Implementation of tanh() activation function for a CNN - c++

Question

I'm trying to implement the activation function tanh on my CNN, but it doesn't work, the result is always "NaN". So i created a simple application where i have a randomized matrix and try to apply the tanh(x) function thus to understand where's the problem?
Here's my implementation :
Eigen::MatrixXd A = Eigen::MatrixXd::Random(10,1000);
Eigen::MatrixXd result, deriv;
result = A.array().tanh();
deriv = 1.0 - result*result;
and the only result to this is this error :
no match for ‘operator-’ (operand types are ‘double’ and ‘const Eigen::Product<Eigen::Matrix<double, -1, -1>, Eigen::Matrix<double, -1, -1>, 0>’)
deriv = (1.0 - result*result );
~~~~^~~~~~~~~~~~~~~
Could you please help me ?

Answer 1

The product result*result does not have the right dimensions for a matrix multiplication. We can use result*result.transpose() instead (unless a coefficient-wise multiplication is intended, in which case one could use result.array()*result.array()).
To subtract the values of the resulting matrix from a matrix full of ones, the .array() method can be used:
deriv = 1. - (result*result.transpose()).array();

Answer 2

I used openCV to create a matrix of ones
like this :
cv::Mat sum;
Eigen::MatrixXd SUM, Acv;
cv::eigen2cv(A,Acv)
sum=Mat::ones(Acv.rows,Acv.cols, CV_32FC1);
cv::cv2eigen(sum,SUM);
so :
deriv = SUM - result*result;
and now, here's another problem :(
/usr/include/eigen3/Eigen/src/Core/CwiseBinaryOp.h :110 : Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::CwiseBinaryOp(const Lhs&, const Rhs&, const BinaryOp&) [with BinaryOp = Eigen::internal::scalar_difference_op<double, double>; LhsType = const Eigen::Matrix<double, -1, -1>; RhsType = const Eigen::Product<Eigen::Matrix<double, -1, -1>, Eigen::Matrix<double, -1, -1>, 0>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Lhs = Eigen::Matrix<double, -1, -1>; Eigen::CwiseBinaryOp<BinaryOp, Lhs, Rhs>::Rhs = Eigen::Product<Eigen::Matrix<double, -1, -1>, Eigen::Matrix<double, -1, -1>, 0>]: l'assertion « aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols() » a échoué.