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Added documentation for the convolution operation

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Benoit Steiner 2015-03-31 08:27:23 -07:00
parent 73cdeae1d3
commit f873686602
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unsupported/Eigen/CXX11/src/Tensor/README.md
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@ -1157,7 +1157,41 @@ in TensorFunctors.h for information on how to implement a reduction operator.
## Convolutions
TBD: convolve(const KernelDerived& kernel, const Dimensions& dims)
### <Operation> convolve(const Kernel& kernel, const Dimensions& dims)
Returns a tensor that is the output of the convolution of the input tensor with the kernel,
along the specified dimensions of the input tensor. The dimension size for dimensions of the output tensor
which were part of the convolution will be reduced by the formula:
output_dim_size = input_dim_size - kernel_dim_size + 1 (requires: input_dim_size >= kernel_dim_size).
The dimension sizes for dimensions that were not part of the convolution will remain the same.
Performance of the convolution can depend on the length of the stride(s) of the input tensor dimension(s) along which the
convolution is computed (the first dimension has the shortest stride for ColMajor, whereas RowMajor's shortest stride is
for the last dimension).
// Compute convolution along the second and third dimension.
Tensor<float, 4, DataLayout> input(3, 3, 7, 11);
Tensor<float, 2, DataLayout> kernel(2, 2);
Tensor<float, 4, DataLayout> output(3, 2, 6, 11);
input.setRandom();
kernel.setRandom();
Eigen::array<ptrdiff_t, 2> dims({1, 2}); // Specify second and third dimension for convolution.
output = input.convolve(kernel, dims);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 6; ++k) {
for (int l = 0; l < 11; ++l) {
const float result = output(i,j,k,l);
const float expected = input(i,j+0,k+0,l) * kernel(0,0) +
input(i,j+1,k+0,l) * kernel(1,0) +
input(i,j+0,k+1,l) * kernel(0,1) +
input(i,j+1,k+1,l) * kernel(1,1);
VERIFY_IS_APPROX(result, expected);
}
}
}
}
## Geometrical Operations