I’m implementing a neural network in C++ using Eigen for linear algebra operations. In the DenseLayer::forward
function, I’m encountering a dimension mismatch issue when trying to add a bias vector to the layer’s output matrix.
Here’s the structure of my code:
Expected Behavior:
After multiplying input * weights.transpose()
, I get an output matrix of size 64x128. I need to add bias to each row of output so that the final result remains 64x128.
Current Approach:
Here's the forward function:
Eigen::MatrixXd DenseLayer::forward(const Eigen::MatrixXd& input) {
input_cache = input;
Eigen::MatrixXd output = input * weights.transpose();
// Attempt to replicate bias across rows to match output dimensions
Eigen::MatrixXd bias_replicated = bias.replicate(output.rows(), 1);
output += bias_replicated;
return output;
}
Error Message:
When I run the code, I encounter the following assertion failure:
Assertion failed: (dst.rows() == src.rows() && dst.cols() == src.cols()), function resize_if_allowed, file AssignEvaluator.h, line 754.
What I've Tried:
.colwise() + bias.col(0)
, .rowwise() + bias.transpose()
, and directly adding bias with different reshaping/transposing.Question: How can I correctly broadcast bias across each row of output to perform element-wise addition without Eigen raising a dimension mismatch error? Is there a better way to handle this broadcasting in Eigen, or am I missing something fundamental in matrix addition with Eigen?
Debug Output: To help, here are the dimensions printed before the error:
Any insights on achieving the correct broadcasting or a workaround for this would be greatly appreciated!
Here is how I fixed it:
Initial fix which is not efficient and based on Homer512's feedback, I revised my initial approach
Recap on the problem:
The assertion error arose because Eigen's .rowwise()
approach requires bias to be treated as a vector for broadcasting along rows. However, Eigen's internal checks often flag mismatches if the vector doesn't conform exactly as expected, especially with transposed vectors in operations like .rowwise()
addition.
Solution: Expanding bias into a Full Matrix Before Adding
To work around this limitation, I manually replicated the bias to match the output dimensions before adding it, This ensures that each row of the output matrix receives a copy of the bias vector, making Eigen's type system happy and avoiding the assertion errors.
I Updated forward Function Implementation:
Eigen::MatrixXd DenseLayer::forward(const Eigen::MatrixXd& input) {
std::cout << "[DenseLayer Forward] Input size: " << input.rows() << "x" << input.cols() << std::endl;
std::cout << "[DenseLayer Forward] Weights size: " << weights.rows() << "x" << weights.cols() << std::endl;
std::cout << "[DenseLayer Forward] Bias size: " << bias.rows() << "x" << bias.cols() << std::endl;
input_cache = input;
Eigen::MatrixXd output = input * weights.transpose();
// Create a matrix of the same size as output, where each row is a copy of the bias vector
Eigen::MatrixXd bias_expanded = bias.transpose().replicate(output.rows(), 1);
std::cout << "[DenseLayer Forward] Output before bias addition size: " << output.rows() << "x" << output.cols() << std::endl;
std::cout << "[DenseLayer Forward] Expanded bias size: " << bias_expanded.rows() << "x" << bias_expanded.cols() << std::endl;
output += bias_expanded;
std::cout << "[DenseLayer Forward] Output after bias addition size: " << output.rows() << "x" << output.cols() << std::endl;
return output;
}
Explanation:
bias.transpose().replicate(output.rows(), 1);
creates a bias_expanded matrix where each row is a copy of bias. This ensures that bias_expanded has the exact dimensions as output, so if output is 64x128, bias_expanded will also be 64x128..rowwise() + bias.transpose()
:.rowwise()
entirely. This approach circumvents Eigen's broadcasting limitations and type-checking issues, making the code more robust.Better Approach: Most recent Fix
Thanks to Homer512's suggestion, I revised my approach for efficiency. Initially, I had bias set up as a matrix, which required full replication to match output dimensions. Now, bias is an Eigen::VectorXd
, which broadcasts naturally:
Eigen::MatrixXd DenseLayer::forward(const Eigen::MatrixXd& input) {
input_cache = input;
Eigen::MatrixXd output = input * weights.transpose();
output.rowwise() += bias.transpose(); // Efficient, no full replication needed
return output;
}
This approach removes the overhead, thanks again!