Training and Validation Plots
Training Hyper Parameters
Training Hyper Parameters
We detail the hyper parameters of DNN models investigated in this paper as follows.
The Hyper Parameters for DNN Models on MNIST and CIFAR-10
The Hyper Parameters for DNN Models on MNIST and CIFAR-10
The Hyper Parameters for DNN Models on IMDb
The Hyper Parameters for DNN Models on IMDb
Note that the "LR" means learning rate, and "Tra.Para." indicates trainable parameters.
Training Performance Across Frameworks
Training Performance Across Frameworks
The following figures show the training and validation plots of four models on GPU with different frameworks. Note that we train and validate LeNet-1 and LeNet-5 on MNIST, ResNet-20 and VGG-16 on CIFAR-10, and TextCNN, TextRNN(LSTM) and TextRNN(GRU) on IMDb.
LeNet-1
LeNet-1
LeNet-5
LeNet-5
ResNet-20
ResNet-20
VGG-16
VGG-16
TextCNN
TextCNN
LSTM
LSTM
GRU
GRU
