Class LogisticRegression

code/logistic_cg.py:52–142

Multi-class Logistic Regression Class The logistic regression is fully described by a weight matrix :math:`W` and bias vector :math:`b`. Classification is done by projecting data points onto a set of hyperplanes, the distance to which is used to determine a class membership probabil

class LogisticRegression(object):
    """Multi-class Logistic Regression Class

    The logistic regression is fully described by a weight matrix :math:`W`
    and bias vector :math:`b`. Classification is done by projecting data
    points onto a set of hyperplanes, the distance to which is used to
    determine a class membership probability.
    """

    def __init__(self, input, n_in, n_out):
        """ Initialize the parameters of the logistic regression

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
                      architecture ( one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
                     which the datapoint lies

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
                      which the target lies

        """

        # initialize theta = (W,b) with 0s; W gets the shape (n_in, n_out),
        # while b is a vector of n_out elements, making theta a vector of
        # n_in*n_out + n_out elements
        self.theta = theano.shared(
            value=numpy.zeros(
                n_in * n_out + n_out,
                dtype=theano.config.floatX
            ),
            name='theta',
            borrow=True
        )
        # W is represented by the fisr n_in*n_out elements of theta
        self.W = self.theta[0:n_in * n_out].reshape((n_in, n_out))
        # b is the rest (last n_out elements)
        self.b = self.theta[n_in * n_out:n_in * n_out + n_out]

        # compute vector of class-membership probabilities in symbolic form
        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)

        # compute prediction as class whose probability is maximal in
        # symbolic form
        self.y_pred = T.argmax(self.p_y_given_x, axis=1)

        # keep track of model input
        self.input = input

    def negative_log_likelihood(self, y):
        """Return the negative log-likelihood of the prediction of this model
        under a given target distribution.

        .. math::