Frequency-resolved optical gating (FROG) is a technique for measuring the intensity and phase of ultrashort laser pulses. In FROG, a spectrogram of the pulse is produced from which the intensity and phase of the pulse's electric field is then retrieved using an iterative algorithm. This iterative algorithm performs well for all types of pulses, but it sometimes requires more than a minute to converge, and faster retrieval is desired for many applications. As a faster alternative, we therefore employed a neural network to invert the function that relates the pulse intensity and phase to its FROG trace. In previous work, we showed that a neural network can retrieve simple pulses, described by four or six parameters, rapidly and directly. In this contribution, we discuss our latest attempts to train an artificial neural network for more complex pulse shapes.