Using diffirent activation function on NeuroSim

[bakigkgz1]

/* Truncation with a custom threshold */
double truncate(double x, int numLevel, double threshold) {
if (numLevel <= 0) { // No truncation if numLevel <= 0
return x;
} else {
int sign = 1;
if (x<0)
sign = -1; // For truncation on negative number

	double val = x * numLevel * sign;
	int r_val = (int)(val);
	if (val - r_val >= threshold)
		val = r_val + 1;
	else
		val = r_val;
	return val*sign/numLevel;
}

}
formula.cpp
/* Activation function */
double tanh_fx(double x) {
return (exp(x) - exp(-x)) / (exp(x) + exp(-x));
}

train.cpp

        // Backpropagation
		/* Second layer (hidden layer to the output layer) */
		for (int j = 0; j < param->nOutput; j++){              
			s2[j] = (1 - a2[j]*a2[j])*(Output[i][j] - a2[j]);
		}

		/* First layer (input layer to the hidden layer) */
		std::fill_n(s1, param->nHide, 0);
		#pragma omp parallel for
		for (int j = 0; j < param->nHide; j++) {
			for (int k = 0; k < param->nOutput; k++) {					
				s1[j] += (1 - a1[j]*a1[j]) * weight2[k][j] * s2[k];
			}
		}

Hello, I made the above changes to use the tanh activation function instead of the sigmoid activation function in the application, but the accuracy rate is very low, around ten percent. What could be the reason for this? I would appreciate your help, thank you, and have a good day