image_recognition.MNIST.LeNet

Posted by yhijioka

Copy the project to Neural Network Console Cloud

dataset-require=MNIST

Dataset : Training

  • Number of data : 60,000
  • Variable : x (image)
  • Type : Image
  • Shape : 1, 28, 28
  • Variable : y (label)
  • Type : Scalar

Examples of variable x in “Training”

Dataset : Validation

  • Number of data : 10,000
  • Variable : x (image)
  • Type : Image
  • Shape : 1, 28, 28
  • Variable : y (label)
  • Type : Scalar

Examples of variable x in “Validation”

Network Architecture : Main

Type Value
Output 21,919
CostParameter 78,810
CostAdd 11,304
CostMultiply 0
CostMultiplyAdd 802,876
CostDivision 490
CostExp 490
CostIf 17,558

Training Procedure : Optimizer

Optimize network “Main” using “Training” dataset.

  • Batch size : 64
  • Solver : Adam
    • Learning rate(Alpha) : 0.001
    • Beta1 : 0.9
    • Beta2 : 0.999
    • Epsilon : 1e-08
  • Weight decay is not applied.

Experimental Result : Learning Curve

Experimental Result : Evaluation

Evaluate network “MainRuntime” using “Validation” dataset.

Variable : y

  • Accuracy : 0.9895
  • Avg.Precision : 0.9895772046719382
  • Avg.Recall : 0.9894022381602995
  • Avg.F-Measures : 0.9894707194531065

References

  • Sony Corporation. Neural Network Console : Not just train and evaluate. You can design neural networks with fast and intuitive GUI. https://dl.sony.com/
  • Sony Corporation. Neural Network Libraries : An open source software to make research, development and implementation of neural network more efficient. https://nnabla.org/
  • Convolution – Chen et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. https://arxiv.org/abs/1606.00915, Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions. https://arxiv.org/abs/1511.07122
  • ReLU – Vinod Nair, Geoffrey E. Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.165.6419&rep=rep1&type=pdf
  • Adam – Kingma and Ba, Adam: A Method for Stochastic Optimization. https://arxiv.org/abs/1412.6980