Theanets 0.8.0pre documentation
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Index
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A
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B
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C
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D
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E
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F
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G
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H
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I
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K
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L
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M
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N
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O
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P
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R
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S
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V
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W
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__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.convolution.Conv2 method)
(theanets.layers.convolution.Pool1 method)
(theanets.layers.convolution.Pool2 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.RecurrentNorm method)
(theanets.regularizers.RecurrentState 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)
bind() (theanets.layers.base.Layer method)
(theanets.layers.recurrent.Bidirectional method)
(theanets.layers.recurrent.Clockwork method)
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)
Conv2 (class in theanets.layers.convolution)
CrossEntropy (class in theanets.losses)
D
decode() (theanets.feedforward.Autoencoder method)
(theanets.recurrent.Text method)
DEFAULT_OUTPUT_ACTIVATION (theanets.feedforward.Classifier attribute)
(theanets.graph.Network attribute)
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)
full_name() (theanets.layers.base.Layer method)
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_name (theanets.layers.base.Layer attribute)
INPUT_NDIM (theanets.graph.Network attribute)
(theanets.recurrent.Autoencoder attribute)
(theanets.recurrent.Classifier attribute)
(theanets.recurrent.Regressor attribute)
input_shape (theanets.layers.base.Layer attribute)
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.feedforward.Tied 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)
log_params() (theanets.layers.base.Layer method)
Loss (class in theanets.losses)
loss() (theanets.graph.Network method)
(theanets.regularizers.Contractive method)
(theanets.regularizers.HiddenL1 method)
(theanets.regularizers.RecurrentNorm method)
(theanets.regularizers.RecurrentState method)
(theanets.regularizers.Regularizer method)
(theanets.regularizers.WeightL1 method)
(theanets.regularizers.WeightL2 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.BernoulliDropout method)
(theanets.regularizers.GaussianNoise method)
(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)
O
output_name (theanets.layers.base.Layer attribute)
OUTPUT_NDIM (theanets.feedforward.Classifier attribute)
(theanets.graph.Network attribute)
(theanets.recurrent.Autoencoder attribute)
(theanets.recurrent.Classifier attribute)
(theanets.recurrent.Regressor attribute)
output_shape (theanets.layers.base.Layer attribute)
output_size (theanets.layers.base.Layer attribute)
P
params (theanets.graph.Network attribute)
(theanets.layers.base.Layer attribute)
Pool1 (class in theanets.layers.convolution)
Pool2 (class in theanets.layers.convolution)
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
RecurrentNorm (class in theanets.regularizers)
RecurrentState (class in theanets.regularizers)
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)
resolve_inputs() (theanets.layers.base.Input method)
(theanets.layers.base.Layer method)
(theanets.layers.feedforward.Tied method)
(theanets.layers.recurrent.LSTM method)
(theanets.layers.recurrent.SCRN method)
resolve_outputs() (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.convolution.Conv2 method)
(theanets.layers.feedforward.Tied method)
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.convolution.Conv2 method)
(theanets.layers.feedforward.Feedforward method)
(theanets.layers.feedforward.Tied 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)
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.Layer method)
(theanets.layers.feedforward.Tied method)
(theanets.layers.recurrent.Bidirectional method)
(theanets.layers.recurrent.Clockwork method)
(theanets.layers.recurrent.LSTM method)
(theanets.layers.recurrent.MRNN method)
(theanets.layers.recurrent.SCRN 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.convolution.Conv2 method)
(theanets.layers.convolution.Pool1 method)
(theanets.layers.convolution.Pool2 method)
(theanets.layers.feedforward.Feedforward method)
(theanets.layers.feedforward.Tied method)
(theanets.layers.recurrent.Bidirectional 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)
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