class_docstr: 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.
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docstr: 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 datapoints lie
  
  :type n_out: int
  :param n_out: number of output units, the dimension of the space in
                which the labels lie
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code: def __init__(self, input, n_in, n_out, name_prefix=""):
      """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 datapoints lie
  
      :type n_out: int
      :param n_out: number of output units, the dimension of the space in
                    which the labels lie
  
      """
  
      # initialize with 0 the weights W as a matrix of shape (n_in, n_out)
      self.W = theano.shared(
          value=np.zeros((n_in, n_out), dtype=theano.config.floatX),
          name=name_prefix + "W",
      )
  
      # compute vector of class-membership probabilities in symbolic form
      self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W))
  
      # compute prediction as class whose probability is maximal in
      # symbolic form
      self.y_pred = T.argmax(self.p_y_given_x, axis=1)
  
      # parameters of the model
      self.params = [self.W]
  
file_path: Theano/theano/tensor/tests/mlp_test.py
imports: from __future__ import absolute_import, print_function, division
  from collections import OrderedDict
  import numpy
  import theano
  import theano.tensor
label_desc: Use image node_id 1 to create a new LogisticRegression object from inherited base classes: object with example: obj = LogisticRegression(input, n_in, n_out, name_prefix)