Index

_

__init__() (theanets.activations.Activation method) (theanets.activations.LGrelu method) (theanets.activations.Maxout method) (theanets.activations.Prelu method) (theanets.feedforward.Autoencoder method) (theanets.feedforward.Classifier method) (theanets.feedforward.Regressor method) (theanets.graph.Network method) (theanets.layers.base.Concatenate method) (theanets.layers.base.Flatten method) (theanets.layers.base.Input method) (theanets.layers.base.Layer method) (theanets.layers.base.Product method) (theanets.layers.base.Reshape method) (theanets.layers.convolution.Conv1 method) (theanets.layers.feedforward.Classifier method) (theanets.layers.feedforward.Feedforward method) (theanets.layers.feedforward.Tied method) (theanets.layers.recurrent.Bidirectional method) (theanets.layers.recurrent.Clockwork method) (theanets.layers.recurrent.GRU method) (theanets.layers.recurrent.LSTM method) (theanets.layers.recurrent.MRNN method) (theanets.layers.recurrent.MUT1 method) (theanets.layers.recurrent.RNN method) (theanets.layers.recurrent.RRNN method) (theanets.layers.recurrent.SCRN method) (theanets.losses.CrossEntropy method) (theanets.losses.GaussianLogLikelihood method) (theanets.losses.Hinge method) (theanets.losses.KullbackLeiblerDivergence method) (theanets.losses.Loss method) (theanets.losses.MaximumMeanDiscrepancy method) (theanets.losses.MeanAbsoluteError method) (theanets.losses.MeanSquaredError method) (theanets.recurrent.Autoencoder method) (theanets.recurrent.Classifier method) (theanets.recurrent.Regressor method) (theanets.recurrent.Text method) (theanets.regularizers.BernoulliDropout method) (theanets.regularizers.Contractive method) (theanets.regularizers.GaussianNoise method) (theanets.regularizers.HiddenL1 method) (theanets.regularizers.Regularizer method) (theanets.regularizers.WeightL1 method) (theanets.regularizers.WeightL2 method) (theanets.trainer.DownhillTrainer method) (theanets.trainer.SampleTrainer method) (theanets.trainer.SupervisedPretrainer method) (theanets.trainer.UnsupervisedPretrainer method)

A

accuracy() (theanets.losses.CrossEntropy method) Activation (class in theanets.activations) add_bias() (theanets.layers.base.Layer method) add_layer() (theanets.graph.Network method)

add_loss() (theanets.graph.Network method) add_weights() (theanets.layers.base.Layer method) Autoencoder (class in theanets.feedforward) (class in theanets.recurrent)

B

batches() (in module theanets.recurrent) BernoulliDropout (class in theanets.regularizers) Bidirectional (class in theanets.layers.recurrent)

build() (in module theanets.activations) build_graph() (theanets.graph.Network method)

C

Classifier (class in theanets.feedforward) (class in theanets.layers.feedforward) (class in theanets.recurrent) classifier_batches() (theanets.recurrent.Text method) Clockwork (class in theanets.layers.recurrent) Concatenate (class in theanets.layers.base)

connect() (theanets.layers.base.Layer method) Contractive (class in theanets.regularizers) Conv1 (class in theanets.layers.convolution) CrossEntropy (class in theanets.losses)

D

decode() (theanets.feedforward.Autoencoder method) (theanets.recurrent.Text method)

DownhillTrainer (class in theanets.trainer)

E

encode() (theanets.feedforward.Autoencoder method) (theanets.recurrent.Text method)

F

feed_forward() (theanets.graph.Network method) Feedforward (class in theanets.layers.feedforward)

find() (theanets.graph.Network method) (theanets.layers.base.Layer method) Flatten (class in theanets.layers.base)

G

GaussianLogLikelihood (class in theanets.losses) GaussianNoise (class in theanets.regularizers)

GRU (class in theanets.layers.recurrent)

H

HiddenL1 (class in theanets.regularizers)

Hinge (class in theanets.losses)

I

Input (class in theanets.layers.base) input_size (theanets.layers.base.Layer attribute)

inputs (theanets.graph.Network attribute) itertrain() (theanets.graph.Network method) (theanets.trainer.DownhillTrainer method) (theanets.trainer.SampleTrainer method) (theanets.trainer.SupervisedPretrainer method) (theanets.trainer.UnsupervisedPretrainer method)

K

KullbackLeiblerDivergence (class in theanets.losses)

L

Layer (class in theanets.layers.base) LGrelu (class in theanets.activations) load() (theanets.graph.Network class method) log() (theanets.layers.base.Input method) (theanets.layers.base.Layer method) (theanets.layers.recurrent.Clockwork method) (theanets.losses.GaussianLogLikelihood method) (theanets.losses.Loss method) (theanets.regularizers.BernoulliDropout method) (theanets.regularizers.Contractive method) (theanets.regularizers.GaussianNoise method) (theanets.regularizers.Regularizer method)

Loss (class in theanets.losses) loss() (theanets.graph.Network method) (theanets.regularizers.Regularizer method) LSTM (class in theanets.layers.recurrent)

M

MaximumMeanDiscrepancy (class in theanets.losses) Maxout (class in theanets.activations) MeanAbsoluteError (class in theanets.losses) MeanSquaredError (class in theanets.losses)

modify_graph() (theanets.regularizers.Regularizer method) monitors() (theanets.feedforward.Classifier method) (theanets.graph.Network method) MRNN (class in theanets.layers.recurrent) MUT1 (class in theanets.layers.recurrent)

N

Network (class in theanets.graph)

num_params (theanets.graph.Network attribute) (theanets.layers.base.Layer attribute) (theanets.layers.recurrent.Bidirectional attribute)

O

output_name() (theanets.layers.base.Layer method)

P

params (theanets.graph.Network attribute) (theanets.layers.base.Layer attribute) (theanets.layers.recurrent.Bidirectional attribute) predict() (theanets.feedforward.Classifier method) (theanets.graph.Network method) predict_logit() (theanets.feedforward.Classifier method) predict_proba() (theanets.feedforward.Classifier method)

predict_sequence() (theanets.recurrent.Classifier method) Prelu (class in theanets.activations) Product (class in theanets.layers.base)

R

Regressor (class in theanets.feedforward) (class in theanets.recurrent) Regularizer (class in theanets.regularizers) reservoir() (theanets.trainer.SampleTrainer static method)

Reshape (class in theanets.layers.base) RNN (class in theanets.layers.recurrent) RRNN (class in theanets.layers.recurrent)

S

SampleTrainer (class in theanets.trainer) save() (theanets.graph.Network method) score() (theanets.feedforward.Autoencoder method) (theanets.feedforward.Classifier method) (theanets.graph.Network method) SCRN (class in theanets.layers.recurrent)

set_loss() (theanets.graph.Network method) setup() (theanets.layers.base.Layer method) (theanets.layers.convolution.Conv1 method) (theanets.layers.feedforward.Feedforward method) (theanets.layers.feedforward.Tied method) (theanets.layers.recurrent.LSTM method) (theanets.layers.recurrent.MRNN method) (theanets.layers.recurrent.RNN method) (theanets.layers.recurrent.RRNN method) SupervisedPretrainer (class in theanets.trainer)

T

Text (class in theanets.recurrent) theanets (module) theanets.layers.base (module) theanets.layers.convolution (module) theanets.layers.feedforward (module) theanets.layers.recurrent (module) theanets.losses (module)

theanets.recurrent (module) theanets.regularizers (module) Tied (class in theanets.layers.feedforward) to_spec() (theanets.layers.base.Input method) (theanets.layers.base.Layer method) (theanets.layers.base.Reshape method) (theanets.layers.feedforward.Tied method) (theanets.layers.recurrent.Bidirectional method) (theanets.layers.recurrent.Clockwork method) (theanets.layers.recurrent.MRNN method) train() (theanets.graph.Network method) transform() (theanets.layers.base.Concatenate method) (theanets.layers.base.Flatten method) (theanets.layers.base.Input method) (theanets.layers.base.Layer method) (theanets.layers.base.Product method) (theanets.layers.base.Reshape method) (theanets.layers.convolution.Conv1 method) (theanets.layers.feedforward.Feedforward method) (theanets.layers.feedforward.Tied method) (theanets.layers.recurrent.Bidirectional method) (theanets.layers.recurrent.Clockwork method) (theanets.layers.recurrent.GRU method) (theanets.layers.recurrent.LSTM method) (theanets.layers.recurrent.MRNN method) (theanets.layers.recurrent.MUT1 method) (theanets.layers.recurrent.RNN method) (theanets.layers.recurrent.RRNN method) (theanets.layers.recurrent.SCRN method)

U

UnsupervisedPretrainer (class in theanets.trainer)

updates() (theanets.graph.Network method)

V

variables (theanets.graph.Network attribute) (theanets.losses.Loss attribute)

W

WeightL1 (class in theanets.regularizers)

WeightL2 (class in theanets.regularizers)