In deep learning architecture, we will face the training speed and saturate accuracy problems when we increase the number of layers. Residual Neural Network architecture is introduced to allow us to increase the number of layers, yet improving accuracy and avoid the problem of vanishing gradients. ResNet node contains Residual Neural Network layers. Similar to TL Model, the dense layers can also be used after the ResNet layer to create a complete deep learning model.

The ResNet node architecture is designed to follow the ResNet paper. For more information, please visit Microsoft CNTK hand-on tutorial on the ResNet image classification.


Nodes: The number of the Residual Neural Network outputs.

  • Linear
  • ReLU
  • Sigmoid
  • Tanh
  • Softmax
  • SoftPlus
  • Softsign