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Imaging of a subwavelength microstructure made of a periodic grid-like finite set of circular rods is carried out from transient scattered field data in different configurations of sources and receivers. The goal is to identify the position of possibly missing rods. Time reversal is confirmed as a cheap yet efficient first-order diagnostic method even in the demanding context of a subwavelength microstructure. Tools of deep learning, expected to be valid in general circumstances if data acquired in sufficient numbers, are in parallel developed to image the microstructure. To that effect, recurrent neural networks (RNN) and convolutional neural networks (CNN) are both used. Pros and cons of all approaches are illustrated by comprehensive simulations from synthetic data computed via a finite difference time domain method (FD-TD) software carefully tailored to the microstructure model and used also to make the networks learn the microstructures. The analysis is completed from examples of laboratory-controlled data acquired on a microstructure prototype set within a microwave anechoic chamber. These examples confirm the good promises of neural networks even with rather scarce data as exemplified in a forward scattering case—fixed source and receiver antennas face each other and the microstructure is rotated between them. [ABSTRACT FROM AUTHOR]