PyTorch implementation of CNNs for CIFAR benchmark
License: MIT License
๐ Hi, I'm Wei Li (ๆ้ก), also known as Will Lee.
๐ Please check out my homepage: bigballon.github.io
๐ซ Feel free to contact me via fm.bigballon AT gmail.com
Hello,
Should "log_dir" change to "logdir" at here: https://github.com/BIGBALLON/CIFAR-ZOO/blob/master/train.py#L133
def main():
global args, config, last_epoch, best_prec, writer
writer = SummaryWriter(log_dir=args.work_path + "/event")
# read config from yaml file
with open(args.work_path + "/config.yaml") as f:
config = yaml.load(f)
line 168 in train.py, load() missing 1 required positional argument: 'Loader'
line 180 of senet.py:
x = F.avg_pool2d(x, 8, 1)
will be better fitting all image sizes if changed to
x = F.avg_pool2d(x, x.size(3), 1)
Thanks for your repo!
A detail about LeNet-5:
Layer C5 in LeNet should be a convolutional layer as LeCun decribed in his paper:
Layer C5 is a convolutional layer with 120 feature maps.
Each unit is connected to a 5x5 neighborhood on all 16
of S4's feature maps. Here, because the size of S4 is also
5x5, the size of C5's feature maps is 1x1: this amounts
to a full connection between S4 and C5. C5 is labeled
as a convolutional layer, instead of a fully-connected layer
because if LeNet-5 input were made bigger with everything
else kept constant, the feature map dimension would be
larger than 1x1
It is nothing serious~
I wonder how the results are reported in your benchmark?
I have had two trials of lenet with the same config, but got best_test_acc of 67.980% and 67.210%, respectively.
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