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Home Page: http://chainer.org
Various CNN models for CIFAR10 with Chainer
Home Page: http://chainer.org
There is a typo in line 83 where the label array is cast into int32
type then stored in training_labell
. Fortunately, the typo doesn't affect the outcome of the script though.
Another thing: I suggest using numpy.ndarray.astype
method for casting types instead of redefining the array, as the former is a little more optimized than the latter.
In both train() and validate(), the loss and accuracy on a minibatch are multiplied by args.batchsize instead of the true size of the minibatch. When the batchsize does not evenly divide the size of the dataset, the last batch is smaller than batchsize. To fix this, replace the "* args.batchsize" by "*y_batch.size".
In the original paper, the model has 2n+1 layers of 16 filters, 2n layers of 32 filters and 2n layers of 64 filters.
However, your model seems to have 1 layers of 16 filters, 2n layers of 32 filters and 4n layers of 64 filters.
Perhaps do I misunderstand?
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