tutorial.basics.01_logistic_regression
Posted by Yosuke Fukui
Copy the project to Neural Network Console Cloud
dataset-require=MNIST
Dataset : Training
- Number of data : 1,500
- Variable : x (image)
- Type : Image
- Shape : 1, 28, 28
- Variable : y (9)
- Type : Scalar
Examples of variable x in “Training”
Dataset : Validation
- Number of data : 500
- Variable : x (image)
- Type : Image
- Shape : 1, 28, 28
- Variable : y (9)
- Type : Scalar
Examples of variable x in “Validation”
Network Architecture : Main
| Type | Value |
|---|---|
| Output | 787 |
| CostParameter | 785 |
| CostAdd | 1 |
| CostMultiply | 1 |
| CostMultiplyAdd | 784 |
| CostDivision | 0 |
| CostExp | 0 |
| CostIf | 1 |
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.
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/
- LeakyReLU – Andrew L. Maas, Awni Y. Hannun, Andrew Y. Ng. Rectifier Nonlinearities Improve Neural Network Acoustic Models. https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf
- Adam – Kingma and Ba, Adam: A Method for Stochastic Optimization. https://arxiv.org/abs/1412.6980