def _init_coef(self, fan_in, fan_out, rng):
        if self.activation == 'logistic':
            # Use the initialization method recommended by
            # Glorot et al.
            init_bound = np.sqrt(2. / (fan_in + fan_out))
        elif self.activation == 'tanh':
            init_bound = np.sqrt(6. / (fan_in + fan_out))
        elif self.activation == 'relu':
            init_bound = np.sqrt(6. / (fan_in + fan_out))
        else:
            # this was caught earlier, just to make sure
            raise ValueError("Unknown activation function %s" %
                             self.activation)

        coef_init = rng.uniform(-init_bound, init_bound, (fan_in, fan_out))
        intercept_init = rng.uniform(-init_bound, init_bound, fan_out)
        return coef_init, intercept_init
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/multilayer_perceptron.py/BaseMultilayerPerceptron._init_coef/original