Evan Shelhamer 2cd155f545 Merge pull request #522 from sguada/accuracy_without_loss ... Split accuracy and loss		2014-06-26 10:25:12 -07:00
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cifar10	Now AccuracyLayer only computes accuracy, one should use LossLayers to compute loss	2014-06-20 19:56:18 -07:00
feature_extraction	Now AccuracyLayer only computes accuracy, one should use LossLayers to compute loss	2014-06-20 19:56:18 -07:00
imagenet	Now AccuracyLayer only computes accuracy, one should use LossLayers to compute loss	2014-06-20 19:56:18 -07:00
images	add fish bike example image	2014-06-08 20:44:51 -07:00
mnist	Now AccuracyLayer only computes accuracy, one should use LossLayers to compute loss	2014-06-20 19:56:18 -07:00
pascal-finetuning	Now AccuracyLayer only computes accuracy, one should use LossLayers to compute loss	2014-06-20 19:56:18 -07:00
detection.ipynb	finish R-CNN detection example	2014-06-10 10:15:32 -07:00
filter_visualization.ipynb	There are 256 filters in conv2.	2014-06-21 15:22:05 -07:00
imagenet_classification.ipynb	update notebook examples with new wrapper usage, re-organize	2014-05-20 12:16:09 -07:00
net_surgery.ipynb	make notebook for net surgery of fully-convolutional model	2014-06-12 14:41:25 -07:00