def __init__(self, rng, input, n_in, n_hidden, n_out):

        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function
        self.hiddenLayer = HiddenLayer(rng=rng, input=input,
                                       n_in=n_in, n_out=n_hidden,
                                       activation=T.tanh)

        # The logistic regression layer gets as input the hidden units
        # of the hidden layer
        self.logRegressionLayer = LogisticRegression(
            input=self.hiddenLayer.output,
            n_in=n_hidden,
            n_out=n_out)

        # L1 norm ; one regularization option is to enforce L1 norm to
        # be small

        # negative log likelihood of the MLP is given by the negative
        # log likelihood of the output of the model, computed in the
        # logistic regression layer
        self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood
        # same holds for the function computing the number of errors
        self.errors = self.logRegressionLayer.errors

        # the parameters of the model are the parameters of the two layer it is
        # made out of
        self.params = self.hiddenLayer.params + self.logRegressionLayer.params
Initialize the parameters for the multilayer perceptron
dataset/ETHPy150Open flipkart-incubator/optimus/cnn_text_trainer/core/nn_classes.py/MLP.__init__