Figure 5
From: Remote photonic detection of human senses using secondary speckle patterns
DNN classifier architecture. A visualization of the model in operation on a speckle input signal is shown. With state-to-state kernels of size 3 × 3, the ConvLSTM single layer network contains 64 hidden states and input-to-state for 64 input frames. Since the required model's prediction is a binary classification task, we concatenated all forecasting network's states and fed them into a 256-unit dense with ReLU activation, which speeds up our model's training phase by reducing the gradient of the computational process. To produce the final prediction, we added a dense layer with two units on top of the model.
