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Instructions
You will be provided a Cifar-10 dataset. Your goal is to train a convolutional neural network for classification on the dataset, and obtain as high validation accuracy (~0.7) as you can;
In the first stage, you will choose the initial network configuration and then run the network;
In the second stage, there are multiple useful quantities you should monitor during training of a neural network. These plots are the window into the training process and should be utilized to get intuitions about different hyperparameter settings and how they should be changed for more efficient learning.
By clicking the ?⃝ you can get more information.
Dataset Statistics
- Classes: 10
- Input data size: (3, 32, 32)
- Examples per split: Train (8500), Val (1500)
Update Rule
- We use Adam to update our weights. ?⃝
Control Pad
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validation accuracy is 0.