Selected article for: "activation function and dense layer"
Author: Amine Amyar; Romain Modzelewski; Su Ruan
Title: Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation
  • Document date: 2020_4_21
    • ID: hiac6ur7_27
    Snippet: x 1024 to which we add a convolutional layer followed by a maxpooling, and then a flatten operation to convert the data to a mono-dimensional tensor to perform the classification. The multi-layer perceptron consist of a two Dense layer with 128 and 64 neurons respectively, with a dropout of 0.5 and the activation function elu. The last layer is a Dense layer with one neuron for image classification using a sigmoid activation and a binary cross en.....
    Document: x 1024 to which we add a convolutional layer followed by a maxpooling, and then a flatten operation to convert the data to a mono-dimensional tensor to perform the classification. The multi-layer perceptron consist of a two Dense layer with 128 and 64 neurons respectively, with a dropout of 0.5 and the activation function elu. The last layer is a Dense layer with one neuron for image classification using a sigmoid activation and a binary cross entropy as the loss function (loss3):

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