def __init__(self, rng, input, n_in, n_out, W=None, b=None,
                 activation=T.tanh, pool_size = 3, rectify = False):
        self.input = input
        self.n_in = n_in
        self.n_out = n_out
        
        if W is None:
            W_values = numpy.asarray(rng.uniform(
                    low=-numpy.sqrt(6. / (n_in + n_out)),
                    high=numpy.sqrt(6. / (n_in + n_out)),
                    size=(n_in, n_out)), dtype=theano.config.floatX)
            if activation == theano.tensor.nnet.sigmoid:
                W_values *= 4

            W = theano.shared(value=W_values, name='W', borrow=True)

        if b is None:
            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
            b = theano.shared(value=b_values, name='b', borrow=True)

        self.W = W
        self.b = b

        self.delta_W = theano.shared(value = numpy.zeros((n_in,n_out),
                                     dtype=theano.config.floatX), name='delta_W')

        self.delta_b = theano.shared(value = numpy.zeros_like(self.b.get_value(borrow=True),
                                     dtype=theano.config.floatX), name='delta_b')

        lin_output = T.dot(input, self.W) + self.b
        self.output = (lin_output if activation is None
                       else activation(lin_output))
        # parameters of the model
        self.params = [self.W, self.b]
        self.delta_params = [self.delta_W, self.delta_b]
Class for hidden layer
dataset/ETHPy150Open yajiemiao/pdnn/layers/mlp_maxout.py/HiddenLayer.__init__