コード例 #1
0
ファイル: rbm.py プロジェクト: stachon/binet
    def partial_fit(self, X):
        """
        Fit the model to the data X which should contain a partial
        segment of the data.

        Adjust the parameters to maximize the likelihood of v using
        Stochastic Maximum Likelihood (SML).

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The data to use for training.
        """
        v_pos = X
        h_pos = self._mean_hiddens(v_pos)

        if self.use_pcd:
            v_neg = self._sample_visibles(self.h_samples_)
        else:
            v_neg = self._sample_visibles(h_pos)
        h_neg = self._mean_hiddens(v_neg)

        lr = float(self.learning_rate) / v_pos.shape[0]
        op.add_dot(h_pos, v_pos, self.dW, True, False, alpha=1.0, beta=self.momentum)
        op.add_dot(h_neg, v_neg, self.dW, True, False, alpha=-1.0, beta=1.0)
        self.W += lr * self.dW

        self.dbh *= self.momentum
        self.dbv *= self.momentum
        self.dbh += (op.sum(h_pos, axis=0) - op.sum(h_neg, axis=0)).reshape(1, self.dbh.shape[1])
        self.dbv += (op.sum(v_pos, axis=0) - op.sum(v_neg, axis=0)).reshape(1, self.dbv.shape[1])
        self.bh += lr * self.dbh
        self.bv += lr * self.dbv
        if self.use_pcd:
            self.h_samples_ = op.sample_binomial(h_neg)
コード例 #2
0
ファイル: rbm.py プロジェクト: tvandera/binet
    def _sample_hiddens(self, v):
        """Sample from the distribution P(h|v).

        Parameters
        ----------
        v : array-like, shape (n_samples, n_features)
            Values of the visible layer to sample from.

        Returns
        -------
        h : array-like, shape (n_samples, n_components)
            Values of the hidden layer.
        """
        p = self._mean_hiddens(v)
        return op.sample_binomial(p)
コード例 #3
0
ファイル: rbm.py プロジェクト: stachon/binet
    def _sample_hiddens(self, v):
        """Sample from the distribution P(h|v).

        Parameters
        ----------
        v : array-like, shape (n_samples, n_features)
            Values of the visible layer to sample from.

        Returns
        -------
        h : array-like, shape (n_samples, n_components)
            Values of the hidden layer.
        """
        p = self._mean_hiddens(v)
        return op.sample_binomial(p)
コード例 #4
0
ファイル: rbm.py プロジェクト: tvandera/binet
    def partial_fit(self, X):
        """
        Fit the model to the data X which should contain a partial
        segment of the data.

        Adjust the parameters to maximize the likelihood of v using
        Stochastic Maximum Likelihood (SML).

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The data to use for training.
        """
        v_pos = X
        h_pos = self._mean_hiddens(v_pos)

        if self.use_pcd:
            v_neg = self._sample_visibles(self.h_samples_)
        else:
            v_neg = self._sample_visibles(h_pos)
        h_neg = self._mean_hiddens(v_neg)

        lr = float(self.learning_rate) / v_pos.shape[0]
        op.add_dot(h_pos,
                   v_pos,
                   self.dW,
                   True,
                   False,
                   alpha=1.0,
                   beta=self.momentum)
        op.add_dot(h_neg, v_neg, self.dW, True, False, alpha=-1.0, beta=1.0)
        self.W += lr * self.dW

        self.dbh *= self.momentum
        self.dbv *= self.momentum
        self.dbh += (op.sum(h_pos, axis=0) - op.sum(h_neg, axis=0)).reshape(
            1, self.dbh.shape[1])
        self.dbv += (op.sum(v_pos, axis=0) - op.sum(v_neg, axis=0)).reshape(
            1, self.dbv.shape[1])
        self.bh += lr * self.dbh
        self.bv += lr * self.dbv
        if self.use_pcd:
            self.h_samples_ = op.sample_binomial(h_neg)