Figure 2
From: Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Convergence of the network training. F1 score, as a measure of the classification performance, is shown for individual classes (a–c) and their averaged (combined) forms (d–f) over training epochs. At each epoch, the network is trained with all examples in the training dataset, and its performance over these training examples is averaged to obtain the training F1 score of the epoch (orange curves). At the end of each training epoch, the network is used for classifying all examples in the validation dataset resulting in each epoch’s validation F1 score (green curves). This neural network succeeded to recognize (a) SW-480 cells and (b) OT-II cells even at the end of the first train epoch, but required additional runs to detect (c) regions of the waveform containing no cells (blank examples). The shaded area demonstrates the range of performance variations in each epoch for five different training runs. The validation performance approximates the training performance, indicating the model is well-regularized.