TGAN
TGAN
Overview
Requirements
Python
Installation
Data Format
Input Format
Output Format
Demo Datasets
Census dataset
Cover type
Quickstart
1. Load the data
2. Create a TGAN instance
3. Fit the model
4. Sample new data
5. Save and Load a model
Loading custom datasets
Model Parameters
Model general behavior
Neural network definition and fitting
Command-line interface
Random hyperparameter search
Input
Execution
Output
Research
What’s next?
Citing TGAN
Resources
API Reference
Subpackages
tgan.research package
Submodules
Submodules
tgan.cli module
tgan.data module
tgan.model module
tgan.trainer module
Contributing
Types of Contributions
Report Bugs
Fix Bugs
Implement Features
Write Documentation
Submit Feedback
Get Started!
Pull Request Guidelines
Unit Testing Guidelines
Tips
Release Workflow
Credits
Contributors
History
0.1.0
TGAN
Docs
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Index
Edit on GitHub
Index
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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I
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L
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M
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N
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P
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R
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S
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T
B
batch_diversity() (tgan.model.GraphBuilder static method)
build_graph() (tgan.model.GraphBuilder method)
build_losses() (tgan.model.GraphBuilder method)
C
categorical_transformer (tgan.data.Preprocessor attribute)
check_inputs() (in module tgan.data)
check_metadata() (in module tgan.data)
collect_variables() (tgan.model.GraphBuilder method)
columns (tgan.data.Preprocessor attribute)
compute_kl() (tgan.model.GraphBuilder static method)
continous_columns (tgan.data.Preprocessor attribute)
continous_transformer (tgan.data.Preprocessor attribute)
D
data (tgan.data.TGANDataFlow attribute)
discriminator() (tgan.model.GraphBuilder method)
distribution (tgan.data.TGANDataFlow attribute)
E
evaluate_classification() (in module tgan.research.evaluation)
F
fit() (tgan.data.Preprocessor method)
(tgan.model.TGANModel method)
fit_score_model() (in module tgan.research.experiments)
fit_transform() (tgan.data.Preprocessor method)
G
GANTrainer (class in tgan.trainer)
generator() (tgan.model.GraphBuilder method)
get_data() (tgan.data.RandomZData method)
(tgan.data.TGANDataFlow method)
get_model() (tgan.model.TGANModel method)
get_optimizer (tgan.model.GraphBuilder attribute)
get_parser() (in module tgan.cli)
get_train_parser() (in module tgan.cli)
GraphBuilder (class in tgan.model)
I
inputs() (tgan.model.GraphBuilder method)
inverse_transform() (tgan.data.MultiModalNumberTransformer static method)
L
load() (tgan.model.TGANModel class method)
load_demo_data() (in module tgan.data)
M
main() (in module tgan.cli)
metadata (tgan.data.Preprocessor attribute)
(tgan.data.TGANDataFlow attribute)
MultiGPUGANTrainer (class in tgan.trainer)
MultiModalNumberTransformer (class in tgan.data)
N
num_features (tgan.data.TGANDataFlow attribute)
num_modes (tgan.data.MultiModalNumberTransformer attribute)
numpy_default() (in module tgan.research.experiments)
P
prepare_hyperparameter_search() (in module tgan.research.experiments)
prepare_sampling() (tgan.model.TGANModel method)
Preprocessor (class in tgan.data)
R
RandomZData (class in tgan.data)
reverse_transform() (tgan.data.Preprocessor method)
run_experiment() (in module tgan.research.experiments)
run_experiments() (in module tgan.research.experiments)
run_step() (tgan.trainer.SeparateGANTrainer method)
S
sample() (tgan.model.TGANModel method)
save() (tgan.model.TGANModel method)
SeparateGANTrainer (class in tgan.trainer)
shuffle (tgan.data.TGANDataFlow attribute)
size() (tgan.data.TGANDataFlow method)
T
tar_folder() (tgan.model.TGANModel method)
tgan (module)
tgan.cli (module)
tgan.data (module)
tgan.model (module)
tgan.research (module)
tgan.research.evaluation (module)
tgan.research.experiments (module)
tgan.trainer (module)
TGANDataFlow (class in tgan.data)
TGANModel (class in tgan.model)
transform() (tgan.data.MultiModalNumberTransformer method)
(tgan.data.Preprocessor method)