#NEWLINE# def __init__( self , rng , input , n_in , n_hidden , n_out ) : #NEWLINE# #INDENT# 'Initialize the parameters for the multilayer perceptron\n\n        :type rng: numpy.random.RandomState\n        :param rng: a random number generator used to initialize weights\n\n        :type input: theano.tensor.TensorType\n        :param input: symbolic variable that describes the input of the\n        architecture (one minibatch)\n\n        :type n_in: int\n        :param n_in: number of input units, the dimension of the space in\n        which the datapoints lie\n\n        :type n_hidden: int\n        :param n_hidden: number of hidden units\n\n        :type n_out: int\n        :param n_out: number of output units, the dimension of the space in\n        which the labels lie\n\n        ' #NEWLINE# self . hiddenLayer = HiddenLayer ( rng = rng , input = input , n_in = n_in , n_out = n_hidden , activation = T . tanh ) #NEWLINE# self . logRegressionLayer = LogisticRegression ( input = self . hiddenLayer . output , n_in = n_hidden , n_out = n_out ) #NEWLINE# self . negative_log_likelihood = self . logRegressionLayer . negative_log_likelihood #NEWLINE# self . errors = self . logRegressionLayer . errors #NEWLINE# self . params = ( self . hiddenLayer . params + self . logRegressionLayer . params )