pmlearn.neural_network package

Subpackages

• pmlearn.neural_network.tests package
  • Submodules
  • pmlearn.neural_network.tests.test_mutlilayer_perceptron module
  • Module contents

Submodules

pmlearn.neural_network.multilayer_perceptron module

Multilayer perceptron

class pmlearn.neural_network.multilayer_perceptron.MLPClassifier(n_hidden=5)

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianClassifierMixin

Multilayer perceptron classification built using PyMC3.

Fit a Multilayer perceptron classification model and estimate model parameters using MCMC algorithms or Variational Inference algorithms

Examples

http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/

create_model()

load(file_prefix)

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:

• file_prefix (str, path and prefix used to identify where to load the) –
• trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
• load_custom_params (Boolean flag to indicate whether custom parameters) –
• be loaded. Defaults to False. (should) –

Returns:

custom_params

Return type:

Dictionary of custom parameters

save(file_prefix)

Saves the trace and custom params to files with the given file_prefix.

Parameters:

• file_prefix (str, path and prefix used to identify where to save the) –
• for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
• custom_params (Dictionary of custom parameters to save.) – Defaults to None

Module contents

The pmlearn.neural_network module includes models based on neural networks.

class pmlearn.neural_network.MLPClassifier(n_hidden=5)

Bases: pmlearn.base.BayesianModel, pmlearn.base.BayesianClassifierMixin

Multilayer perceptron classification built using PyMC3.

Fit a Multilayer perceptron classification model and estimate model parameters using MCMC algorithms or Variational Inference algorithms

Examples

http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/

create_model()

load(file_prefix)

Loads a saved version of the trace, and custom param files with the given file_prefix.

Parameters:

• file_prefix (str, path and prefix used to identify where to load the) –
• trace for this model. (saved) – Ex: given file_prefix = “path/to/file/” This will attempt to load “path/to/file/trace.pickle”
• load_custom_params (Boolean flag to indicate whether custom parameters) –
• be loaded. Defaults to False. (should) –

Returns:

custom_params

Return type:

Dictionary of custom parameters

save(file_prefix)

Saves the trace and custom params to files with the given file_prefix.

Parameters:

• file_prefix (str, path and prefix used to identify where to save the) –
• for this model. (trace) – Ex: given file_prefix = “path/to/file/” This will attempt to save to “path/to/file/trace.pickle”
• custom_params (Dictionary of custom parameters to save.) – Defaults to None