Fig. 2: 1D-Convolutional neural network.
From: Autonomous scanning probe microscopy investigations over WS2 and Au{111}
a Schematic depicting the 1D-CNN model used for training where each layer makes use of a rectified linear unit activation function and max pooling. The first convolutional layer consists of 64 nodes and the second layer makes use of 128 nodes, which is then passed through a dropout layer, flattened, and fed into a fully connected layer. b Individual spectra can be subsequently passed through a softmax layer using the trained model to yield class probabilities, where example spectra are shown for Aufcc, Auhcp, WS2, and VS. Each spectra depicted exhibits the greatest predictive probability of belonging to the expected class and near zero probabilities for the remaining 3 classes (depicted in gray scale) with the trained model after 6 epochs.
