def _setup_params(self):
        self.srng = RandomStreams(seed=234)
        self.large_cov = np.array([[0.06,0],[0,0.06]], dtype=FLOATX)
        self.small_cov = np.array([[self.gaussian_std,0],[0,self.gaussian_std]], dtype=FLOATX)
        self.cov = theano.shared(np.array(self.small_cov, dtype=FLOATX))
        self.cov_inv_var = theano.shared(np.array(LA.inv(self.small_cov), dtype=FLOATX))
        self.cov_det_var = theano.shared(np.array(LA.det(self.small_cov), dtype=FLOATX))
        self._sample_gaussian = SampleMultivariateGaussian()

        self.W_g0 = self.create_weight(7*7, 128, suffix="g0")
        self.W_g1 = self.create_weight(2, 128, suffix="g1")
        self.W_g2_hg = self.create_weight(128, 256, suffix="g2_hg")
        self.W_g2_hl = self.create_weight(128, 256, suffix="g2_hl")

        self.W_h_g = self.create_weight(256, 256, suffix="h_g")
        self.W_h = self.create_weight(256, 256, suffix="h")
        self.B_h = self.create_bias(256, suffix="h")
        self.h0 = self.create_vector(256, "h0")
        self.l0 = self.create_vector(2, "l0")
        self.l0.set_value(np.array([-1, -1], dtype=FLOATX))

        self.W_l = self.create_weight(256, 2, suffix="l")
        self.W_l.set_value(10 / self.W_l.get_value())
        self.B_l = self.create_bias(2, suffix="l")
        self.W_a = self.create_weight(256, 10, suffix="a")
        self.B_a = self.create_bias(10, suffix="a")


        self.W = [self.W_g0, self.W_g1, self.W_g2_hg, self.W_g2_hl, self.W_h_g, self.W_h, self.W_a]
        self.B = [self.B_h, self.B_a]
        self.parameters = [self.W_l]
dataset/ETHPy150Open zomux/deepy/experiments/attention_models/baseline_model.py/AttentionLayer._setup_params/swapped operands of `/`