`sklearn.neural_network`.BernoulliRBM¶

class sklearn.neural_network.BernoulliRBM(n_components=256, *, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)[source]¶

Bernoulli Restricted Boltzmann Machine (RBM).

A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2].

The time complexity of this implementation is O(d ** 2) assuming d ~ n_features ~ n_components.

Read more in the User Guide.

Parameters

n_componentsint, default=256

Number of binary hidden units.

learning_ratefloat, default=0.1

The learning rate for weight updates. It is highly recommended to tune this hyper-parameter. Reasonable values are in the 10**[0., -3.] range.

batch_sizeint, default=10

Number of examples per minibatch.

n_iterint, default=10

Number of iterations/sweeps over the training dataset to perform during training.

verboseint, default=0

The verbosity level. The default, zero, means silent mode.

random_stateint, RandomState instance or None, default=None

Determines random number generation for:

Gibbs sampling from visible and hidden layers.
Initializing components, sampling from layers during fit.
Corrupting the data when scoring samples.

Pass an int for reproducible results across multiple function calls. See Glossary.

Attributes

intercept_hidden_array-like of shape (n_components,): Biases of the hidden units.
intercept_visible_array-like of shape (n_features,): Biases of the visible units.
components_array-like of shape (n_components, n_features): Weight matrix, where n_features in the number of visible units and n_components is the number of hidden units.
h_samples_array-like of shape (batch_size, n_components): Hidden Activation sampled from the model distribution, where batch_size in the number of examples per minibatch and n_components is the number of hidden units.

References

[1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for: deep belief nets. Neural Computation 18, pp 1527-1554. https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf
[2] Tieleman, T. Training Restricted Boltzmann Machines using: Approximations to the Likelihood Gradient. International Conference on Machine Learning (ICML) 2008

Examples

>>> import numpy as np
>>> from sklearn.neural_network import BernoulliRBM
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = BernoulliRBM(n_components=2)
>>> model.fit(X)
BernoulliRBM(n_components=2)

Methods

`fit`(X[, y])	Fit the model to the data X.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`gibbs`(v)	Perform one Gibbs sampling step.
`partial_fit`(X[, y])	Fit the model to the data X which should contain a partial segment of the data.
`score_samples`(X)	Compute the pseudo-likelihood of X.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Compute the hidden layer activation probabilities, P(h=1\|v=X).

fit(X, y=None)[source]¶

Fit the model to the data X.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): Training data.

Returns

selfBernoulliRBM: The fitted model.

fit_transform(X, y=None, **fit_params)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

gibbs(v)[source]¶

Perform one Gibbs sampling step.

Parameters

vndarray of shape (n_samples, n_features): Values of the visible layer to start from.

Returns

v_newndarray of shape (n_samples, n_features): Values of the visible layer after one Gibbs step.

partial_fit(X, y=None)[source]¶

Fit the model to the data X which should contain a partial segment of the data.

Parameters

Xndarray of shape (n_samples, n_features): Training data.

Returns

selfBernoulliRBM: The fitted model.

score_samples(X)[source]¶

Compute the pseudo-likelihood of X.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): Values of the visible layer. Must be all-boolean (not checked).

Returns

pseudo_likelihoodndarray of shape (n_samples,): Value of the pseudo-likelihood (proxy for likelihood).

Notes

This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

transform(X)[source]¶

Compute the hidden layer activation probabilities, P(h=1|v=X).

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): The data to be transformed.

Returns

hndarray of shape (n_samples, n_components): Latent representations of the data.

Examples using `sklearn.neural_network.BernoulliRBM`¶

sklearn.neural_network.BernoulliRBM¶

Examples using sklearn.neural_network.BernoulliRBM¶

`sklearn.neural_network`.BernoulliRBM¶

Examples using `sklearn.neural_network.BernoulliRBM`¶