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CIFAR-10 CNN implementation in Apple mlx
After adding mlx.nn.BatchNorm, evaluation results differ in train.py vs. test.py.
Model seems to be correctly set to eval()
and train()
Epoch 27: Loss 1.07467 | Train accuracy 0.785 | Test accuracy 0.542 | Throughput 688.19 images/second | Time 75.054 (s)
Epoch 28: Loss 1.05003 | Train accuracy 0.790 | Test accuracy 0.541 | Throughput 688.58 images/second | Time 74.930 (s)
Epoch 29: Loss 1.03433 | Train accuracy 0.799 | **Test accuracy 0.543** | Throughput 706.21 images/second | Time 73.218 (s)
$ python test.py --model models/model_CIFAR-100_029.npz --dataset=CIFAR-100
Loaded model for CIFAR-100 from models/model_CIFAR-100_029.npz
Starting testing ...
....
Test accuracy: 0.5409345030784607
The PR implementing batch norm in MLX (ml-explore/mlx#217) has been merged. Batch normalization can be added to the model in this repo, to better align with https://github.com/menzHSE/torch-cifar-10-cnn.
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