  We present a Convolutional Neural Network (CNN) architecture for inverse
Raman amplifier design. This model aims at finding the pump powers and
wavelengths required for a target signal power evolution, both in distance
along the fiber and in frequency. Using the proposed framework, the prediction
of the pump configuration required to achieve a target power profile is
demonstrated numerically with high accuracy in C-band considering both
counter-propagating and bidirectional pumping schemes. For a distributed Raman
amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and
0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum
test error are obtained numerically employing 2 and 3 counter, and 4
bidirectional propagating pumps, respectively.
