Module dimdrop.util.rbm

Classes

class EarlyStoppingRBM (n_components=256, batch_size=100, lr=0.01, patience=3, epochs=1000, verbose=0)

Adaptation on the BernoulliRBM class of sklearn to add the ability to stop early when training does not improve.

Parameters

n_components : int, optional

The size of the output, default 256

batch_size : int, optional

The batch size of the rbm, default 100

lr : float, optional

The learning rate of the rbm, default 0.01

patience : int, optional

The amount of epochs without improvement before training stops, default 3

epochs : int, optional

The maximum amount of epochs, default 1000

verbose : int, optional

The verbosity of the rbm, default 0

Attributes

rbm : BernoulliRBM

the rbm to be trained

Source code

class EarlyStoppingRBM:
    """
    Adaptation on the `BernoulliRBM` class of sklearn to add the ability to
    stop early when training does not improve.

    Parameters
    ----------
    n_components : int, optional
        The size of the output, default `256`
    batch_size : int, optional
        The batch size of the rbm, default `100`
    lr : float, optional
        The learning rate of the rbm, default `0.01`
    patience : int, optional
        The amount of epochs without improvement before training stops,
        default `3`
    epochs : int, optional
        The maximum amount of epochs, default `1000`
    verbose : int, optional
        The verbosity of the rbm, default `0`

    Attributes
    ----------
    rbm : BernoulliRBM
        the rbm to be trained
    """

    def __init__(
        self,
        n_components=256,
        batch_size=100,
        lr=0.01,
        patience=3,
        epochs=1000,
        verbose=0
    ):
        self.rbm = BernoulliRBM(
            n_components=n_components,
            n_iter=1,
            batch_size=batch_size,
            learning_rate=lr,
            verbose=verbose
        )
        self.patience = patience
        self.epochs = epochs
        self.verbose = verbose

    def fit(self, data):
        """
        Fit the rbm to the given data

        Parameters
        ----------
        data : array
            Data to be fitted
        """
        self.rbm.fit(data)
        min_likelyhood = np.mean(
            [np.mean(self.rbm.score_samples(data)) for _ in range(5)])
        last_likelyhood = min_likelyhood
        min_index = 0
        for i in range(1, self.epochs):
            if min_index + self.patience > i:
                if self.verbose:
                    print('Epoch {}/{}'.format(i + 1, self.epochs))
                self.rbm.fit(data)
                last_likelyhood = np.mean(
                    [np.mean(self.rbm.score_samples(data)) for _ in range(5)])
                if last_likelyhood < min_likelyhood:
                    min_likelyhood = last_likelyhood
                    min_index = i
            else:
                break

Methods

def fit(self, data)

Fit the rbm to the given data

Parameters

data : array

Data to be fitted

Source code

def fit(self, data):
    """
    Fit the rbm to the given data

    Parameters
    ----------
    data : array
        Data to be fitted
    """
    self.rbm.fit(data)
    min_likelyhood = np.mean(
        [np.mean(self.rbm.score_samples(data)) for _ in range(5)])
    last_likelyhood = min_likelyhood
    min_index = 0
    for i in range(1, self.epochs):
        if min_index + self.patience > i:
            if self.verbose:
                print('Epoch {}/{}'.format(i + 1, self.epochs))
            self.rbm.fit(data)
            last_likelyhood = np.mean(
                [np.mean(self.rbm.score_samples(data)) for _ in range(5)])
            if last_likelyhood < min_likelyhood:
                min_likelyhood = last_likelyhood
                min_index = i
        else:
            break