), pos_integer(), Annex.Data.data(), Annex.Data.data()) ::
Annex.LayerConfig.t(Annex.Layer.Dense)
dense(pos_integer(), pos_integer(), Annex.Data.data(), Annex.Data.data()) :: Annex.LayerConfig.t(Annex.Layer.Dense)
Given a number of rows
, columns
, some weights
,
and some biases
returns a built Dense
layer.
dropout(frequency)
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dropout(float()) :: Annex.LayerConfig.t(Annex.Layer.Dropout)
dropout(float()) :: Annex.LayerConfig.t(Annex.Layer.Dropout)
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.
predict(learner, data)
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predict(Annex.Learner.t(), Annex.Learner.data()) :: Annex.Learner.data()
predict(Annex.Learner.t(), Annex.Learner.data()) :: Annex.Learner.data()
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.
sequence(layers)
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sequence([Annex.LayerConfig.t(module())]) ::
Annex.LayerConfig.t(Annex.Layer.Sequence)
sequence([Annex.LayerConfig.t(module())]) :: Annex.LayerConfig.t(Annex.Layer.Sequence)
Given a list of layers
returns a LayerConfig
for a Sequence
.
train(learner, dataset, options \\ []) View Source
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.