可以看到数据得到了良好的分类,与模型准确率(0.98)相互印证 再看在CIFAR10上的效果图之一(模型准确率=0.3): 在模型准确率0.3左右,可以看到数据分类效果不好 当模型准确率=0.79时,我们再看看CIFAR10上的效果图: 当模型准确率提到0.79,可以看到数据分类效果好了很多 2.1. 首先我们按照常规创建编译神经网络模型,并用...
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Object classification with CIFAR-10 using transfer learning visualization classifier images keras cnn classification image-classification convolutional-networks convolutional-neural-networks transfer-learning tsne-algorithm tsne keras-models keras-classification-models keras-neural-networks cnn-keras cnn-model keras...
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The performance of t-SNE-CUDA compared to other state-of-the-art implementations on the CIFAR-10 dataset. t-SNE-CUDA runs on the output of a classifier on the CIFAR-10 training set (50000 images x 1024 dimensions) in under 6 seconds. While we can run on the full pixel set in under...
This t-SNE implementation code uses TSNE from scikit-learn and it might take a little bit long time to generate the image. This code can be easily applied to other datasets such as CIFAR10, CIFAR100, etc. Usage python3 main.py STL10 example This example image is generated by ImageNet...
python cne_scripts_notebooks/scripts/cifar10_acc.py -m 16 -r 0 The results will be printed in terminal but can also be checked out in notebooks/eval_cifar.ipynb.For other experiments adapt the parameters at the top of compute_embds_cne.py and compute_embds_umap.py or at the top of ...
Object classification with CIFAR-10 using transfer learning visualization classifier images keras cnn classification image-classification convolutional-networks convolutional-neural-networks transfer-learning tsne-algorithm tsne keras-models keras-classification-models keras-neural-networks cnn-keras cnn-model keras...
andrew-draganov/gidr-dunofficial 13 Dimensionality Reduction Datasets Edit CIFAR-10Fashion-MNIST Submitresults from this paperto get state-of-the-art GitHub badges and help the community compare results to other papers. Methods Edit AddRemove ...
The performance of t-SNE-CUDA compared to other state-of-the-art implementations on the CIFAR-10 dataset. t-SNE-CUDA runs on the output of a classifier on the CIFAR-10 training set (50000 images x 1024 dimensions) in under 6 seconds. While we can run on the full pixel set in under...