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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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先不管三七二十一,列出cifar10中定义模型和训练模型中的summary的代码: # Display the training images in the visualizer. tf.summary.image('images', images) def _activTensorboard教程:高维向量可视化 Tensorflow高维向量可视化 觉得有用的话,欢迎一起讨论相互学习~Follow Me 参考文献 强烈推荐Tensorflow实战Google...
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...
Datasets Edit CIFAR-10 Fashion-MNIST Results from the Paper Edit Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Methods Edit SPEED Contact us on: hello@paperswithcode.com . Papers With Code is a free resource...
CIFAR 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 raw pixels of the CIFAR-10 training set (50000 images x 1024 dimensions x 3 channels) in under 12 seconds. ...
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...
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...